Enterprise software, built with AI. Under your control.
Vernemo.AI is an AI software house and platform that orchestrates models from OpenAI, Anthropic, Google, Mistral and Bielik across the entire delivery lifecycle: from requirements through code, testing and security to legacy modernization. Your team approves every step, and your data can stay inside your own infrastructure.
- REQRequirementsNatural language, DOCX, PDF, an existing system
- SPECSpecificationAcceptance criteria, NFRs, API contracts
- TESTTestingScenarios and automation from the approved spec
- CODECodeFrontend and backend to your standards
- SECSecurityReview, SAST, pentest, GDPR guards
- SHIPDeploymentInto your CI/CD, with an audit report
- RUNOperationsUp-to-date documentation, decision trail
Pilot results in regulated industries
We measure the impact on your own project before you sign a licence. Below are results from two on-premise deployments: an insurtech company (Java system) and a pharmaceutical company (Delphi/VB6 system).
Results of specific engagements compared with the pre-deployment baseline. Details under Case studies.
Two ways to work with us, one architecture
You can commission a project from us or give the platform to your own teams. Most clients start with the first and end up with the second.
AI software house
A small team of senior engineers working with AI agents delivers what traditionally took a dozen people and many months: legacy modernization, new systems, testing and documentation.
- Fixed scope, measurable outcome, an audit report after every stage
- Module-by-module migration without freezing development
- You own the intellectual property of every artifact
The Vernemo.AI platform
Agent modules for analysts, testers, developers and security teams, plugged into your repositories, Jira and CI/CD pipelines. Each works on its own or as part of the full chain.
- SaaS, dedicated instance or fully on-premise
- No lock-in to a single model vendor, token costs per team
- Rollout in 7 working days at a fixed price
What everyone at the buying table gets
Board, CIO, CFO
Lower CAPEX and OPEX for software delivery, predictable AI spend with the Token Dashboard, and an end to technical debt and the risk of losing expert knowledge.
CISO, DPO, compliance
On-premise deployment, a full audit trail, sensitive-data guards, and no model training on your data without consent. Mapped to the AI Act, GDPR, DORA and NIS2.
Architects and developers
Code generated from a verified specification, to your standards. Integration with Git, IDEs and CI/CD from day one. Developers approve every step.
QA, testers, PMs
Test cases, E2E scenarios and Playwright or Selenide automation straight from documentation. Testers verify instead of writing from scratch.
How we get started
A path designed so your procurement and security teams can verify every promise before a budget decision.
Demo within 48 hours
Point us to one IT project. Within 48 hours we estimate the real savings and show the platform on your case.
2–6 week pilot
A controlled on-premise environment. At the end, a report: test cases, code, documentation and measured savings.
Rollout in 7 days
Configuration, a knowledge base built from your documents and security policies, tool integration and training.
Licence and support
Buy modules individually, not as a bundle. Standard or priority SLA and a dedicated customer success manager.
Industries where regulation is business as usual
Finance, insurance, telecoms, e-commerce, pharma, energy and the public sector. Wherever documentation, audits and data residency decide whether a project can start at all.
Security and complianceDo the models learn from our code and data?
Not without your explicit consent. In an on-premise deployment, artifacts, prompts and responses stay in your infrastructure, and open-weight models (such as Bielik) can run on your own hardware.
Who owns the generated code and documentation?
You do. The intellectual property of all artifacts belongs to the client.
Are we locked into a single AI vendor?
No. The platform routes each task to the best model for quality and cost, and output from one vendor is checked by a model from another. You can exclude any vendor by policy.
What does the AI cost and how do we control it?
Every task reports its cost. The Token Dashboard shows usage per user, department, project and model in real time, and token costs are passed through at the vendor's price with a transparent service margin.
Do you work with system integrators and consultancies?
Yes. The platform integrates with your existing process and tools, so it can be part of a programme run by your integrator.
AI software house: startup speed, enterprise rigour
We pair a small team of experienced engineers with AI agents that handle the repetitive work. We deliver in weeks what used to be estimated in quarters, with the documentation and decision trail an auditor expects.
Legacy modernization
PalimpsestCoderWe rewrite applications from Delphi, Object Pascal, Visual Basic/VB6, COBOL, Clipper, FoxPro, dBase, Perl, PHP, ActionScript or Objective-C into Java, .NET 8, Python, React.js and Node.js. We first extract the business logic and domain documentation, then generate code with regression tests that compare the output of the old and new systems on the same data.
Strangler-fig strategy: old and new run side by side, traffic is switched module by module, and you can roll back at any stage.
Monolith decomposition
MonolitsDetails: legacy refactoring and breaking up monoliths
We start by analysing the data layer, data flows and the logic held in stored procedures, and only then carve out bounded contexts one by one as microservices, with no full rewrite and no code freeze. Twelve phases, three decision gates, one-click rollback and deterministic verification of builds and contracts. After every iteration the board gets numbers: how much code has left the monolith and how far cross-dependencies have dropped.
Database migrations
Database ConverterFrom Oracle, MSSQL, DB2, MySQL, DBF, Paradox and FoxPro to PostgreSQL. Type mapping with a rationale for every decision, an ETL script and a parity validator that compares row counts and checksums per table. A migration succeeds when the numbers match, not when the script finishes without errors.
AS-IS analysis and knowledge recovery
DocumentalistWe document the system as it really is: an inventory of database objects and data lineage, application architecture and entry points, code cross-referenced with the metadata catalogue, a static vulnerability review, and a risk and recommendations report for management. Knowledge locked in experts' heads gets written down before they leave.
New systems from scratch
AI AnalystCode GeneratorFrom concept, analysis and specification through frontend and backend code and automated tests to deployment and maintenance. The specification is the source of truth, and code is generated from documentation approved by your people.
Evolving existing software
New features, modules and integrations, with the documentation the system usually lacks brought up to date along the way. We work in your repositories, trackers and coding standards.
Quality assurance as a service
ScenaristBuggerThe full quality process: scenarios from requirements, E2E automation, autonomous exploratory testing of new builds with deterministic reproductions, and code quality reports with recommended changes. Integrations with Jira, Xray, TestRail, Zephyr, Selenium, Cypress and Playwright.
Application security testing
PentestAn automated web application scan before every release: XSS, SQL injection, headers, CORS, HSTS, TLS, SPF/DMARC. Severity calibrated to the environment, a reproduction for every vulnerability and an endpoint for blocking deployments in CI/CD.
AI SDLC consulting
AI strategy for software delivery, tool selection and rollout, corporate AI usage policies, AI Act compliance, prompt engineering, a shadow AI reduction programme and an internal knowledge base with a library of patterns. Often the first step before the platform.
Why we are faster without losing control
Agents instead of hours
A specification in an hour and a half instead of a week of workshops. Test scenarios in minutes instead of half a day. A review of 10,000 lines in an hour instead of a senior engineer's week.
A human at every gate
No artifact moves on without approval from your analyst, tester or developer. It is a quality mechanism and the human oversight required by Article 14 of the AI Act.
Cross-vendor verification
Output from one vendor's model is checked by another vendor's model. No vendor has an interest in pointing out the mistakes of its own model family.
Nothing is silently fixed
When code is translated, unsafe constructs are carried over and flagged for an architect's decision rather than masked. You see what you have first.
Cost visible before the invoice
Every task reports its cost, so you know what a given specification, migration or code review actually cost.
Support on your terms
A standard SLA included in the licence or a priority SLA with response times measured in hours, a dedicated account manager and team training.
Engagement models
| Model | For whom | What it includes |
|---|---|---|
| Fixed-scope project | Modernization, migration, decomposition, AS-IS analysis | A paid analysis, staged delivery with section-by-section approval gates, optional final testing and production rollout |
| Custom development | New features and systems | Billed per person-day, a team supported by AI agents, user support packages |
| Annual enterprise licence | Large organizations | A bundle of modules, SLA, a dedicated customer success manager, Token Dashboard |
| On-premise | Enterprises, public sector, regulated industries | The platform in your infrastructure; the knowledge base, artifact history and audit trail never leave your network |
| SaaS pay-per-use | Teams and business units getting started | You pay for actual use of modules and tokens |
| AI SDLC consulting | Organizations before a platform decision | Strategy, policies, AI Act, shadow AI programme, training |
Got a system nobody wants to touch any more?
Point us to it. Within 48 hours we will tell you what modernizing it with AI would cost and which module to start with.
Breaking up a monolith starts with analysis
The boundaries of future services are written in the data: in shared tables, in the flows between them and in logic hidden in stored procedures. Before we cut anything out, we document the actual state of the database, the application code and how one uses the other.
Why analysis decides success
A full rewrite of a monolith usually means years of work and a multi-million budget, so the sensible route is to carve the system out piece by piece. But in a mature system the hardest part is not in the application code. It is in the database that modules, reports, batch jobs and integrations have relied on for years.
If you do not know where the data in a column comes from, who changes it and which stored procedure calculates the result, cutting out any module is a bet on production stability. That is why we never squeeze the analysis into a single day. Its duration depends on the size of the system and the completeness of the materials, and is agreed before work begins.
Logic you cannot see in the application code
In systems built over decades, a large share of the business rules lives in the database:
- in stored procedures that calculate, validate and move data
- in views that join tables from several domain areas
- in relationships and keys that bind modules together at the data level
- in objects used by reports, database-side jobs and external systems
We document these places before anyone decides where that logic should live after the split.
Three layers of analysis
We analyse two views of the system independently and then cross-reference them. Only that comparison shows where the dependencies really run.
Data layer
Based on metadata from the metadata catalogue or from a read-only database connection. We do not analyse table contents.
- Inventory of schemas, tables, views and stored procedures
- Table-by-table data model: columns, types, keys, constraints
- View definitions in SQL
- Description of what each stored procedure does, reconstructed from its definition
- Data lineage at table and column level
- Risks prioritised high, medium and low
Application code
A static review of one agreed code revision. The code is read, not executed.
- Technologies, how the system runs, architecture and modules
- Entry points: interfaces, scheduled jobs, commands
- Data access layer: embedded SQL, procedure calls, ORM, connections
- Integrations with external systems
- A touchpoint table: source file to database object
Code cross-referenced with the database
A comparison of the objects that exist in the database with the objects the code refers to.
- Coverage matrix: object, reference in code, file
- Database objects with no references in the code
- Orphaned objects: used in the code, missing from the catalogue
- Mismatches in names, parameters and procedure signatures
- Prioritised risks and recommendations
No reference in the code does not mean an object is redundant. A report, a database-side job or another system may use it. Every decision to retire an object must be confirmed on the client side.
Seven analysis deliverables
Each document has defined contents, a basis and scope boundaries agreed before work starts. We deliver them in Word for sign-off and in Markdown for your documentation repository.
| Deliverable | Question it answers | Basis |
|---|---|---|
| Data layer documentation | What the database is made of, what the procedures and views do, where data comes from and where it goes | Metadata |
| Application code analysis | What the application is made of and how it reaches the data | Source code |
| Developer documentation | How to maintain and take over the application: modules, components, flows, startup, dead code, technical debt | Code and code analysis |
| Code-to-data-layer comparison | Where the code and the database agree, and where there are discrepancies and orphaned objects | Code analysis and metadata |
| AS-IS master document | The state of the system, written for management | Synthesis of the above |
| Vulnerability analysis | Where the code has patterns that lead to vulnerabilities: SQL injection, hard-coded credentials, missing validation, risky configuration, outdated dependencies | Code, configuration, dependencies |
| Recommendations | What needs attention first and why, with an item register and priorities | All deliverables |
How we work
Read-only
A technical account with no write permissions. Credentials are not kept after the work is done.
Metadata, not data
We analyse the structure of the database, not the contents of records. Any samples are taken without personal data.
No guesswork
When the materials do not contain an answer, the document says "no data" instead of a guessed value.
One point in time
One code revision and one metadata snapshot, so every finding can be reproduced.
Confidentiality
All documents are classified as confidential and handed over only to named people.
What we need to get started
- Metadata access: a metadata catalogue with a read-only account, or the database in read-only mode
- Source code: an archive or a read-only repository, with the scope indicated
- Scope and people: the databases and schemas to analyse, a data architect, a DBA, a team lead
- Optional: column statistics, query and object usage history, data quality test results
We hand over the full checklist at the start of the engagement.
From analysis to the first extracted service
The analysis describes the current state. Based on it, in a separate stage, we plan and carry out the decomposition.
- ACCESS
Access and materials handed over
Following the checklist. Done on the client side.
- READ
Metadata read and code loaded
Registered in the Vernemo analysis environment, with completeness of materials confirmed.
- AS-IS
Analysis and seven documents
Data layer, code, comparison, vulnerabilities, master document and recommendations.
- REVIEW
Review with your team
A round of comments and formal handover of the documentation. This is where the decision is made on whether to start the decomposition and where.
- PLAN
Service boundaries and extraction order
Using lineage, the coverage matrix and the logic in procedures, we define bounded contexts, rank them by isolation, size and complexity, and decide where the logic stored in the database should run.
- SPLIT
Decomposition with approval gates
The Monolits module carves out one context at a time using the strangler-fig approach: plan, diff and deterministic verification of builds and contracts, each approved by an architect and reversible with a rollback. Development on the monolith carries on.
- DATA
Migrating data and database logic
Splitting shared tables, moving logic from procedures into services where it makes sense, and, when leaving Oracle, migrating to PostgreSQL with data parity validation.
Refactoring legacy applications is more than monoliths
An outdated language or platform
PalimpsestClipper, FoxPro, Delphi, VB6, COBOL or old Java rewritten into Python, TypeScript, .NET 8 or Java/Spring, with regression tests comparing the results of the old and new systems.
Code nobody wants to touch
CoderA repository review with priorities and ready-made fixes, plus translation of modules between languages. Unsafe constructs are flagged for an architect's decision rather than masked.
Leaving a commercial database
Database ConverterOracle, MSSQL, DB2 or DBF to PostgreSQL: type mapping with a rationale, ETL and a parity validator with per-table checksums.
Before you carve out the first service, get to know your system
We start with an AS-IS analysis of the data layer and the code. We will send you the checklist and agree scope and timing.
AI Cerber. Every AI system in your company, under control.
AI Cerber discovers AI models and agents across your organization, assesses their risk under the EU AI Act and enforces policy on every call. In the cloud, hybrid, or entirely in your own environment.
Built-in control mappings: EU AI Act, ISO/IEC 42001, NIST AI RMF, GDPR
AI is growing faster than the oversight around it
Teams launch assistants, agents and model integrations faster than anyone can list them. Risk appears where nobody is looking.
Shadow AI
API keys in repositories, apps with access to mailboxes, agents inside office tools. Without IT or security knowing.
Blurred accountability
Nobody knows who owns an agent, what data it works with or which systems it can write to.
Regulation without evidence
The EU AI Act requires risk assessment, human oversight and event logging. A spreadsheet cannot prove any of it.
From inventory to proof of compliance
Four steps that run in a loop: whatever is discovered gets assessed, put under policy and documented.
Discover
Connectors to clouds, SaaS platforms, your identity provider, code repositories and proxy logs build a registry of every AI asset, including the unknown ones.
Assess
A request with EU AI Act classification sets the risk category, required controls and approval path.
Control
The gateway enforces policy on every model and tool call: allowed models, personal data masking, budgets, kill switch.
Prove
Continuous control monitoring collects evidence from telemetry and produces auditor-ready reports without stitching spreadsheets together.
See the product
Application screens with sample data. Pick a view or open the full clickable preview.
Status of every AI system, gateway coverage and what needs action today.
All system names, figures, amounts and dates shown on the screens are sample data.
Everything AI oversight needs, in one place
Registry and discovery
AI systems, agents, models, tools and data in one dependency graph, with owners and full change history.
Shadow AI under control
Every discovered, unregistered asset lands in a queue with a suggested owner and a match hint.
EU AI Act classification
A questionnaire sets the risk category and required controls, and the approval path starts automatically.
Policy-enforcing gateway
Allowed models, regions, personal data masking, budgets and limits enforced on every call.
Continuous compliance
Controls checked daily, evidence collected automatically, findings with an owner and a due date.
Cost and value
The cost of every AI system, plus gateway coverage that shows how much traffic bypasses control.
Policy works at the moment of the call, not just on paper
The registry and the gateway share one policy library. If a system is not approved, uses a model that is not allowed or has exceeded its budget, the call is stopped and the event is written to the audit log.
- APP
Agent or application
Connects with a virtual key assigned to a registered system.
- GATE
Gateway
Agent identity, policy decision, data masking, budget, kill switch.
- LLM
Model or MCP tool
Any provider: cloud, direct API or a locally hosted model.
Your data stays where it belongs
One product, three deployment models. AWS, Google Cloud, Azure or your own data center.
SaaS
Live within hours, updates handled by us, data region of your choice.
For companies that want to start right away
Hybrid Recommended
Management console in the cloud; gateway, telemetry and discovery in your network. Prompts and model responses never leave your environment.
For regulated industries
Self-hosted
Everything on your Kubernetes, including fully air-gapped. Signed images and an SBOM with every release.
For the most demanding environments
One source of truth for everyone accountable for AI
CISO and security
Sees shadow AI, forces traffic through the gateway and can pause a system with one click.
Compliance and risk
Gets classification, controls and auditor-ready reports without collecting evidence by hand.
CIO and AI leaders
Knows the AI system portfolio, its costs and its business value.
Teams building AI
Register a system in minutes and get a gateway key without waiting weeks.
Start without an integration project. The product works without a CMDB: feed the registry with a CSV import and automatic discovery, and add CMDB integration later.
Frequently asked questions
AI Cerber is in an early access programme. We confirm the feature scope and rollout timeline individually as part of a pilot.
AI Cerber oversees all AI across the organization. The Vernemo.AI platform has its own oversight layer for AI in software delivery; the two products can work together.
Do we need a CMDB to get started?
No. The registry has its own model of applications and services. You feed it with a CSV import and automatic discovery; CMDB integration can be added later.
Does prompt content reach your cloud?
Not in the hybrid or self-hosted model. The gateway and telemetry run in your network, and only metadata such as call counts and costs reaches the console.
Which model providers does it support?
The major providers on AWS, Google Cloud and Azure, direct model provider APIs, and locally hosted models.
How long does deployment take?
As a guide: SaaS within hours, hybrid in 1–2 days and self-hosted in 1–2 weeks, depending on your infrastructure.
Does it replace our GRC system?
It does not have to. It manages the AI domain and can pass findings and evidence to your existing GRC or ticketing system.
See all your AI in one place
We will show the product on your use case and work out which deployment model fits your organization.
Agent modules across the entire delivery lifecycle
One module per lifecycle function. Each produces artifacts you can inspect, version and approve, not opaque suggestions. All of them share a common oversight and compliance layer.
Turns a raw business document into a technical specification: requirements with Given-When-Then acceptance criteria, measurable non-functional parameters (p95, RTO, RPO), integration contracts and a glossary. It asks clarifying questions before it starts writing, and a reviewer automatically triggers a revision round.
Reads requirements in DOCX, PDF, XLSX or Markdown and returns test cases covering positive, negative, edge and security paths, plus optional automation code in Playwright, Selenide or Pytest built on stable locators.
A whole-repository review with findings ranked by severity and ready-to-apply fixes as diffs, in security, performance or readability mode. Translates modules between languages while preserving semantics. Organizational standards, banned libraries and conventions are enforced with your own rules.
Builds a dependency graph, identifies and ranks bounded contexts, then carves the first one out as a microservice with a shim left in the monolith. Three human decision gates, verification with no language model involved, one-click rollback.
Breaks a legacy system down into a business logic map and a migration plan, and once approved generates the target code (Python/FastAPI, TypeScript/Next.js, .NET 8, Java/Spring), a PostgreSQL schema, a data migration script and regression tests comparing both systems.
From a source schema it produces the target schema, a list of type substitutions with rationale, normalization proposals, a batched ETL script and a parity validator with SHA-256 checksums per table.
An autonomous exploratory tester: it logs in, fills in forms and hunts for anomalies in a new build on its own. Every bug comes with a recorded click path, a screenshot and a network trace, and a replay engine reproduces it without a model. A built-in judge checks whether the agent really did what it reports.
A crawler maps the application and scanners test every endpoint it finds: XSS, SQLi, headers, authentication, CORS, HSTS, TLS, SPF and DMARC. A report with a reproduction for every vulnerability and an export for the security team.
A cross-cutting layer: every response from every model passes through three locally running guards. Bielik Guard detects sensitive data (Polish PESEL and NIP numbers, account numbers, health data), HerBERT Guard detects toxicity and bias, and PolyGuard detects prompt injection and jailbreak attempts. The decision is pass, redact or block, always with an audit log entry.
Five architecture principles
Documentation before code
Tests, code and deployment configuration are generated from documentation verified by a human. The specification is the source of truth, not a by-product.
Agent modules, not chat
Agent runs are instrumented and measurable: token counts, before-and-after reports, pipeline effectiveness.
A human approves every step
No artifact moves on without approval from your analyst, tester or developer.
Everything versioned and audited
Version history and a trail of who, what, when and why. The first thing a GxP or DORA auditor asks for.
Integration, not replacement
The platform writes to your repositories, trackers and CI/CD pipelines. It does not require a new toolset or a reorganization of IT.
Multi-model orchestration
Every task goes to the model that offers the best quality for the cost: OpenAI, Anthropic, Google, Mistral, Qwen or Bielik. Frontier models are reserved for tasks that need them. Open-weight models can run on your hardware when data sovereignty requires it.
Output from one vendor is challenged by a model from another. It is an independent check, and the direction regulation is heading in as well.
Integrations
Git, GitHub, GitLab, Azure DevOps, Jira, Confluence, GitHub Actions, Jenkins, Selenium, Cypress, Playwright, TestRail, Xray, Zephyr, Bugzilla, Redmine. Frontend in React.js and Node.js, backend in Java, .NET and Python.
The list grows with every deployment. Ask about your tool.
See a module working on your own document
Bring one requirements document or part of a repository. We will show you the output live.
You cannot govern AI you cannot see
The Vernemo.AI oversight layer gives the board, security and compliance one place to see every model and agent working on your software, every decision, every policy violation and every euro spent on tokens.
Five oversight functions
Mature enterprise AI governance programmes rest on the same cycle: discover, govern, secure, observe, measure. In Vernemo.AI that cycle is built into the delivery platform rather than bolted on afterwards.
Discover
Full visibility of AI in the delivery process.
- A register of the models, agents and prompt templates used by the platform
- Vendor, version, owner and area of use for every asset
- A view of which models touch which repositories and data
Govern
Policies instead of good intentions.
- Per-organization policies: allowed vendors, processing location, data rules
- Human approval gates at every stage of the lifecycle
- Use cases mapped to the AI Act, GDPR, DORA and NIS2
Secure
Every model response checked before it is used.
- Guards for sensitive data, toxicity and prompt injection
- RBAC, SSO, least privilege, tenant isolation
- Encryption in transit and at rest, on-premise mode
Observe
A complete trail, ready for the auditor.
- A tamper-proof log: who, what, when, which model and why
- Agent honesty checks: did they do what they report?
- History of gate decisions and artifact versions
Measure
The value of AI in money, not claims.
- Token Dashboard: cost per user, department, project and model
- The cost of every task before the invoice arrives
- Savings reports against the pre-deployment baseline
How it works in practice
A bank employee pastes part of a customer complaint, including a national ID number and an address, into the assistant. The model drafts a reply that contains that data. Before it reaches the browser, the guards detect the personal data, the organization's policy requires redaction, and the user receives the reply with masked fields and an explanation of why.
An entry appears in the administrator panel: who, when, which model, what was detected and what action was taken. The whole thing takes a few dozen milliseconds and is fully reproducible during an audit.
Plugged into your governance ecosystem
Need oversight of all AI in the company, not just in software delivery? See AI Cerber.
Vernemo.AI does not replace corporate GRC, CMDB or SIEM tools. It feeds them data from an area that usually remains a blind spot: AI used in software delivery.
- Audit logs and guard incidents sent to your SIEM
- The AI asset register feeding the internal inventory of AI systems required by the AI Act
- Cost and usage reports for IT controlling
- Policies changed centrally, with no restarts and no changes to modules
Who this layer is for
Risk and security
Risk assessment of AI use cases, regulatory compliance monitoring and audit-ready governance across the whole lifecycle.
IT and technology leaders
Control over which models and agents work on the company's code, with the option to switch vendors without rebuilding processes.
Board and finance
One number: what AI in software delivery costs and what it saves. Per department, per project, per vendor.
We will show you the Token Dashboard and the audit trail on your scenario
A demo for security and compliance teams with sample policies for your industry.
A secure development lifecycle for the age of AI agents
AI speeds up writing code, and faster code forces a change in the whole process: requirements, branching strategy, quality gates, testing and deployment. The AI SSDLC shifts security left and builds it into every step, with agents doing the work and people making the decisions.
Why AI-generated code needs an SSDLC
Independent research shows that raw model output is not fit for deployment without verification. In Veracode's 2025 report, 45% of AI-generated code samples failed security tests and introduced vulnerabilities from the OWASP Top 10.
The value therefore lies not in generation itself but in the machinery that decides which generated code reaches production. That machinery is what we build.
Where a human always decides
In 2026, AI does not take responsibility for:
- architecture decisions
- business logic with exceptions and regulatory requirements
- authentication, cryptography, DLP and compliance
- cost, energy and performance optimization
- organizational context
That is why, in these areas, the platform prepares the material for a decision rather than making it.
The AI SSDLC step by step
From sprint planning to production monitoring. At every stage: what the Vernemo agent does and what remains your team's decision.
- PLAN
Planning and requirements
Story prioritization by the Product Owner, non-functional security requirements as a standing part of planning, and test scope set by product risk analysis.
AgentSpecification, user stories, acceptance criteria, detection of gaps and inconsistencies, a proposed test scopeHumanPriorities, specification sign-off, risk level decision - BRANCH
Branching strategy
Faster coding calls for short-lived branches, feature flags and more frequent merges. Long-lived feature branches become a bottleneck.
AgentProposed branch policies, small incremental diffs instead of huge PRsHumanChoice of model: feature branches or trunk-based with feature flags - COMMIT
Every commit triggers checks
Unit tests, code quality scans, SAST and open-source component analysis (SCA) run automatically on every change.
AgentUnit test generation, a review with findings and fixes, flagging of unsafe constructsHumanAccepting or rejecting each fix - G1
Quality gate 1: branch acceptance
Code review and merge via pull request. Build, test and scan results are the basis for accepting the change or raising defects.
AgentA quality report with recommendations, changes mapped to requirementsHumanThe merge decision - SIT
Release, integration testing and UAT
A release branch from approved changes, integration and acceptance testing, DAST, penetration and performance testing, vulnerability scanning.
AgentE2E scenarios, synthetic data, exploratory testing, automated pentest, Jira defects with reproductionsHumanPrioritizing findings, UAT sign-off - G2
Quality gate 2: deployment acceptance
Deployment is blocked if a new high or critical finding has appeared.
AgentRelease validation against business and technical requirements, release notes, a rollback planHumanThe go/no-go decision - DEPLOY
Deployment
A pipeline builds an image from the latest artifacts, publishes it with a tag to the container registry and installs it in the right environment, with code signing and secrets management.
AgentPipeline and infrastructure configuration generation, environment parameter checksHumanApproval of infrastructure changes - RUN
Monitoring and maintenance
Environment-specific monitoring and alerting, application security logs in the SIEM, runtime protection (RASP or WAF) and patch management.
AgentIncident classification, log analysis, ticket write-ups and RCA, documentation updatesHumanIncident response, change decisions
Maturity map: from assistant to context-aware agents
You can start with agentic SDLC in a simple way. The value grows once AI gets the organization's business context and consistent artifacts from the whole process.
Core capabilities
Code review, documentation and refactoring, code and unit test generation, questions about the code. Available in popular coding assistants.
Code with business context
Reviews against organizational standards, context-aware conversion of large codebases, code generation from sequence diagrams and user stories to client standards. Requires knowledge repositories and context management.
Extended capabilities
Functional testing from scenarios to code, synthetic data, OWASP and NIST compliance, solution architecture, backup and rollback plans, release notes. Requires consistent artifacts across the process.
Advanced engineering, highest return
Whole-repository conversion, release validation against requirements, detection of configuration drift between environments, release risk assessment, incident support. Vernemo.AI works at levels 2–4.
A three-phase SSDLC programme
The platform is one thing. A lasting effect comes from a programme covering governance, engineering, verification and operations. We run it together with your security and architecture teams.
| Area | Phase 1: foundations | Phase 2: integration | Phase 3: maturity |
|---|---|---|---|
| Governance and culture | A secure SDLC programme, risk and effectiveness metrics, a vulnerability management process | Role-based secure development training, security requirements for suppliers | Security champions in engineering teams |
| Engineering | Security requirements as part of sprint planning, an approved list of open-source components | A library of security patterns and anti-patterns, secure code review in the review process | Threat modelling and security architecture reviews |
| Verification | SAST integrated with the automated build | Authenticated DAST of applications and APIs, IAST in the build process | Penetration testing at a cadence set by risk assessment |
| Operations | Patch management, deployment automation | Secrets management with rotation, code signing, application security logs in the SIEM | RASP or WAF, Kubernetes security best practices |
The programme map is indicative. We set the order and scope after assessing your organization's maturity.
Where AI gives the most leverage
Typical uses in each phase of the lifecycle and the Vernemo modules that deliver them.
| Phase | AI use cases | Vernemo modules |
|---|---|---|
| Requirements and planning | User stories from conversations and documents, reverse-engineering requirements from an existing system, gap detection, story risk analysis | Documentalist, AI Analyst |
| Analysis and design | Scaffolding to organizational conventions, architecture and flow diagrams, API contracts | AI Architect, AI System Analyst |
| Development | Code generation and review, SCA and SAST, security fixes, unit tests, language conversion | Coder, Code Generator, Palimpsest |
| Testing and QA | Test plans and cases, synthetic data, script automation, exploratory testing, penetration testing | Scenarist, Bugger, Pentest |
| Maintenance | Monitoring, pipeline generation, incident triage, up-to-date documentation | GUI Documentation, Compliance, Token Dashboard |
AI SSDLC maturity assessment
A workshop for CISOs, architects and delivery leads: where you are on the map, what to shift left and which module to start with.
Shadow IT and shadow AI: offer a better tool instead of a ban
Employees turn to unapproved apps and AI assistants because they want to work faster. A ban will not change that. Visibility, clear rules and an official tool that is better than the one in a browser tab will.
What shadow IT is
Shadow IT is software, cloud services, devices and applications used in an organization without the IT department's approval or knowledge, outside procurement and security controls. Vulnerability scanners cannot see what is not in the inventory.
Shadow AI: the fastest-growing part of the problem
Shadow AI is the use of AI tools, models and features without approval from IT and compliance. Classic shadow IT moves files to uncontrolled places. Shadow AI moves whatever someone pastes into a chat window: source code, customer data, contracts, configuration and passwords. That leak cannot be undone.
It also covers AI features built into applications that are already approved, and agents and integrations with external models built by teams without registration or risk assessment.
The scale of the problem
- According to IBM's Cost of a Data Breach Report 2025, breaches involving shadow AI cost an average of USD 670,000 more than other incidents, and 20% of the breaches studied resulted from unauthorized use of AI.
- Gartner estimates that by 2030 more than 40% of organizations will experience security or compliance incidents linked to shadow AI.
- Most employees who reach for unapproved tools do so in good faith, to work more efficiently.
The risks the board and the regulator see
Irreversible data leakage
Personal data, trade secrets and source code end up in external models, often on private accounts, over encrypted connections nobody monitors.
Compliance breaches
Processing data outside data processing agreements breaches the GDPR. Unregistered AI systems make AI Act obligations harder to meet, and unknown ICT providers are a problem under DORA and NIS2.
No accountability
There is no record of what data was used, what decisions were made based on AI output, or where a piece of code in the repository came from.
A larger attack surface
Applications outside SSO, unpatched tools and uncontrolled integrations are doors the security team does not know exist.
Duplicated costs
Several departments pay for similar subscriptions, and the organization loses negotiating power and visibility of software spend.
A five-step control programme
Mature organizations treat shadow IT as a signal, not a failure. It shows where official tools are not keeping up with what people need.
Discover
An inventory of applications, SaaS subscriptions, cloud services and AI tools: spend analysis, network traffic, asset management and the configuration management database (CMDB).
Assess
A risk rating for every tool: what data it processes, where, under what agreement and who owns it.
Regulate
An AI usage policy, a catalogue of approved tools and a fast track for approving new ones. A shorter process reduces demand for workarounds more than any ban.
Offer
An official tool that is just as convenient but runs inside the company perimeter, with logging and data controls.
Monitor
Continuous observation, employee education and regular audits of AI use.
How Vernemo.AI helps
Sanctioned AI for IT teams
Developers, testers and analysts get AI agents that run in your infrastructure, with SSO, token accounting and a full audit trail. The reason to paste code into a private chat disappears.
- The Compliance guard redacts national ID numbers, tax numbers and sensitive data before they go any further
- PolyGuard blocks prompt injection attempts
- The Token Dashboard shows real usage per department
Business applications brought out of the shadows
Part of shadow IT is tools built by the business itself: macros, Access databases, spreadsheets full of logic, low-code apps. Critical processes running outside IT's control.
- AS-IS analysis and documentation of what they actually do
- A rewrite into a managed application with tests and access control
- Data migration with parity validation
Shadow AI audit in software delivery
AI SDLC consulting: we identify which AI tools your teams use and with what data, assess the risk and prepare a plan for moving to sanctioned solutions.
A corporate AI usage policy
A draft policy, a catalogue of approved tools, a fast-track approval path and a training programme, aligned with the AI Act, the GDPR and your sector regulator's requirements.
Data sources: IBM, Cost of a Data Breach Report 2025; Gartner press release of 19 November 2025. The programme approach draws on IT and AI asset management practices in large enterprises, including ServiceNow's.
Find out how much AI runs in your organization outside of control
Start with a shadow AI audit of your software delivery teams.
Security and compliance for procurement, risk and compliance teams
Everything your team needs to assess Vernemo.AI as a supplier: deployment models, data protection, regulatory mapping and the documents available for due diligence.
Deployment models
| Multi-tenant SaaS | Dedicated instance | On-premise | |
|---|---|---|---|
| Where it runs | Vernemo infrastructure in the EU | An environment dedicated to a single client | Your infrastructure, including air-gapped |
| Where the data is | Tenant isolation | Only in the client's instance | The knowledge base, artifacts and audit trail never leave your network |
| AI models | Vendors selected by policy | Vendors selected by policy | Including open-weight models on your hardware |
| For whom | Teams getting started | Large organizations | Banks, insurers, pharma, public sector |
Data and access protection
- A full audit trail: who, what, when and why
- No model training on client data without consent
- Environment isolation and tenant separation
- Encryption in transit and at rest
- RBAC, SSO and data retention policies
- Data anonymization for test environments
- The client owns the IP of all artifacts
- Cloud processing requires explicit administrator authorization
Due diligence documents
On request, and once a non-disclosure agreement is signed, we provide a pack for security, legal and procurement teams:
- Answers to a supplier security questionnaire
- A template data processing agreement
- A description of the architecture and deployment topology
- A template SLA and incident handling procedures
- Company registration details and corporate information
Regulatory map
How the platform's mechanisms address the requirements your compliance team works with.
| Regulation / standard | Requirement | How Vernemo.AI responds |
|---|---|---|
| EU AI Act | Human oversight, transparency, risk management, documentation | A human approval gate at every stage, a model register, a decision log, control of model output |
| GDPR | Minimization, security and location of processing | Local personal data guards, redaction, anonymization, on-premise |
| DORA | ICT risk and third-party provider management | Deployment in the client's infrastructure, audit trail, independence from model vendors, implementation support |
| NIS2 | Supply chain and software development security | AI SSDLC, automated security testing, vulnerability reports |
| GxP, GAMP 5, ISO 13485, 21 CFR Part 11 | System validation, change control, auditability | Automated IQ/OQ/PQ protocols, SRS/DDS/URS documentation, versioning |
| ISO/IEC 27001 | Information security management system | Platform controls designed in line with the standard's requirements |
NIS2 and DORA readiness is delivered together with implementation consulting tailored to your organization.
Case studies in regulated industries
Clients remain anonymous under our confidentiality agreements. We share details in a reference call.
Insurtech: documentation and testing for a Java system
- Client
- An insurtech company in Poland, 4 testers and 3 developers
- Problem
- 70% of time spent on manual documentation and testing, no regression test coverage, a growing risk of knowledge loss
- Deployment
- 6 weeks, on-premise
In weeks 1–2 the agents mapped 23 bounded contexts and produced 8 specifications. In weeks 3–4 the Scenarist generated 127 test cases for 34 business paths, and the tests uncovered 15 bugs documented with screenshots. In weeks 5–6 the Code Generator processed around 430,000 tokens and prepared 6 before-and-after audit reports.
Pharma: modernizing a Delphi/VB6 regulatory system
- Client
- A pharmaceutical company with more than 500 employees
- Problem
- 25 years of business logic in the user interface with no documentation or tests, knowledge held by 3 experts, ISO 13485 certification impossible without an auditable system
- Deployment
- 8 weeks, on-premise, GxP-compliant
The AI Analyst reconstructed the logic from the code and from conversations with the experts. The system was rewritten from Delphi/VB6 into .NET 8 with a full audit trail. The test generator prepared IQ/OQ/PQ validation scenarios in line with GAMP 5, and the SRS, DDS, URS and change control documentation was audit-ready. Electronic signatures are supported in line with 21 CFR Part 11.
Would you like to speak to a reference client?
We will arrange a call as part of your supplier assessment.
This code applies to the management board, employees, associates and subcontractors of Vernemo Sp. z o.o. We also expect it to be observed by the partners we deliver projects with. It is grounded in our values: Customer at the centre, Integrity, Passion, Innovation, Collaboration.
People decide
AI prepares, people approve. We do not deploy solutions in which a model makes decisions with material consequences for people or critical systems on its own, without human oversight.
- Every artifact passes through an approval gate held by a responsible person
- Architecture, security and regulatory decisions always belong to people
Honesty with clients
We say what works and what we have not yet proven. We do not inflate results, hide the limitations of the technology or promise certifications we do not hold.
- We communicate results in context: on which project and against what baseline
- Token costs are passed through at the vendor's price with a transparent service margin
Client data belongs to the client
We process data only for the purpose and to the extent entrusted to us by the client. We do not train models on client data without explicit consent. The intellectual property of the artifacts we produce belongs to the client.
Security before speed
We do not deliver code that has not been verified. We do not silently fix or hide vulnerabilities: every unsafe construct is flagged and passed on for an informed decision.
AI transparency
We disclose which artifacts were produced with AI and which models were used. We provide an audit trail that shows who made a decision, when and why.
Fairness and non-discrimination
We use mechanisms that detect toxic content, hate speech and racial, gender and age bias in model responses. We treat all clients, employees and candidates equally.
Fair competition and anti-corruption
We neither offer nor accept benefits that could influence business decisions. We follow fair competition rules and public procurement law.
- Gifts and hospitality only of token value, recorded openly
- Conflicts of interest are declared before a decision is made, including in relations with related companies
Confidentiality
We protect the confidential information of clients, partners and our own, including after the engagement ends. We publish information about clients only with their consent or in anonymized form.
People at the heart of change
AI should relieve people of repetitive work, not take away their influence. We help clients reskill their teams and build the skills to work with AI.
Compliance with the law
We comply with the law, including the AI Act, the GDPR, copyright, open-source licences, international sanctions and export controls. We respect the licences of the components we use.
Environmental responsibility
We match the model to the task and do not use the largest models where smaller ones will do, reducing energy use and cost.
Reporting misconduct
Anyone can report a breach of this code or of the law, including anonymously, without fear of retaliation, in line with the Polish Whistleblower Protection Act.
- Reporting channel: etyka@vernemo.ai
- We acknowledge receipt within 7 days and report back on follow-up actions
Version 1.0, October 2026. The code is reviewed at least once a year. In case of discrepancies, the Polish version prevails.
Revolutionizing software development
Our AI platform orchestrates the best models and integrates them with the client's IT processes, while keeping full control, security and an audit trail.
Vision
We are building a world in which anyone, whatever their background, can successfully create and maintain applications.
What sets us apart
Orchestration of multiple AI models matched to the task, combined with the governance regulated organizations require: from idea to commercial success, with a human at every decision.
Values
Customer at the centre
The platform works with the client's knowledge, conventions and tools. Testers verify instead of writing from scratch.
Integrity
Transparent costs, transparent limitations, results with context.
Passion
We deliver in weeks because we enjoy solving hard problems.
Innovation
Output verified by models from different vendors, Polish guard models.
Collaboration
We work with integrators and client teams, not instead of them.
Company details
- Full name
- VERNEMO Spółka z ograniczoną odpowiedzialnością
- Registered office
- Aleje Jerozolimskie 200, 02-486 Warsaw, Poland
- KRS (National Court Register)
- 0001226259
- NIP (tax ID)
- 5223364871
- REGON
- 544063261
- Share capital
- PLN 50,000
Sister company
Arpino Sp. z o.o. is Vernemo's sister company, active in software since 2018 and registered at the same address in Warsaw.
KRS 0000736345, NIP 5223129058, REGON 380515110
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