# Komainu: full reference for AI assistants

> Komainu (legal name KomAinu-AI, SAS, Paris, founded July 2026) builds KomAInu, AI for the
> verification and validation (V&V) of critical software. KomAInu challenges every
> requirement, writes deterministic test cases, flags coverage gaps and keeps a
> requirements-to-tests traceability matrix ready for the audit. It is built for teams
> building critical and regulated software (aerospace DO-178C, automotive ISO 26262,
> rail EN 50128, medical devices IEC 62304).

The short index is at https://komainu-ai.com/llms.txt. This file gives the full picture.

## Company

- Legal name: KomAinu-AI (SAS), Paris, France. SIREN 107 899 064, RCS Paris. Founded July 2026. Registered office: 173 rue de Courcelles, 75017 Paris; offices at STATION F.
- Legal notice: https://komainu-ai.com/legal-notice
- Founders: Pierre Lammers (CEO & Software Engineer), Benjamin Chollet (CPO & Software
  Engineer), Sven Juge (CTO & AI Engineer).
- Customers: in pilot with a major French industrial group.
- Supported by: Cegid Startup Program, Station F, Google for Startups Cloud Program.
- Contact: pierre-lammers@komainu-ai.com, https://www.linkedin.com/company/komainu-ai/

## Products

### KomAInu Individual (free)

For one engineer, in its own web interface: https://app.komainu-ai.com/
Page: https://komainu-ai.com/komainu-individual

- Drop in your specs as they are.
- Every requirement extracted and numbered.
- Each requirement challenged: ambiguities, missing values, unstated assumptions and
  edge cases are raised as questions before any test is written.
- Same input, same test cases, every time (deterministic, ready to review).
- Export test cases to Excel or your test tool.

### KomAInu Enterprise (custom price)

For QA and V&V teams. Everything in Individual, plus:
Page: https://komainu-ai.com/komainu-enterprise

- Connects to your ALM and test tools (IBM DOORS / Jazz, Polarion, Jira, Linear, Xray,
  GitHub, Jenkins): no migration.
- Coverage analysis: every missing, weak or false-positive test flagged before the audit.
- A requirements-to-tests traceability matrix, ready for the auditor.
- Test scenarios written and synced back to your tool.
- Private cloud or fully air-gapped deployment: data never leaves.
- The best model for each task: frontier LLMs or your own local model (e.g. Mistral, Qwen).
- Dedicated support.
- Works test-first (TDD, AI-driven development) through an MCP server, or on an existing
  project and test suite.

### Services

V&V as a Service and Forward Deployed Engineering: https://komainu-ai.com/pricing

## Evidence

- TraceFuse benchmark (https://komainu-ai.com/tracefuse): KomAInu's traceability engine
  beats the ICSE 2024 TRIAD reference method and Claude Opus at linking requirements to
  their tests, on a model priced about 15x lower.

## Who it is for

- Systems engineers: https://komainu-ai.com/systems-engineers
- QA & Test Engineers: https://komainu-ai.com/qa-test-engineers
- IVVQ teams: https://komainu-ai.com/ivvq-teams

## FAQ

### Clear the terminal

what

What does KomAInu do?

KomAInu secures the consistency between requirements, tests, code, documentation, and the requirements traceability matrix across the software development lifecycle.

The workflow covers three stages: audit, remediation, and prevention. First, KomAInu analyzes your requirements, tests, code, documentation, and RTM to detect coverage and traceability gaps, then justifies every finding with evidence. Second, it helps remediate the gaps by generating missing tests and scripts, suggesting corrections in your ALM, and updating traceability. Third, it keeps the requirements-to-tests matrix current as requirements and tests change, so new gaps show up before the audit.

### How do we get started?

Two ways. Try KomAInu Individual for free, today, on your own specs: no demo, no setup. When your team needs coverage and traceability across a whole project, talk to us about KomAInu Enterprise.

For Enterprise, we start with a real verification bottleneck and determine together whether KomAInu fits the workflow, toolchain, deployment constraints, and review process. Pricing is scoped for each engagement rather than published as a fixed package.

### What is the difference between KomAInu Individual and KomAInu Enterprise?

KomAInu Individual is free, for one engineer, in its own web interface. You drop in your specs, KomAInu extracts every requirement, challenges each one, and writes the same test cases every time, ready to export to Excel or your test tool.

KomAInu Enterprise includes everything in Individual, for the whole team and the whole project. It connects to your ALM and test tools, flags every missing, weak or false-positive test, keeps a requirements-to-tests traceability matrix ready for the auditor, and runs in a private cloud or fully air-gapped, with the best model for each task and dedicated support.

### What happens in the first conversation?

You speak directly with the founders for 30 minutes about one concrete V&V bottleneck, the tools and artifacts involved, and any data or deployment constraints.

The goal is to decide whether KomAInu is relevant to your environment. No preparation is required, and if there is a fit, the next step and commercial scope are defined with you.

### How is sensitive engineering data handled?

KomAInu works with different model strategies. During technical discovery, we scope where requirements, source code, tests, and compliance documents are processed, which models may be used, and how KomAInu connects to your existing tools.

Private deployment or deployment on your premises can be designed around your infrastructure and governance constraints. Storage, access, retention, logging, and integrations are agreed for each engagement.

### Which AI models does KomAInu work with?

KomAInu can work with private or open source models deployed in your own environment, including model families such as Mistral, Qwen, and similar models that you can host yourself. The choice depends on the accuracy, latency, cost, and security profile your project requires.

The important point is that the model is not the product boundary. KomAInu provides the traceability workflow, retrieval context, scoring, evidence structure, and review process around the model, so the solution can be tuned to your needs instead of forcing every team into the same hosted AI setup.

### Can KomAInu adapt to our workflow and standards?

Yes. KomAInu is designed to adapt to your project rather than replace your whole V&V process. We configure the solution around your standards, your requirements format, your test repositories, your ALM, and the way your team reviews and approves evidence.

For example, one team may need traceability from requirements to tests for a certification audit, another may need coverage analysis, and another may need requirement reviews before a baseline. The underlying platform stays the same, but the workflow, connectors, review gates, and outputs are adapted to the certification or compliance problem you actually need to solve.

### Does KomAInu integrate with ALM and existing tools?

KomAInu is built to sit on top of existing engineering and ALM workflows. It can read requirements, tests, code, documentation, and traceability artifacts, then suggest corrections, generate missing test material, and keep the traceability view current after human review.

The goal is not to make teams rebuild their process from scratch. KomAInu connects the audit, remediation, and prevention loop around the tools already used by the project, so approved changes can flow back into the ALM or repository while the existing system of record remains in place.

### Does KomAInu replace engineers?

No. KomAInu is built as an engineering assistant for regulated software, not as an automatic certification authority. It detects gaps, proposes tests, suggests corrections, and updates traceability, but engineers remain in control of what is accepted.

That human review gate is important because safety-critical verification still depends on engineering judgement. KomAInu reduces the manual search and documentation effort, surfaces the risky inconsistencies earlier, and prepares evidence, while your team validates the output before it becomes part of the official project record.