Put your own codebase, conventions and runbooks behind your developers' AI tools.
We connect AI assistance to your repositories, architecture docs and ticket history, add task-specific helpers for tests, migrations and reviews, and keep every change inside the pull request, CI and approval process you already trust.
- 01Index code and context
- 02Pick up a task
- 03Propose changes and tests
- 04Run automated checks
- 05Review and merge
The workflow
Engineering teams maintaining large or aging codebases, and the senior engineers who spend much of their week answering questions and reviewing changes.
- New engineers need weeks of pairing before they can change core services safely.
- Test coverage is thin in the code that changes most, so reviews rely on memory.
- Repetitive changes such as dependency upgrades and API migrations take weeks of manual work.
- Generic AI coding tools do not know internal libraries, conventions or security rules.
Index code and context
SystemRepositories, architecture decision records, runbooks and resolved tickets are indexed with the same access rules as the source systems.
Pick up a task
PersonA developer takes a ticket and asks where the relevant code lives, how a service behaves, or how similar changes were made before.
Propose changes and tests
AIThe assistant drafts code changes, tests and a description following team conventions, citing the files and examples it used.
Run automated checks
SystemThe pull request runs through CI, static analysis, secret scanning and the test suite like any other change.
Review and merge
PersonA developer reviews the change, edits it as needed and a code owner approves it before merge.
Where people stay in control
No AI-generated change merges without a person reviewing it and the normal code-owner approval. The assistant has read access to code and write access only to branches and draft pull requests; it cannot push to protected branches, change CI configuration or access production secrets.
- Time for a new engineer to ship a first production change
- Review cycle time on pull requests
- Test coverage in the most frequently changed modules
- Defects found after merge in AI-assisted changes compared with others
Data and integrations
- Read access to repositories and the code hosting platform API
- Architecture docs, runbooks and ticket history
- CI pipeline and static analysis configuration
- Team coding standards and security rules
Realistic boundaries
- Output quality depends on test coverage; where tests are thin, reviewers carry more of the checking.
- Source code may only be sent to model endpoints your security team approves, which can limit model choice.
- Architecture and design decisions stay with engineers; the assistant proposes, it does not decide.
How we build it
- AI App DevelopmentComplete AI products — interface, model layer, application code and integrations — designed for the people who will use them every day.
- Enterprise & Private AIAI deployed with the data isolation, access control and operational ownership your security and compliance teams require.
- RAG & Enterprise Knowledge SystemsAnswers drawn from your own documents and data, with sources shown, permissions respected and content kept current.
- Quality EngineeringTest automation, performance testing, release checks and production quality signals built into how software is delivered.
- Technology & SaaSAI features inside your product, developer tooling, support operations and platform engineering — built by a team that ships software, not just prototypes.
- Financial ServicesDocument review, knowledge access, fraud signals and customer operations — built with the controls and auditability the sector expects.
Questions buyers ask
Off-the-shelf tools are a reasonable starting point. We add what they lack — your internal context, task-specific workflows such as migrations or test generation, and controls on data handling and permissions — and we can build on top of the tools your team already uses.
Only if you choose that. We can run on models deployed in your own cloud account or use approved providers with no-retention terms. See private AI deployment.
Map this workflow with us.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.