From decades-old legacy estates to regulated, air-gapped networks: FRYTAG turns the places where generic AI coding fails into the places where governed AI delivery works.
On mature estates, business logic lives in old modules, stale documentation, and the memory of a few experts — and plans look complete until integration effects surface in production. FRYTAG builds a living graph of history, build, and code, then turns every change request into an evidence-backed plan with affected areas, risks, and relevant tests up front.
Retirement, attrition, vendor transitions: when people walk out, their system knowledge walks with them. FRYTAG captures senior expertise in structured seeding sessions — versioned, auditable claims with provenance and freshness. The knowledge stays with the team, not in inboxes and heads.
In banking, insurance, and the public sector, cloud copilots are not an option and GPU budgets are thin. FRYTAG is built for exactly that: precise deterministic context first, local models fully supported, proven database stack — capable AI delivery without a byte leaving your infrastructure.
Push a ready epic or spec before you go to bed; wake up to an evidence-backed pull request — or a clear, actionable failure report. The FRYTAG Autocoder works in isolated workspaces, enforces path boundaries, and never merges: humans keep merge authority, always.
Build-to-order and third-party software arrives with quality you cannot see. The FRYTAG Systems Augur reviews it through ten specialist lenses — including security against named standards like OWASP and CWE — infers the real architecture from the code, and compiles an executive summary you can hand to your board.
New developers spend months rediscovering what the team already knows. FRYTAG serves onboarding context packs — domain maps, behavior summaries, misleading names, known traps — and every answer cites its evidence. Ramp-up becomes a guided tour instead of an archaeological dig.
Regression zones unknown, test relevance unclear, missing tests discovered in production. FRYTAG analyzes co-change history and coverage up front: every change plan lists the tests that matter and the tests that are missing — before the first line is written.
Copilot, Claude Code, Cursor — rolled out to hundreds of engineers, each rescanning your repositories differently. FRYTAG gives every agent the same governed source of truth: tenant-isolated machine access, contracted tool interfaces, one knowledge graph instead of a hundred private ones.
Structure, boundaries, and evidence — not opinion.
Delivery confidence without surrendering control.
AI adoption that passes governance and audit.
Named standards, audit trails, due diligence.
No. Keep Copilot, Claude Code, Cursor, and the rest — they plug into one governed gateway. FRYTAG supplies the context, governance, and evidence; your tools keep writing code.
No. FRYTAG runs on your infrastructure with a proven database stack, and local models are fully supported. Sovereign and air-gapped setups are a design goal, not an afterthought.
Never. The Autocoder produces evidence-backed pull requests; merging stays with your team. Human merge authority is a built-in invariant, not a policy promise.
It is built for them. Brownfield systems with years of history, weak documentation, and scarce experts are exactly where the knowledge graph pays off most.
No. FRYTAG's approach is context precision over model size: with precise, evidence-backed context, smaller and local models deliver what raw-repo prompting cannot.
Transparency is built in: model usage, tokens, latency, and costs are measured per task, and every fact in the knowledge graph carries evidence, confidence, and freshness.
Every FRYTAG engagement begins the same way: your code, your rules, your people in the loop. Bring the system nobody dares to touch — that is where governed AI delivery shows what it can do.
