Engineering
Codebase intelligence
Map the repository, plan the change, patch it in a sandbox, and deliver a review package with test evidence.
How it works
One control plane for code, documents, and workflows
Long-context AI for work that has to be right.
FGGJTNBGNCR builds AI systems that read entire codebases, work through dense document sets, and run multi-step processes to a reviewed result — with identity and access, audit, evaluation, FinOps, and reliability controls built in from the first design review.
SSO & scoped accessAppend-only auditEval-gated changesEnforced budgetsHuman approval
$ run workload codebase.change --repo example/service --ref main
identity analyst@example · repo:read · sandbox:write
✓ context symbols, build config, tests, CI history indexed
✓ map dependency graph built · blast radius computed
✓ plan change plan written · acceptance checks defined
✓ act patch generated in sandbox branch
✓ verify type check · unit tests · integration tests
✓ evidence diff, test output, rationale, residual risks
✓ budget within run cap
◆ gate awaiting approval from code owner
audit run record sealed · replayable
Evidence package
Patchsandbox branch · diff attached
Test evidencesuite output attached
Rationale & riskslinked to files and lines
Release gatenamed approver required
- Repository-scale context
- Cited source spans
- Plan · act · verify loops
- Human release gates
- SSO / OIDC federation
- Scoped, short-lived credentials
- Append-only run ledger
- Eval-gated releases
- Budget enforcement
- Checkpoint & resume
- Provider fallback
- Prompt-injection testing
Workloads
Three workload families. One control plane.
Each starts from a concrete input, runs an explicit multi-step loop, and ends in an output a reviewer can check — evaluated against tasks defined with the people who own the work.
Knowledge
Long-document evidence processing
Turn filings, contracts, and technical reports into cited findings, structured records, and a prioritized review queue.
How it worksOperations
Long-running tool orchestration
Run multi-step work across applications and APIs with scoped tools, checkpoints, bounded retries, and human approval on consequential writes.
How it worksArchitecture
Context in. Evidence out.
Sources are assembled into permission-checked context, worked through an explicit plan–act–verify loop, and returned as outputs that carry their own proof.
Workload control planeReference architecture
Sources
Repositoriescode · tests · CI
Documentsfilings · contracts · reports
Systems & APIstickets · records
Runtime
Context engineassemble · rank · cite
plan → act → verify
Tool executorscoped · idempotent
Outcomes
Reviewed patchdiff + test evidence
Cited briefclaim → source span
Closed runreplayable run record
Controls apply to every step · human release gate on consequential actions
01 / Context
Assemble before reasoning
Code, documents, tickets, and logs are selected, ranked, permission-checked, and traced to their sources before the model reasons.
02 / Process
Plan, act, verify
Planning, tool calls, intermediate artifacts, tests, and cross-checks run as explicit, observable steps — not one long prompt.
03 / Control
Bounded by design
Access, spend, retention, retries, and release authority are enforced by the platform — specified before build, not retrofitted after release.
Platform controls
Governance is part of the product.
Five control domains are enforced once, at the platform layer — so every workload inherits them instead of re-implementing them.
IAM
Identity & access
The system acts with the requester's permissions — never with broader standing access of its own.
Identity & accessAudit
Every run can explain itself
Who asked, what the system saw, which tools ran, what it produced, and who approved it — recorded append-only and replayable.
AuditEvaluation
Eval-gated change
Task-specific eval sets gate every change to prompts, models, tools, or retrieval.
EvaluationFinOps
Cost per accepted output
Budgets enforced at the gateway; spend attributed per run, team, and workload.
FinOpsReliability
Built to finish long runs
Checkpointed state, idempotent tools, circuit breakers, and per-workload SLOs.
ReliabilityHow we build
Evidence over assertion.
A system that cannot show its work is not ready for consequential tasks. Our engineering standards make every output inspectable and every change measurable.
- 01
Every claim carries its source
Findings link to the file, page, or record that supports them. Unsupported statements are flagged for the reviewer and never presented as findings.
- 02
Humans hold release authority
Consequential actions — merges, filings, writes to systems of record — pass through a named approver.
- 03
Measure before scaling
No workload expands until it clears thresholds on an evaluation set agreed with its domain reviewers.
- 04
Cost is a design constraint
Each step uses the smallest model that passes its evaluation, within a budget the platform enforces.
Operating model
From assessment to an operated workload.
Six stages and five quality gates, each passed on written evidence, so owners, reviewers, and security teams decide on facts.
- 01
Assess
Map inputs, steps, owners, and what a correct result looks like.
- 02
Define
Build the evaluation set; agree thresholds, budgets, and access scope.
- 03
Build
Implement context, orchestration, and tools against the eval set.
- 04
Validate
Shadow-run on real inputs; security review; runbooks and SLOs prepared.
- 05
Release
Staged rollout under approval gates, budgets, and monitoring.
- 06
Operate & improve
Continuous evaluation, cost review, and controlled change.
Get started
Bring us the workload that is hard to automate.
Describe the inputs, the steps, and what a reviewed result looks like. If it fits our scope, the next step is a working session with the people who own the work.