Autonomous AI systems your risk committee will actually approve.
We design, build, and hand over governed agentic software — the Agentic Development Lifecycle, enforced decision rights, tamper-evident audit trails, and hard cost ceilings. Shipping production systems since 2019.
What We Build
ADLC Platform Enablement
Executable review gates for agent-authored code: agent CI/CD with worktree isolation, policy-as-code release gates, telemetry your engineers can read.
Read more →02Agentic Systems Engineering
Hierarchical multi-agent orchestration, evaluation harnesses treated as infrastructure, structural safety at the protocol boundary.
Read more →03AI Governance & Readiness Audit
Four weeks. AI surface, model spend, control gaps and shadow usage mapped. Board-ready risk brief against NIST AI RMF and the EU AI Act.
Read more →04AI Cost Governance
Runaway token spend is an architecture defect. Tiered model routing, hard budget ceilings, per-call ledgering by model and prompt hash.
Read more →05Custom Software Engineering
React, Angular, Python, PHP, Kubernetes. Production delivery since 2019 — the discipline the AI work rests on.
Read more →06Managed AI Operations
Someone has to own the agents at 3am. Drift and decay monitoring, cost and latency SLOs, escalation runbooks, real on-call.
Read more →ADLC — the software lifecycle, rebuilt for agents that write the code.
SDLC assumes a human at every gate. That assumption fails the moment agents are producing the majority of the diff. Teams respond by either removing the gates — and losing control — or keeping them and losing the speed they bought AI for.
The Agentic Development Lifecycle keeps every gate and makes it machine-enforceable. Throughput rises. Governance does not degrade. Both are measurable.
Intent
Requirements captured as executable contracts with acceptance criteria, not tickets.
Plan
Agent decomposition with token budget, blast radius and escalation ladder declared upfront.
Generate
Tiered agent workforce in isolated worktrees. Model class matched to task complexity.
Verify
Adversarial review agents. A finding ships only if it survives independent refutation.
Release
Policy-as-code gates, signed provenance, rollback triggers declared before deploy.
Observe
Drift, decay, cost and behaviour tracked as first-class SLOs with automatic demotion.
Runaway token spend is a design failure. We engineer it out.
Agentic programmes fail in two directions: they consume budget with nothing in production, or they take actions outside their mandate. Both are architecture problems with architecture answers.
Our reference system runs an entire autonomous research organisation at $0.88 per terminal verdict. Model tiering is the discipline that makes that possible.
| Tier | Model class | Assigned work | Share of calls |
|---|---|---|---|
| T0 | scripts — no LLM | Polling, status checks, test runs, shipping | — |
| T1 | light | Fetch, format, classify against explicit criteria — never verdicts | — |
| T2 | mid | The default workforce: coding, tests, refactors, research passes | 68% |
| T3 | heavy | Design and risk logic: multi-file architecture, gate logic, cross-module debugging | 27% |
| T4 | frontier | Orchestration: synthesis, verification of workers’ diffs, money-safety decisions | 5% |
What the machine owns. What leadership owns.
Autonomy without an explicit boundary is negligence. We write the boundary down, encode it, and make it something the system cannot edit. This is the artefact your auditors and your board will ask for.
- Idea generation — swarms propose, bull/bear agents debate, a judge rules with confidence
- Research, execution and stress replay against historical regimes
- Gate verdicts — promote, refine or kill, with an evidence dossier written for every kill
- Staged deployment, decay detection and automatic demotion
- Daily postmortems and lesson-writing into durable memory
- Irreversible commitments — a signed audit row is the only path to production
- Capital and resource allocation
- Infrastructure and third-party integration changes
- Guardrail thresholds — the constitution the machine cannot edit
- Nothing else. By design.
An engineering firm that publishes its own numbers
Birchblue builds governed autonomous systems for enterprises — the Agentic Development Lifecycle, enforced decision rights, tamper-evident audit trails and hard cost ceilings.
We have shipped production software since 2019. We run our own agentic stack in production first, and we publish its operating metrics rather than its marketing.
Scope
Fixed-scope statement of work with declared gates, named outcomes and a token budget per stage. No time-and-materials drift.
Build
Tiered agent workforce in isolated worktrees. Adversarial review before merge — a change ships only if it survives refutation.
Prove
Evaluation harness run against your own traffic. Cost and latency SLOs declared, rollback triggers agreed before release.
Hand over
Runbooks, on-call rotation and training. You operate it without us — or we keep the pager, on retainer.
Why teams bring us the system that won’t cross the last mile
Most AI projects die between the demo and production. We engineer the layer that gets them across.
Fixed scope, declared gates, named outcomes. One client engagement at a time — that constraint is why our dates hold.
We run it on our own book first
AAQuant is our reference implementation, not a slide. Every number we publish about it comes from its own ledger.
Governance is a byproduct, not a project
Audit evidence falls out of the pipeline. You are not assembling it the week before a board meeting.
Cost is engineered, not discovered
Tiered routing and hard ceilings are designed in at the plan stage. 5% of calls reach the frontier tier.
One engagement at a time
That constraint is why our dates hold. You get senior people, not a bench.
Some of Our Previous Projects
AAQuant
- client: Birchblue (in-house)
- Location: USA
- Year Of Complited: 2026
Tesla
- client: Tesla
- Location: USA
- Year Of Complited: 2024
JK Design
- client: JK Design
- Location: USA
- Year Of Complited: 2019
Evil Geniuses
- client: Evil Geniuses
- Location: USA
- Year Of Complited: 2019
Onesearch
- client: Enterprise search client
- Location: USA
- Year Of Complited: 2018
Beezone
- client: Digital library client
- Location: USA
- Year Of Complited: 2018
360 Alumni
- client: Higher-education client
- Location: USA
- Year Of Complited: 2018
Globalinfo Tech
- client: Global InfoTech
- Location: USA
- Year Of Complited: 2019
Minimal distortion. Maximum output.
Requirement gathering is the product decision. On our reference build, discovery ran a full quarter — structured interviews with practitioners, manual operation of every workflow end to end — and produced 40+ planning documents before a line of the autonomous system was written. We automated only what we understood.
