Engineering
Forward-deployed engineering
Engineers working inside your repos and change control, with budgeted context windows, machine-checkable goals and closed evaluation loops.
AI systems · Governance · Cloud architecture
We engineer the AI operating system your enterprise runs on.
Governance-grade AI. Decision lineage you can audit. Systems that ship to production — and stay there.
Explore a decision in the graph
- Nodes:
- 1,024
- Edges:
- 3,871
- Guardrails:
- 42active
- Ledger height:
- 8,442,109
Live graph. Backdrop generated with Kling + Seedance via Higgsfield.
- Decision
- Agent / inference
- Guardrail / policy
- Data & context
- Deprecated lineage
Thesis
Three rules every system we ship obeys.
Enterprise AI fails in the gaps between the model, the policy and the change process. We close those gaps with engineering, not guidance.
- 01
Every decision is ledgered.
Each consequential model output writes a hash-chained record — context hash, model version, guardrail verdicts, reviewer — so it can be replayed, audited and defended a year later.
- 02
Guardrails are a plane, not a prompt.
Policy runs as its own versioned service between reasoning and action. PII redaction, jailbreak classifiers, domain rules and spend gates are tested like code and enforced outside the model.
- 03
Production is the only environment that counts.
We forward-deploy engineers into your stack and hold the work to SLOs, eval gates and a rehearsed rollback. A pilot that never reaches production is a cost, not a result.
The Earp AI Operating System
Six planes. Each does one job, and each can be audited.
A request enters through context, is reasoned over, checked by policy, committed as a decision, shipped through deployment and watched by observability. Every boundary is an interface you can test.
- 01Context planeRetrieval topology, token budgets per source and provenance tags — every token the model sees is chosen, costed and traceable.
- 02Reasoning planeModel routing by task, latency and cost curve across providers, with prompts and tool schemas versioned like code.
- 03Guardrail planePolicy-as-code between reasoning and action: PII redaction, jailbreak classification, domain rules and spend gates.
- 04Decision planeHash-chained ledger entries, idempotency keys and deterministic replay for every consequential output.
- 05Deployment planeEval-gated releases, canaries and one-command rollback into your Azure, AWS or GCP landing zone.
- 06Observability planeGolden-set regression, drift metrics and SPC control charts that page a human before a customer notices.
Governance & decision lineage
Every output leaves a receipt.
When a regulator, a customer or your own incident review asks why the system did something, the answer is a ledger lookup — not an archaeology project across logs.
- Hash-chained entries
- SHA-256 over the previous hash plus the canonical record body. Edit one field and every later hash fails verification.
- Deterministic replay
- Context hash, model version and guardrail verdicts are stored with the output, so a decision can be re-derived and compared byte for byte.
- Idempotency keys
- One key per intent. Retries, timeouts and duplicate webhooks resolve to the same ledger entry instead of a second action.
- Audit export
- Evidence packs with readiness mapping to SOC 2, ISO/IEC 42001, NIST AI RMF, EU AI Act.
Ledger tip · newest last
Synthetic demo data- #8,442,105dcn_e6d812b68506contract-clause-agent · redact_passageblockedm.reyeshash a1b4aa5791…284502, chained to previous hash d35975…9677
- #8,442,106dcn_f0682db79bcacredit-limit-agent · hold_transactionpassj.lindqvisthash 46780bbde6…57f567, chained to previous hash a1b4aa…4502
- #8,442,107dcn_a93150ad0b0bmeter-anomaly-agent · open_work_orderblockedm.reyeshash d48f4211ac…8ae927, chained to previous hash 46780b…f567
- #8,442,108dcn_deee6988ea05fraud-triage-agent · request_kyc_docspassj.lindqvisthash 0f89a924ef…28aa6c, chained to previous hash d48f42…e927
- #8,442,109dcn_f3bd389d8c18foia-redaction-agent · flag_far_clausepassa.okaforhash faaeff3d47…4b9583, chained to previous hash 0f89a9…aa6c
Practice areas
Where we work, and the mechanism behind each.
Six practices, one operating model. Every engagement pulls from several of them, and every one of them writes to the same ledger.
Intelligence
Training, architecture & applied AI
Fine-tuning with DPO and eval-driven loops, model routing on latency and cost curves, and NLP, OCR and ASR pipelines in production.
Quality
AI Six Sigma
DMAIC for model behaviour: golden sets, drift metrics and SPC control charts that block a release when a process leaves its limits.
Build
AI coding & build
Spec-driven, agent-assisted development with human review gates, eval-gated merges and provenance recorded for every generated line.
Cloud
Cloud landing zones
Identity, network, security and AI reference zones on Azure, AWS and GCP, delivered as Bicep, Terraform and CDK you own.
Tools
Tools constellation
The index of foundation models, coding agents, media models and clouds we run in production — and the job each one is trusted with.
Book a Systems Assessment
Two weeks, fixed scope. We map every model, prompt, data flow and decision path you run today, score them against your compliance regime, and hand you a ranked remediation plan with named mechanisms — not a slide deck.
How an engagement runs
Four phases. Each one ends with something you keep.
Fixed scope at the front, measured outcomes at the back. You can stop after any phase and still own a working artifact.
Phase 012 weeks
Assessment
We inventory every model, prompt, data flow and decision path you run, then score each against your compliance regime.
DeliverableSystems map, risk register and a ranked remediation plan.
Phase 023–4 weeks
Architecture
We design the target planes against your identity, network and data boundaries, and agree the evals that define done.
DeliverableReference architecture, eval suite and landing-zone IaC plan.
Phase 038–16 weeks
Forward deployment
Our engineers ship inside your environment, behind your change control: ledger, guardrail plane and the first production workloads.
DeliverableProduction services, runbooks and passing eval gates.
Phase 04Ongoing
Control
Control charts, drift alerts and quarterly audits keep the system inside its limits. Your team owns it; we stay on call.
DeliverableSPC dashboards, audit exports and on-call handover.
Insights
Field notes from production.
Governance
Idempotency Keys Are a Governance Primitive
Retries are where AI systems double-charge, double-send and double-decide. One key per intent makes every action safe to repeat — and provable after the fact.
Lineage
Why Your AI Has No Decision Lineage
Logs record that a model ran. Lineage records what it saw, which policy passed it and who signed off. Most stacks only capture the first.
Context engineering
Context Windows Are a Budget, Not a Bucket
Filling the window is not a retrieval strategy. Allocate tokens by source, measure what each allocation buys, and cut the rest.
Put a ledger under every decision your AI makes.
Start with a two-week Systems Assessment: a map of every model and decision path you run, the gaps against your compliance regime, and a sequenced plan to close them — with a named mechanism for each.