We build AI software for teams that have to answer to customers, boards and auditors. Opinionated process, defensible metrics, senior engineers on every engagement.
Production with
Delivery assurance
Each section is self-contained and written for a different reader — builders, buyers, and boards.
LLM integrations, retrieval-augmented generation, multimodal pipelines, and autonomous agents — engineered for production workloads, not demos.
See capabilities02 · Business caseA practical view of where AI changes the unit economics of software delivery — and where it doesn't. Clear, measured expectations for boards and execs.
Review the ROI03 · ComparisonHow AI-native delivery differs from traditional engagements across six operational dimensions — and why the gap compounds quarter over quarter.
Compare dimensionsNumbers we put in the statement of work, not in a brochure. Each has a stated scope and a way to verify it.
3–5×
Throughput uplift
Measured on greenfield feature work over 4 engagements (2024).
≥ 90%
PR review coverage
Automated static, security and policy checks on every commit.
p95 < 2.0s
RAG answer latency
Budget we design to. Measured end-to-end at the API edge.
99.9%
Uptime target
Contracted on managed platforms; observability shipped on day one.
Not a plugin. A six-phase delivery process built around AI, with senior engineers owning every gate.
We use AI to accelerate the early phases of system design — not to replace the judgement call. A senior engineer still owns every boundary, contract and data model.
Claude Code and Cursor run alongside every engineer as a paired agent. Scaffolding, migrations and repetitive logic get generated under strict style and security guardrails.
Unit, integration and eval suites are written alongside the feature. Regression and drift are caught before merge, not after the customer escalates.
Every pull request is scanned for OWASP top-10, secret leakage and licence issues. AI surfaces risks; senior engineers triage. Nothing merges without human sign-off.
APIs, READMEs and architectural decision records are generated from source, reviewed by humans, and kept in lockstep with the code as it evolves.
Model calls, tokens and latency are measured per-tenant from day one. Budgets, rate-limits and fallbacks are implemented before we go live — not after the first bill.
Repeatable, de-risked, and written down. Every stage has an artefact, an eval target, and a named owner.
We map the workflow, inspect the data, and identify the highest-ROI AI opportunity before writing a line of code. You get a one-page scope with an eval target attached.
A live prototype on real data — not slideware. Covers the riskiest assumption first so the business case is testable inside a fortnight.
Guardrails, rate limiting, caching, retries, fallbacks, observability and security review. Everything that separates a demo from a system customers can depend on.
Every AI surface ships with an eval harness. Senior engineers review AI output on a sampling basis. Failure modes are tracked as first-class tickets.
Usage, cost and quality telemetry flow into weekly reviews. We improve prompts, retrieval, and models against the eval score — not against vibes.
Send us the workflow, the data and the question you're trying to answer. We'll come back with a one-page scope, an eval target, and a costed two-week prototype.
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