Purpose-Built Software · AI-native SDLC
Built for your edge cases. Shipped at AI speed.
Custom software, AI agents, and integrations — engineered with an AI-native development lifecycle. Software that bends to your domain instead of forcing your domain to bend to off-the-shelf SaaS.
What we build
- Custom web & mobile applications
- AI agents & multi-agent systems
- Domain integrations & data pipelines
- Internal tools your team won't fight
How we build (AI SDLC)
- Spec-driven, AI-augmented development
- AI pair-programming & review loops
- Generated tests, property-based coverage
- Continuous evals, not just CI
When off-the-shelf isn't enough
SaaS is built for the average customer. Your edge cases are exactly what gives your business its edge. We build the software those edges actually deserve.
"We pay six SaaS vendors to roughly do what one purpose-built tool could do well."
Consolidation play. Often pays for itself in 12–18 months on licence savings alone.
"Our workflow lives in spreadsheets and Slack because no SaaS fits it."
Bespoke internal tool. Where your operational moat lives, your tooling should match.
"Our AI agent prototype works in a notebook — we need it in production."
Agent productisation. Eval harness, guardrails, observability, deployment — the unglamorous 80%.
"We're integrating five upstream systems and it's a mess."
Domain integration layer. We design contracts, write adapters, and own the data shape.
AI-native software development lifecycle
AI changes more than what we ship — it changes how we ship. Our SDLC treats AI as a first-class teammate across every loop: spec, build, test, review, deploy, evaluate. Not a sidebar.
Spec
Outside-in & AI-readable
Specs are written for both engineers and the AI agents that will help build them. Acceptance criteria are executable; ambiguity gets caught at the spec layer, not the bug layer.
Build
AI pair-programming
Engineers drive; AI agents pair. We use the agent layer for boilerplate, refactors, and scaffolding — and reserve human judgement for design and trade-offs.
Test
Generated & property-based
AI generates the obvious test cases. Engineers write the property-based and chaos tests. Mutation testing validates the suite is actually catching regressions.
Review
Multi-agent code review
Specialist agents (security, performance, accessibility, lint) review every PR before a human does. Humans review the trade-offs, not the typos.
Deploy
Trunk-based, feature-flagged
Small, reversible changes. Feature flags wired in from spec time. Rollback is a click, not a deploy.
Evaluate
Continuous evals, not just CI
For AI-touching code, CI passing isn't enough. We run eval harnesses on golden sets every commit, so regressions in behaviour get caught alongside regressions in code.
Stack-agnostic. Opinion-rich.
We pick the right tool for the job, not the tool we've memorised. Our principles travel; our stack adapts.
Languages
TypeScript / Python / Elixir / Rust / Go — selected by the problem's shape, not the team's habit.
AI / LLM
OpenAI, Anthropic, Bedrock, Vertex, open-weight via Groq/Cerebras. BYOK by default; provider-agnostic where it matters.
Data
Postgres + pgvectorscale for the boring 90%, specialist stores where workloads justify the operational tax.
Cloud
AWS, GCP, Azure, Cloudflare — primary cloud chosen for compliance posture and data residency, not loyalty.
What you walk away with
- Software in production — not a prototype, not a slide deck.
- An eval harness — for any AI-touching feature, so quality is measured, not guessed.
- Infrastructure-as-code — for everything we deploy. Reproducible from a fresh AWS account.
- Runbooks and on-call documentation — your team can actually act on at 3am.
- Knowledge transfer — your engineers can extend what we built without us in the room.
- An honest hand-off — including what we'd do differently if we started over today.