Start here
Discovery Sprint
We clarify the problem, users, architecture and risks, and agree what the first release must do.
- Scope
- Fixed, about 2 weeks
- You get
- Scope, architecture, plan and a fixed price per milestone
02 · Engineer
Engineer at AI speed.
Senior engineers own the architecture, business logic, security and quality. AI accelerates the code, tests, documentation and refactoring. We build enterprise applications, products and intelligent workflows, and choose rules, models or agents by what the problem actually needs.
What we're usually asked to fix.
Leaders rarely need more developers. They need working capability, sooner, that they can trust in production.
The roadmap outruns the team
Priorities keep growing, hiring is slow, and the backlog keeps getting longer.
AI pilots that never ship
Impressive demos, then stalled on data access, security review, cost or accuracy.
Speed bought with technical debt
Fast releases that became slow ones, because quality and design were skipped.
Work still runs on email and spreadsheets
Approvals, handoffs and document processing that should have been automated years ago.
AI costs nobody can predict
Large-model bills and vendor lock-in for tasks a smaller model or plain code could do.
Vendors who need managing
Teams that wait for instructions, rather than owning the outcome and pushing back on bad ideas.
What we build.
Three kinds of work, often in the same program.
Applications and products
Web, mobile and cloud-native software for your business or your customers.
- Enterprise applications and portals
- SaaS products and new product lines
- Web and mobile (iOS and Android)
- APIs, integrations and DevOps
Enterprise AI
AI built to reach production, engineered like any other critical system.
- Assistants and knowledge search over your content
- Retrieval with sources (RAG)
- Copilots and controlled agents that act through your APIs
- Smaller and private models where they fit
Workflow automation
Remove manual steps from the processes that slow your teams down.
- Approvals, routing and case handling
- Document intake and data extraction
- Rules where rules are enough
- Human review where judgment is needed
How we engineer
Senior judgment decides. AI does the heavy lifting.
Our delivery model was designed around AI, not retrofitted with it. The split of responsibility is explicit, and it doesn't change under deadline pressure.
AI on the keyboard
- Scaffolding and implementation
- Test generation
- Documentation
- Refactoring and code analysis
- Repetitive integration work
Architects in charge
- Architecture and data model
- Business rules and edge cases
- Security and privacy
- Review and merge of every change
- Production readiness
The simplest thing that works.
Not every problem needs AI, and few need the biggest model. We choose by business value, accuracy, latency, privacy, maintainability and total cost of ownership.
- Conventional software
When the logic is known and stable. Cheapest to run, easiest to test.
- Rules and automation
When decisions follow policy. Transparent and auditable.
- Commercial models
When you need broad language ability quickly, with the right data controls.
- Retrieval (RAG)
When answers must come from your own content, with sources.
- Smaller and private models
When cost, privacy or latency matter more than breadth. Can run in your own cloud.
- Controlled agents
When the task needs judgment and action, with permissions, approvals and audit trails.
Quality gates on every change.
AI makes code cheap to write. It doesn't make it correct. These gates apply to every change, whoever or whatever wrote it.
- Architecture review before build starts, and when the design changes
- Senior review of every change before it merges
- Automated tests in the pipeline, including tests generated with AI and reviewed by engineers
- Security scanning for dependencies, secrets and common vulnerabilities
- AI evaluations for accuracy, safety and cost before any AI feature ships
- Monitoring and rollback in place before go-live
How we measure engineering.
Against a baseline agreed at the start, so you can see what changed. We don't count lines of AI-generated code.
Lead time
From approved work to running in production.
Quality
Rework, defects that reach production, and security findings.
Reliability
How often changes fail, and how fast we recover.
Cost
Cost per delivered capability, not cost per hour.
Delivery evidence.
A product we took from idea to launch, and software we build and run ourselves.
Product story · health and wellness
From an idea to an AI-powered app in 16 weeks
A founder with a mission and no engineering team. We ran discovery, designed the experience, built an AI-enabled web and mobile app with a coach grounded in the founder's method, and stayed on to improve it.
Read the storyWays to engage.
Fixed and outcome-based where scope can be defined, and a senior-led pod for ongoing roadmaps.
Then
Milestone delivery
We build against agreed milestones, with a working build to try every two weeks.
- Priced
- Fixed price per milestone
- You own
- The code, designs and IP we create for you
For ongoing roadmaps
Senior-led pod
A small pod led by a named architect, working inside your roadmap, tools and rituals.
- Priced
- Monthly
- Measured
- Lead time, quality and cost per capability
Questions leaders ask about AI-assisted engineering.
What comes up when a CTO or VP evaluates our model.
Is AI-written code safe to put in production?
Only after it passes the same gates as any other code: senior review, automated tests, security scanning and, for AI features, evaluations. Engineers stay accountable for every line that merges.
Does AI-assisted delivery mean lower prices?
It means more working capability for the money. Where scope is defined, we price per milestone, so you benefit when we're faster. Ongoing pods are priced monthly and reported against lead time, quality and cost, measured from an agreed baseline.
Which AI models and tools do you use?
It depends on your data, security and cost requirements. We work with commercial models, open models and smaller private models, and we can run within your cloud. We don't lock you into one vendor.
Will you use AI tools on our code?
We follow your AI policy. Where you require it, we use only the enterprise AI tools you approve, or none at all.
Who owns the IP?
You own the code, designs, documentation and other deliverables we create for you, including prompts and evaluation sets written for your project, once they're paid for. Our pre-existing tools and products stay ours, and you get a perpetual license to any part of them built into your deliverables. The contract sets out the details.
Can you work alongside our own engineers?
Yes. Many engagements are mixed teams. We follow your repositories, tools and release process, and leave documentation and tests your team can maintain.
What if requirements change?
They usually do. Each milestone is re-planned with you, and the working build every two weeks makes changes visible early, when they're cheapest.
Start with a two-week Discovery Sprint.
Clarify what to build, how, and what it will cost, with a senior architect, before you commit to delivery.
- The problem, users and outcomes, agreed in writing
- Architecture and technology choices, including where AI fits
- Risks, dependencies and data access needs
- A milestone plan with a fixed price per milestone
- A clickable prototype where it helps