Approach
We're
AI-first
— not because it's the trend, but because the math has changed.
A custom internal tool that took a quarter to build now takes a week. The interesting question isn't whether to use AI. It's what's now worth building that wasn't worth building before.
The thesis
AI has collapsed the cost of a wide class of tasks. The work isn't to chase the model of the week — it's to figure out what's newly possible, and ship it.
The phrase "AI-first" gets used loosely. We mean something specific: when we scope work, we start by asking which parts of it used to be expensive because they required scarce human attention — and which of those parts no longer do.
That's where the leverage is. A dashboard that needed a frontend specialist now takes an engineer and a good prompt. A research process that needed three analysts can run on one operator plus a properly scoped agent. A first draft of almost anything — copy, design, code, plans — is no longer the bottleneck.
What's left after that is judgment, taste, and accountability — and those are the things we lean into hard.
How we work
A loop, not a waterfall. Each step informs the last.
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Discover
Discover
We start by understanding what you're actually trying to do. What's the underlying need, what constraints are real, what does success look like. Most projects find their shape here.
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Prototype
Prototype, fast
We build something rough and runnable inside the first week or two. With AI in the loop, prototypes are cheap — so we use them to learn instead of to demo.
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Validate
Validate
Real users, real data, or real benchmarks — whichever applies. We'd rather find out something's wrong in week two than in month four.
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Build
Build
Production code, real infrastructure, real tests. The prototype gets rebuilt or hardened — whichever serves the work better.
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Iterate
Iterate
Software is never done. We hand off cleanly, and we stay available to keep improving the thing — or hand it fully to your team, if that's the plan.
What AI accelerates · what stays human
The line moves over time. We re-draw it on every project.
Accelerated by AI
What we lean on the model for
- First drafts of almost anything — copy, code, designs, plans.
- Code generation for the well-trodden parts of a project.
- Document parsing, summarization, and classification at scale.
- Boilerplate, repetitive refactors, and the long tail of "obvious" work.
- Exploratory analysis where the question is still being shaped.
Stays human
What we don't outsource
- Judgment about what to build and what to skip.
- Taste — for design, copy, code quality, and product feel.
- Accountability for the result, especially when it goes wrong.
- Trust with your team and your customers.
- The last 10% — the part that decides whether something feels right.
What we're tracking
Refreshed regularly. If we're recommending it, we're using it.
Models
Frontier reasoning models
Claude, GPT, and Gemini at the top of the curve — plus open-weight options when latency, privacy, or cost rule the cloud out.
Agents
Tool-using agents
Long-running agents that browse, write code, run shells, and call APIs. We've shipped several into production. We know what breaks.
Coding
AI in the editor
Claude Code, Cursor, and similar — embedded directly in the build loop. Engineering speed has roughly tripled in the work we measure.
Retrieval
Grounded answers
RAG and structured retrieval over real customer data, with the evals to know when it's working and when it's confabulating.
Evals
Measuring what we ship
We don't trust vibes. Every AI feature gets a small evaluation suite, run on every change, before anything goes to production.
Deployment
Boring infrastructure
We pair leading-edge models with infrastructure that's intentionally unfashionable. Boring stacks are easier to keep running.