02 / Approach
We measure before we ship.
Most AI projects fail the same way: a demo that wows in the room and crumbles in production. We build the opposite.
Every engagement starts with evals. Before we write an agent, we agree on what “working” means in numbers — accuracy, latency, cost, and how often it should escalate to a human. Those numbers become the contract for the project.
Then we build against them. Iteration is fast because we can measure. When the model improves, we re-run the evals. When the data shifts, we catch it. When something regresses, we know within minutes.
It’s not glamorous. It’s the difference between a science project and a system you can run a business on.
Why evals first
The hardest part of AI engineering isn’t writing the agent. It’s knowing when the agent is wrong.
We define success up front: precision and recall targets for classification, rubrics for generation, replay harnesses for tool-use agents. Those suites run in CI for the life of the project. Refactors become safe. Model upgrades become measurable. Prompt changes land with a pass or a fail, not a gut check.
That’s the environment you need to keep improving a system after launch — not just ship one.
Why Claude
We’re Claude-first by conviction. Opus handles reasoning-heavy work with the consistency that matters at the edge cases. Sonnet handles production throughput at a cost structure that pencils out. Claude Code handles our own engineering workflow.
Depth beats breadth for production reliability. We’d rather know one stack well than abstract across five and know none of them. When Anthropic ships a new capability, we already understand how to use it.
We’re not religious about it. If your use case has hard requirements Claude doesn’t meet, we’ll tell you. We just haven’t hit that case yet.
How we build production-grade agents
Customer-facing agents live or die on the details most demos skip:
- Tool use and orchestration Designing the right tools, the right descriptions, and the right handoffs between agents.
- Context management Keeping agents coherent across long documents, multi-turn conversations, and multi-step workflows.
- Structured output JSON schemas, extraction patterns, and validation so downstream systems can actually consume what the agent produces.
- Human-in-the-loop Explicit escalation criteria for the cases that shouldn’t be automated.
- Observability Tracing, cost tracking, and eval regressions surfaced before your customers see them.
This is the work between a prototype and a production system. It’s most of what we do.
How a typical engagement works
Most engagements run six weeks from kickoff to a production agent.
Discovery
Two weeks of stakeholder interviews and workflow audit. We learn your system before we touch it.
Evals
The test harness comes before the agent. "Working" gets defined in numbers, not intuition.
Build
Iterative sprints against eval targets. Every change is measured. Regressions surface immediately.
Ship
Deployment with tracing, cost dashboards, and a runbook. You inherit a system you can operate.
Every sprint ends with a demo backed by the latest eval run. You always know where you are against the targets you approved.
What you own at the end
Everything. The code is in your repo from day one. No proprietary framework, no hosted black box, no monthly fee that keeps the lights on.
We hand off with a runbook, a deployment guide, and an eval suite your team can run themselves. If something breaks in production at 2am six months later, your engineers can diagnose it without us. We write the documentation to make that true.
No lock-in isn’t a feature we advertise. It’s a constraint we impose on ourselves — and it forces us to write code that’s actually maintainable.
Principles
- CLAUDE-FIRST MODEL LAYER
- EVALS BEFORE PRODUCTION
- SHIP IN WEEKS, NOT QUARTERS
- OWN THE CODE, NOT JUST THE OUTPUT
- MEASURE COST ALONGSIDE ACCURACY
- HUMAN-IN-THE-LOOP BY DEFAULT
“Before we write an agent, we agree on what ‘working’ means in numbers. Those numbers become the contract.”
Ready to build something that actually works in production?
We start every engagement with a two-week discovery sprint — stakeholder interviews, workflow audit, and a prioritized roadmap. No retainer required. You walk away with a spec whether you build with us or not.
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