Nelfet.
how we work
How we take AI-native software from first principles to production — and keep it reliable once it's there.
delivery model
Each cycle is measured and observed, so the next one is better. The loop is the same whether we're building a product or an agent.
Frame the real decision the software must support — not the surface request.
Shape architecture and AI strategy together: model vs. deterministic, retrieval, safe failure.
Small, observable increments. Each independently deployable, rollback carries no collateral damage.
AI features ship with curated eval sets and automated scoring. Regressions caught before users.
Feature flags and gradual rollout. Rollback is one step, not a crisis procedure.
Traces, latency, cost, and quality scores wired in from day one.
engineering standards
Versioned eval sets with automated CI scoring. A regression blocks the release — no exceptions.
Structured logs, traces, and per-request model costs instrumented before first deployment, not retrofitted.
Least-privilege, short-lived credentials, managed secrets, prompt-injection validation — baked in, not bolted on.
Strict TypeScript with runtime schema validation at external boundaries eliminates whole categories of integration bugs.
Every change runs type checks, unit tests, integration tests, and AI evals before merge.
Feature flags, gradual rollout, one-step rollback. Risk surfaces in a fraction of traffic first.
principles
AI shapes design, implementation, and feedback loops — not a feature bolted on post-launch.
We build for edge cases, graceful degradation, and maintainability — not prepared walkthroughs.
Engineers reason about latency, cost, failure modes, and evals — not just the API surface.
Trade-offs made explicit: cost vs. accuracy, speed vs. safety, build vs. integrate. Risks named early.
tools
We don't sell a fixed stack. We choose the tools that fit your problem, your team, and what you already run — boring where it should be, modern where it matters.
Tell us what you're building and how we can help.