AI Strategy & Prototyping

We de-risk your AI bet with rigorous, evidence-first prototyping and the engineering disciplines that make AI behaviour knowable — guardrail and eval engineering, and a capability-gated test harness that runs against real providers, not mocks. The buyer's first question is "how do you know it works?" — we answer with code, not a deck.

Coming into form the per-provider capability matrix — what we test, per provider — as the rigor proof

Outcomes

A fast, runnable prototype that de-risks an agentic or LLM bet before a big build
A way to KNOW the AI behaves — capability-aware test suites that run against the real model, not a mock
Guardrail and eval engineering — input and output guards and deterministic policy enforcement designed into the system
A clear go / no-go on the full build, grounded in a runnable artifact you can see work

How it works

The riskiest moment in an AI project is the gap between a demo that looks good and a system you can trust in production. We close that gap two ways: a fast, runnable prototype that proves the value before you commit to a full build, and the engineering disciplines that make AI behaviour knowable rather than hoped-for.

The prototype is a working artifact, not a slide — the agent loop, the toolset, the orchestration, running live on our own stack, so you get a real go/no-go grounded in something you can see work. The rigor is the harder half, and it is where the buyer's "how do you know it works?" gets a real answer. We build capability-aware test suites that run the same behavioral test against a deterministic fake and the real provider SDKs, gated on a per-provider capability matrix — so a caching test runs where caching actually exists and self-documents why it skipped where it does not. The tests assert on real token and usage metrics, against the real model, not a mock that proves nothing.

We build it through the D2D Line — FRAME the bet and what "proven" means, DESIGN the prototype and the test strategy, BUILD it, VERIFY it against real providers, and SHIP it to you.

What we are, and what we are not

This is eval and guardrail engineering — test infrastructure and deterministic policy enforcement built into your system. It is not a productized evals platform: there is no LLM-as-judge scoring engine, no eval dashboards or leaderboards, no run-tracking UI. We engineer rigorous AI testing into your build; we do not ship an evals product, and we will not say we do. The guardrails are deterministic, rule-based guards, not ML classifiers.

What you own at the end

The prototype, the test suites, the capability matrix, and the guard policy are yours — source and all. The rigor stays with the codebase after we leave, so your team keeps knowing the AI behaves, run after run.

FAQ

Is the prototype throwaway? Not necessarily. It is built on the same stack the full engagement uses, so a proven prototype is a head start on the build rather than a discard.

What does "test against real providers" mean in practice? The behavioral tests run against the live model SDKs and assert on real usage — the discipline is "test the real thing." Where a provider lacks a feature, the matrix gates the test out and records why, so the suite stays honest across providers.

How is it priced? Fixed-scope, fixed-fee — a bounded prototype sprint or an eval-engineering engagement, pinned in the FRAME phase before any build starts.

Proof

A capability-gated test harness — a per-provider capability matrix, attribute-driven gating, and real-provider integration against the live Anthropic, OpenAI, and Google SDKs, asserting on real token and usage metrics; the suites run green.
A native guardrails framework with a pluggable extension point and a three-state pass / block / escalate model — the substance behind 'we engineer safety into your agents, not bolt on a vendor filter.'
Runnable prototypes that stand up fast — an agentic-coding demo with a multi-provider CLI, and an orchestration fan-out demo — to de-risk an agentic build live.