AI-accelerated delivery
AI in the loop on every engagement — not as a sales line, as a delivery practice. We use coding agents, evals, and structured prompting to ship a quarter's roadmap in six weeks. Same code review. Same tests. Half the calendar.
Most “AI consulting” is a slide deck and a Notion template. We treat AI as an engineering primitive: agents reviewing PRs, evals replacing flaky manual QA, structured prompts producing schemas, and humans in the loop where the loop earns it. The output is shipped product, not a strategy memo.
A working AI-flow practice inside your team — agents, prompts, evals, runbooks.
Concrete delivery: a feature, migration, or audit shipped using the practice.
Cursor / Claude Code workflows tuned for your repo, your conventions, your reviewers.
An eval harness for every prompt that ships to production.
Cost guardrails: spend caps, model routing, fallback chains, telemetry.
Documentation your engineers can keep operating after we leave.
Documentation your engineers can keep operating after we leave.
Inside the engagement.
- 01
Coding agents
Claude Code, Cursor agents, OpenAI Codex — wired into your repo with the right tools, the right context, and the right humans on PR review. Not vibes; a configured workflow.
- 02
Eval-first features
Every LLM-backed feature ships with an eval suite. We seed it from real customer transcripts, run it on every change, and treat regressions like test failures.
- 03
Structured outputs
JSON Schema, Zod, function-calling, constrained generation. The model returns data, not prose. Your code consumes it like any other API.
- 04
RAG that earns its keep
Embeddings on the data that warrants them, retrieval evaluated against ground truth, hybrid search where it helps. Most projects don’t need a vector DB — we’ll tell you which.
- 05
Cost & latency
Model routing (cheap → smart), caching, streaming, parallel calls. We hit your latency budget and your cost-per-request budget before we ship.
- 06
Guardrails & evals
Prompt-injection tests, jailbreak suites, output filters, redaction. The boring stuff that keeps the feature in production past launch week.
The AI-flow toolchain.
Models, agents, evals, and infra. Opinionated about evals; pragmatic about everything else.
- + MODELSClaude (Anthropic)GPT-4 / 5GeminiLlama
- + AGENTSClaude CodeCursorCodexLangGraph
- + EVALSBraintrustPromptfooInspectCustom
- + INFRAVercel AI SDKOpenRouterPineconePostgres + pgvector
From kickoff to handover.
- 01
Use-case scoping
We sit with your team and look at three or four candidate use cases. We score them on impact, evaluability, and delivery risk. You leave with one we’ll ship and two we won’t.
Use-case scorecardEval seed setRisk registerWEEK 1 - 02
Eval harness
We build the eval suite before the feature. Real transcripts, ground truth, pass/fail criteria signed off by you.
Eval harness in CIGround-truth setPass criteriaWEEK 2 - 03
Build + iterate
Daily eval runs. Every prompt change is a PR. Cost and latency tracked alongside accuracy from day one.
Working featureCost dashboardDaily eval runsWEEK 3–8 - 04
Ship + train
Ramped rollout behind a flag. Your engineers run the eval harness, edit prompts, and ship without us by end of the engagement. We hand over the runbook.
Ramped rolloutEngineer trainingRunbook30-day supportWEEK 9–10
No. Cursor and Claude Code are tools we use, but the practice is bigger: eval-first development, structured outputs, model routing, guardrails, cost telemetry. The tools change every six months; the practice doesn't.
Two ways. We baseline a comparable feature your team shipped without AI flow, and we report delivery time + defect rate against that baseline. Numbers in the engagement summary.
Agreed — unsupervised AI code is bad. Reviewed AI code, written against an eval harness, with senior engineers on every PR, is the same quality as good human code. The eval suite is the floor.
Yes. We can run on Anthropic / OpenAI / Google enterprise tiers (no training on your data, BAAs available), or fully on-prem with open models if your compliance posture requires it.
We size the cost-per-request before we ship and route to cheaper models where quality holds. Most production deployments land in the cents-per-request range; we'll tell you in week one if yours can't.
Yes — and most do. AI flow runs as a practice on top of Desktop, Migration, and SaaS engineering engagements. Standalone is also fine if you have a specific use case to ship.
Tell us what you’re building.
Two paragraphs is enough. We’ll come back with a one-page fee letter inside four working days — or point you to a studio that’s a better fit. No qualification calls, no discovery decks.