Senior engineers integrating against the model API the same way we integrate against Postgres. No LangChain, no LlamaIndex, no agent framework that needs a migration every six months. Eight weeks from problem to live, eval suite included, full source ownership at handoff.
These three rules are why our LLM builds survive their first month in production. Skip any one and you’ve shipped AI slop on a timer.
We call the model the same way we call Postgres. No wrapper SDK, no agent framework, no graph DSL. If you can read the OpenAI cookbook, you can read our code.
We write a golden eval set before we ship a prompt. Every regression closes the gate. Production traffic feeds the next eval cycle — that's the Compound Growth Loop.
Postgres, pgvector, Cloudflare Workers. Tools your team already runs, your ops team already monitors, your CTO already approved. No new vendors to onboard.
Three pillars, sixteen layers, one feedback loop. The discipline that separates a system that survives production from a demo that dies in week three. Foundations · Amplification · Judgment — applied to every prompt, every retrieval, every eval gate.
Read the K-Framework
Each service below is a focused eight-week sprint — fixed scope, eval suite at handoff. Bundle two or three if the problem warrants, or sequence them as a multi-phase program for regulated builds.
Production LLM that passes legal, security, and procurement on the same go-live date.
Run your own weights in your own VPC. Latency, cost, and compliance — under your control.
A golden eval suite before a single prompt ships. Regressions close the gate, not the user.
LoRA, DPO, or full fine-tune. We pick based on data volume, not vendor pitch.
Hybrid retrieval on Postgres + pgvector. No separate vector DB, no five-system synchronisation problem.
Function-calling agents with hard guardrails. Crossreferences /ai-agents — same lab, deeper engineering view.
Token telemetry, drift detection, cost-per-conversation dashboards. What gets measured gets shipped.
JSON schema enforcement, validator loops, repair prompts. LLM as a structured component, not a chatbot.
TypeScript test runner with golden sets, hard assertions, and LLM-as-judge soft scoring. Runs locally, in CI, and in production against live traffic.
Cloudflare Worker in front of every model call. Vendor abstraction, retry/fallback, cost caps, PII redaction, and OpenTelemetry traces.
Postgres + pgvector with hybrid BM25 + dense search. Chunking pipeline tuned to your corpus. Citation surface in every answer.
Per-user, per-endpoint, per-prompt-version token + cost rollups. Drift alerts, daily anomaly reports, per-tenant caps.
Versioned prompts with eval results attached. Deploy a prompt the way you deploy a service — review, test, ship, rollback.
Twenty pages, paper-printed at handoff. How to debug, what to monitor, when to call us. Your team owns operations from day one.
Same five-step cadence on every engagement. Aligned to the K-Framework loop — Build, Measure, Reflect, Improve.
Two-day workshop. We map the use case to the K-Framework, write the golden eval set with you, and decide direct-API vs RAG vs fine-tune. Output: a one-page engagement contract.
Inference proxy, retrieval layer if needed, eval harness, observability. The boring infra goes first so the interesting work has a place to land.
Daily eval runs against the golden set. Hard assertions close the gate. We tune chunking, prompt structure, model choice — measured, not guessed.
Cost caps, PII redaction, schema validation, rate limits per tool. Drift-detection pipeline against production traffic. Runbook drafted alongside the engineering.
Code review with your team. Printed runbook. Eval suite walkthrough. 90 days of warranty support. After that, you own everything — including the right to extend it without us.
