Real leverage, not theatre.

The $1,500 Feasibility Audit

Most AI questions don't need a six-month engagement to answer — they need two weeks of senior attention and an honest write-up. So we've productized the first step. Fixed price, fixed scope, one deliverable you can act on.

What we deliver

The AI market is loud. Most of what's sold as transformation is autocomplete with a markup. Our AI consultancy is for teams that want to find the genuine leverage — and avoid the expensive mistakes of the last two years.

• Discovery & feasibility Two-to-four-week engagements that answer: should you build this with AI at all? What's the right model class? What does production cost actually look like? Output is a written go / no-go you can defend.
• RAG & retrieval pipelines Document ingestion, chunking strategies, embedding selection, hybrid search, re-ranking, and the observability tier that lets you debug a bad answer six months from now.
• Agentic systems Tool-using agents with proper guardrails — structured outputs, retry policies, cost ceilings, escalation paths, and evaluation harnesses. Not demos — production.
• Evaluation & quality Eval suites that match how your users actually behave. LLM-as-judge done carefully, human review where it matters, and regression detection for every prompt change.
• Cost & latency engineering Model routing, prompt compression, caching layers, and request shaping. We've taken AI bills from "this can't ship to production" to "this works at scale" more than once.

How we work

Most AI engagements start with a one-week feasibility study before any code is written. We'll prototype against your real data, measure what's actually possible, and write up the results honestly. If AI is the wrong answer, we'll tell you — and that's where the engagement ends.

For builds, we work in two-week sprints with quantitative evals at every gate. No "the demo looked great" handoffs. The system meets the bar or it doesn't ship.

What we build with

• Foundation models Claude, GPT, Gemini, open-weights (Llama, Mistral, Qwen). We're model-agnostic and will recommend based on capability, cost, latency, and data-residency requirements — not partnerships.
• Frameworks & protocols MCP for tool integration, LangGraph or custom orchestration, structured-output enforcement (JSON schemas, function calling).
• Vector & retrieval Postgres + pgvector, Pinecone, Weaviate, Qdrant. BM25 hybrid search via OpenSearch / Elasticsearch where appropriate.
• Observability & evals Langfuse, Helicone, custom telemetry, ground-truth eval suites, LLM-as-judge with human-rated calibration sets.

What we'll push back on

We will not help you put a chatbot on your homepage because a competitor did. We will not build an agent that sends customer emails without proper review. And we will not pretend that retrieval-augmented generation is a substitute for fixing your data. AI is a powerful component — we'll help you use it where it earns its place.