Production LLM systems. Not chatbots.
We build LLM-powered software that goes into production and stays there — structured outputs, validated schemas, observable pipelines, real cost controls.
Most teams ship LLM demos. We ship LLM systems. Lead qualification that classifies prospects into warm / lukewarm / cold from long-form content. Document extraction with audit trails. Decision-support workflows with human-in-the-loop fallbacks. The chat window, where it exists at all, is a fallback — not the product.
What we actually build
- →Content classifiers (warm / cold / qualified / disqualified)
- →Structured extraction from documents and long-form text
- →Decision-support and routing workflows with human-in-the-loop
- →MCP servers exposing business systems to AI assistants
- →Claude Skills that codify domain expertise
- →AI agents that automate multi-step workflows across tools
The reference architecture
How an LLM feature actually ships
Every LLM call we put into production runs through the same scaffolding. The model is one node in a pipeline that mostly looks like normal software.
- 01
Inputs are normalized and bounded
We fetch, clean, and trim inputs to known token budgets. Cost and behavior are predictable before the model ever runs.
- 02
Prompt is assembled deterministically
Rubric, schema definition, and few-shot examples are composed from versioned config — not free text living in an SDK file somewhere.
- 03
Model call with pinned versions
Anthropic Claude with model version pinned and temperature low. Same input produces the same output, modulo controlled stochasticity.
- 04
Output is schema-validated
Pydantic validates every response. Invalid output gets one fix-it retry; still invalid goes to a human review queue. It never silently coerces.
- 05
Audit trail to Postgres
Verdict, reasoning, source excerpts, model version, prompt version, and timestamp are written for every call. Reproducible, defensible.
- 06
Orchestration via n8n
Scheduling, retries, CRM sync, and human-fallback notifications all live in n8n. The LLM is one node in a workflow.
What we explicitly don't do
We get asked about these often. We say no on purpose.
Chatbots as the product
Chat dumps the cognitive load on the user and the reliability burden on the model. We use chat only as a fallback for ambiguous cases.
Fine-tuning for tasks Claude already does well
A well-engineered prompt with few-shot examples almost always beats fine-tuning on cost, iteration speed, and maintainability.
RAG for the sake of RAG
Most "chat-with-your-docs" projects don't need RAG. They need extraction, classification, or a normal search index with a small LLM layer on top.
Free-form text outputs in pipelines
If a downstream system has to parse what the LLM said, the parse will eventually break. Schemas are the contract.
Featured case study
Lead qualification for a B2B SaaS
Schema-validated warm / lukewarm / cold classification from long-form prospect content, with structured reasoning fields and audit logs.
Tech we use here
"An LLM that returns free-form text is a bug surface. We make schemas the contract, validate every output, and route failures to humans. If your AI feature can't be unit tested, it isn't a feature yet."
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