AI that ships, measures, and earns its keep.
We build production AI for teams who are tired of demos. RAG assistants over your real data, agentic workflows wired into your stack, and evaluation pipelines that prove the model is actually getting better.
Real AI work, scoped to ship — not to demo.
We treat AI like software: requirements, evaluations, monitoring, rollback. The output is something your team can rely on, not a screenshot for the board.
RAG copilots & assistants
Domain-specific assistants over your docs, tickets and CRM data. Hybrid search, reranking, citations, fallback paths and audit logs in every response.
Agentic workflows
Multi-step agents that read your APIs, write to your systems, and ask a human when confidence drops. Built on patterns we know hold up under load.
Salesforce Agentforce & Einstein
Agentforce agents, Einstein Copilot, prompt templates and Apex actions — scoped tightly with sandboxed test data so the model stays inside the lines.
Data & retrieval pipelines
Chunking, embeddings, vector stores (pgvector, Pinecone, Weaviate), hybrid keyword retrieval, freshness windows, and ACL-aware filters.
Evaluations & guardrails
Golden datasets, LLM-as-judge harnesses, red-team suites and regression tests. Every prompt change ships behind a measured improvement.
Inference economics
Caching, prompt compression, smaller-model routing, batching. We make AI cheap to run before we make it impressive in the demo.
Prompt & policy engineering
Documented prompts, versioned in git, with style guides for tone, refusals and persona — so a marketing edit does not break production.
Safety, privacy & compliance
PII redaction, retention controls, audit trails, and a clear stance on training: we never train models on your data, full stop.
A pragmatic path from idea to measured pilot.
Use-case scoping
Two days of discovery. We rank candidate use-cases by ROI, data readiness and risk, then pick one with a clear success metric. The rest goes on a roadmap, not into the pilot.
Eval-first build
We start by writing the test set: 50-200 prompts your team agrees represent the real workload. The model only ships when it beats the prior baseline on that set.
Production pilot
Limited rollout to a small user cohort. Full observability, human-in-the-loop where stakes are high, and a kill-switch your team controls.
Scale & operate
Once the metrics hold, we widen access, harden the pipeline, and hand off the eval harness so your team can keep improving the system without us.
Senior people. Honest scope. Software you can run on.
Evals before ego
We refuse to ship AI without an eval harness. If the model gets worse, you will see it before your customers do.
Senior, not pretending
Your engagement is staffed by engineers with real production AI on their CV — not bootcamp graduates riding the hype curve.
Your data, your data
NDAs upfront, least-privilege access, no training on your tenants. We will sign your DPA and we will read it.
Questions we hear often.
Have an AI use-case worth doing right?
Book a 45-minute call and tell us the workflow. We will tell you honestly whether AI helps, what it would cost to ship, and what the success metric should look like.