AI / ML
AI is a data problem first.
RAG systems, vector search, conversational analytics, and the pipelines behind each of them.
We build the foundation before the model. The model is the easy part.
AI / ML · live signal
What this engagement looks like by the numbers
0+
AI features in prod
<0
ms p95 latency
0%
eval coverage
Problems we solve
If any of these sound familiar, we can help.
What we build
The deliverables.
RAG systems
Retrieval-augmented generation architectures that ground LLMs in your company's data.
Conversational analytics
Natural language interfaces to your warehouse. Ask in English, get real numbers.
Vector search
Semantic search across documents, tickets, and feedback — anything unstructured.
LLM-powered workflows
Automation that extracts, classifies, summarizes, or acts on data.
Engagement models
Three ways to work together.
01
Proof of concept
4-6 week sprint to ship one production AI use case.
Cadence
- Week 1Use-case + data readiness
- Week 2-3Data prep + retrieval
- Week 4-5Build + evals
- Week 6Demo + handoff
- Internal RAG chatbot
- Support ticket triage
- Document extraction pipeline
02
Full build
End-to-end AI product with observability, monitoring, and governance.
Cadence
- Month 1Foundation + data layer
- Month 2Agent / tool harness
- Month 3Evals + observability
- OngoingSafe rollout + monitoring
- Customer-facing AI features
- Internal agent workflows
- Multi-model routing
03
Advisory
AI architecture, tooling, and model selection.
Cadence
- As neededArchitecture deep-dives
- MonthlyModel + tooling reviews
- OngoingEval framework guidance
- QuarterlyStrategy refresh
- LangGraph vs. custom orchestration
- Vector DB selection
- Eval frameworks
Tools we use
Fivetran
Sigma