AI Product Engineering
Ship AI products, not proofs of concept.
We design, build, and operationalize custom AI systems for enterprise teams — private LLMs, RAG, document intelligence, and industry copilots that run in production with audit trails and SLAs.
— The problem
Sound familiar?
- 01Your AI pilot worked in a notebook but never made it to production.
- 02Off-the-shelf copilots miss your industry vocabulary and data model.
- 03Public LLMs are a non-starter for your data residency or compliance boundary.
— What we deliver
Concrete outputs. Nothing hand-wavy.
Use case scoping, success criteria, and evaluation benchmarks.
Model selection — Claude on Bedrock, Llama/Mistral self-hosted, or fine-tuned OSS.
RAG pipeline on your corpus with chunking, reranking, and citation.
Domain-specific copilots and agent workflows.
Production deployment inside your AWS/Azure/GCP or on-prem.
MLOps handover — eval harness, model versioning, drift monitoring, runbooks.
— Methodology
How we run the engagement.
Phase 1
Discover
Use-case scoping, data access, success metrics, eval design.
Phase 2
Design
Model + retrieval architecture, UI contract, security boundary.
Phase 3
Build
Ingest, index, integrate, test against eval harness.
Phase 4
Operate
Production deploy, monitoring, retrain cadence, handover.
— Stack we work in
Opinionated but pragmatic.
We're deepest on AWS and Claude/Bedrock. We also ship on Azure, GCP, and open-source where they're the right fit.
Models
- Claude on Bedrock
- Llama 3 / Mistral self-hosted
- Fine-tuned OSS
Retrieval
- OpenSearch
- pgvector
- Pinecone
- custom hybrid
Frameworks
- LangGraph
- LlamaIndex
- custom agent runtimes
Eval
- Ragas
- DeepEval
- domain-specific harnesses
— Where we apply it
Industries we've built patterns for.
— FAQ
Frequently asked.
Get started
Ready to scope your AI Product Engineering engagement?
Book 30 minutes with our team — we'll tell you honestly whether we're the right fit.