AI Data Engineering & Generative AI Analytics
AI-ready data platforms, RAG pipelines, and analytics copilots in production.
AI projects fail on data, not on models. We build the engineering layer that makes AI work: ingesting unstructured content, chunking and embedding it, keeping it fresh, and exposing clean, well-described semantics that an LLM can actually query.
On top of that we ship the applications — retrieval-augmented chat over your knowledge base, natural-language-to-SQL over your warehouse, document extraction, and analytics copilots — with evaluation, guardrails, observability, and a cost ceiling. We build things that survive contact with real users, not demos.
What we do
- AI-ready data platforms: unstructured ingestion, chunking, embeddings
- Vector search and hybrid retrieval (Databricks Vector Search, pgvector, Snowflake Cortex)
- RAG applications over documents, tickets, and knowledge bases
- Natural-language-to-SQL and analytics copilots over your warehouse
- Semantic layers and metric definitions that make LLM answers correct
- LLM evaluation sets, guardrails, and prompt/version management
- Agent pipelines for data quality, documentation, and ops
- Deployment, monitoring, and token cost control
What you get
- An AI-ready data layer with refresh pipelines and lineage
- A deployed, evaluated GenAI application
- An evaluation suite so quality changes are measurable
- Guardrails, monitoring, and a token cost budget
- Architecture docs and handover to your team
How we work
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01
Frame
Pick use cases with real value and define success metrics.
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02
Prepare
Build the AI-ready data layer: ingestion, embeddings, semantics.
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03
Prototype
Ship a working application against real data and real users.
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04
Harden
Add evals, guardrails, monitoring, and cost controls.
A low-risk way to start
Short, fixed scope, clear deliverable. You get a plan you can act on — with us or without us.
AI Readiness Assessment
We check whether your data can actually support the AI features you want, and tell you what has to be built first.
- Use-case shortlist scored on value and feasibility
- Gap analysis of your data, semantics, and governance
- Reference architecture and delivery plan for the first build
Related work
Platforms we have built in this space, with the team setup and the architecture behind each one.
Frequently asked questions
What is AI data engineering, exactly?
It is the data work that has to happen before an AI feature can be reliable: getting unstructured content into the platform, chunking and embedding it, keeping it in sync, and defining semantics clearly enough that a model returns correct answers. Most 'AI problems' are really data problems.
Do you build on our existing warehouse or somewhere else?
On your existing platform wherever possible — Databricks, Snowflake, or your cloud provider's AI services. Adding a separate AI stack next to your warehouse creates a second copy of the truth, and that is what breaks first.
How do you stop the model from giving wrong answers?
Three things: a clean semantic layer so the model queries the right definitions, an evaluation set that scores answers on every change, and guardrails that refuse rather than guess. We treat evals as a deliverable, not an afterthought.
Can you help our engineers adopt AI-assisted development?
Yes. We run enablement on AI-assisted coding workflows — practical frameworks, prompts, and review guardrails so your developers ship faster without dropping quality or security standards.
Ready to talk about AI Data Engineering?
Tell us what you are working on. We reply within one business day, and we will tell you plainly if we are not the right fit.