Data & RAG Integration
Your data is fragmented, messy, and exactly where the value is. We connect it, clean it, and wire it into retrieval and agent workflows that reflect your business — with citations, permissions, and freshness — instead of a generic model guessing.
The difference between an AI system that transforms your operations and one that embarrasses you in a board demo is almost never the model. It's whether the system knows your business: your policies, your contracts, your tickets, your tribal knowledge trapped in SharePoint folders nobody has governed since 2019.
Getting there is data engineering, not prompt magic: ingestion from systems that were never meant to talk, document pipelines that turn scans and PDFs into structured truth, retrieval tuned on your real questions rather than tutorial defaults, and permission-aware search so the model never surfaces a document the asking user couldn't open. We build that whole path — and we prove it with retrieval evals, not adjectives.
Map where truth lives
The corpus audit: which sources matter for the first workflow, what condition they're in, and what 'fresh enough' means.
Build the ingestion spine
Connectors, document processing, normalization, and quality gates — incremental freshness, not nightly re-embeds of the world.
Tune retrieval on reality
Chunking, hybrid search, reranking, and permission filters — evaluated on golden question sets from your actual users.
Ground the generation
Citation-backed answers, faithfulness evals, and 'I don't know' as a feature, not a failure.
Answers grounded in your documents with sources attached — trust as a feature.
ACL-aware retrieval: nobody learns anything through the AI they couldn't learn without it.
Policy changes at 9 a.m. are in answers the same day, not after the quarterly re-index.
Our data is a disaster. Do we need a data project before an AI project?
No — you need the slice of data the first workflow requires, cleaned to sufficiency, with quality gates so it stays that way. Boiling the governance ocean first is how initiatives die. Perfect data is a mirage; sufficient data is a four-week milestone.
How do you handle document permissions in RAG?
At retrieval time, against the asking user's actual entitlements — source-system ACLs propagated into the index and enforced on every query. It's non-negotiable architecture: a chatbot that leaks the M&A folder to the intern is a career-ending demo.
Whose RAG stack do you use?
Yours, where it exists; open, boring, and swappable where it doesn't. pgvector or a dedicated vector store as workload dictates, hybrid retrieval, and evals that make every component earn its place. No vendor lock-in disguised as architecture.
Ready to skip the kickoff theater and ship?
Tell us about the AI initiative your last three vendors couldn’t close. We’ll scope the outcome on a short call.
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