Illinois · Applied AI
Semantic search (RAG) in Chicago
Semantic search (RAG) designed around the way a Chicago team actually runs.
What semantic search (RAG) actually does
Search that understands intent, not just keywords. Your team types what they mean – and gets the right document, ticket, or product from across every system, with citations.
Chicago businesses don't need another generic AI pitch. Semantic search (RAG) only earns its keep when it's built around the workflow you actually run on a wet Tuesday, and that's how we scope every engagement we take on in Illinois.
- 01 Indexes Drive, SharePoint, Notion, Slack, your CRM
- 02 Returns answers with source links – no hallucinations
- 03 Permissioned so staff only see what they should
- 04 Re-indexes nightly so results stay fresh
Built on: Pinecone Claude Postgres pgvector Vercel AI SDK
Why Chicago businesses choose this
Chicago runs on logistics, manufacturing, finance and a genuinely diverse industrial base – AI here means fitting into operations that were already running lean before AI existed.
What Chicago teams tell us when they get on a call.
A major freight and logistics hub, a deep manufacturing base, and a futures and options trading industry with zero patience for anything slow. Chicago businesses want AI that respects an operation that already runs tight.
We work with teams across Chicago: The Loop · River North · Wicker Park · Lincoln Park · Evanston · Naperville.
How we build semantic search (RAG) for a Chicago team
We scope narrow, ship a working pilot, then harden it into production. The first slice is the highest-leverage workflow for your Chicago business, so value lands before the build is finished. Every engagement starts with a short call and a paid discovery if the brief needs one.
AI search over your data.
The outcome for Chicago teams
If we build the right slice first, Chicago teams feel the difference inside the first month. Average search time drops from 6 minutes to 12 seconds.
Average search time drops from 6 minutes to 12 seconds.
Semantic search (RAG) in Chicago – common questions
What's the realistic timeline for semantic search (RAG) with a Chicago?
Most Chicago businesses have their first usable slice in week 5 or 6. We'd rather ship narrow and real than broad and aspirational – your team gets to use the thing well before the engagement is "done".
Is semantic search (RAG) worth it for a smaller Chicago?
Often, yes – and counterintuitively the ROI is sometimes faster than for the big end of town because there's less integration overhead. We'll tell you honestly on the scoping call if it isn't.
Anyone else in this space using semantic search (RAG)?
Plenty. Average search time drops from 6 minutes to 12 seconds. The interesting question is rarely "does it work" – it's "is your team ready to use the output." That's what we'd scope on the call.
What happens if we want to swap a vendor out later?
Semantic search (RAG) is built behind a small adapter layer specifically so swapping a model provider or a data source is a one-day job, not a re-architecture. Pinecone, Claude, Postgres pgvector, Vercel AI SDK are our defaults, but the build is intentionally portable.
Twenty minutes, your call.
You describe what's broken. We'll tell you what we'd actually do about it.
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Talk to us about this
Tell us what you're trying to do and we'll reply with how we'd build it — no obligation.