A live retrieval demo, search build notes, and practical patterns for local AIOS layers, tool-using workflows, routing, support, reporting, and dashboard systems.
A reference architecture demonstrating how to parse, index, and query massive nested OpenAPI specs locally using pgvector. Resolves circular references and token limits that break standard RAG chains by preserving parent-child schema structures during vector chunking.
Marketplace search breaks when users type vague human queries like "vibey coffee spot" instead of exact categories. The goal was to make search more forgiving without letting AI invent venues or ignore real filters like location and hours.
Mid-market e-commerce merchants lose hours manually syncing shipping statuses when they source products across multiple third-party logistics (3PL) warehouses. Mismatched carrier tracking numbers lead to customer support backlogs and duplicate fulfillment states.
Small businesses lose up to 30% of booking conversions when phone inquiries arrive outside working hours. Rigid automated interactive voice response (IVR) flows lead to high drop-off rates and empty calendars.
Inbound leads sit in forms, inboxes, spreadsheets, or CRMs while someone manually qualifies and assigns them.
Closed-won deals still require someone to create invoices, check payments, and update finance tools by hand.
Support teams repeat the same answers and lose time deciding where each ticket should go.
Teams need an operating layer where AI can understand context, use approved tools, remember workflow state, ask for approval, and leave an audit trail.
Teams need a shared view of workflow status, exceptions, handoffs, and what the controlled system already handled.
Connecting legacy software or local databases to modern cloud tools usually requires brittle, custom middleware that breaks without warning.
Send one bottleneck. I will tell you whether it needs a no-code fix, a focused system repair, or a scoped AI build.