Patterns
Vertical AI patterns
The product shapes we keep seeing inside operating businesses: real workflow pain, explicit data, named owners, and a v1 small enough to prove in weeks.
AI quoting engine
The AI quoting engine
Custom quoting is where vertical businesses bleed time: the catalog is enormous, the pricing logic lives in two estimators' heads, and every quote is a small research project.
Read patterndocument-grounded RAG with citations
Document-grounded RAG
Your industry's answers are buried in guideline PDFs, spec sheets, and policy documents nobody reads end to end.
Read patternAI operator console for internal operations
The operator console
AI output that lands in a chat window dies in the chat window.
Read patternAI intake and triage agent
The intake triage agent
Every operating business has a front door where work arrives raw: an inbox, a form, a phone queue.
Read patternvertical AI data model for industry data
The vertical data model
Every vertical AI product stands on the same unglamorous thing: the industry's mess — catalogs, SKUs, option trees, guideline exceptions — modeled as entities software can reason over.
Read patternAI document extraction pipeline
The document pipeline
Purchase orders arrive as PDFs, specs as scans, applications as attachments — and someone re-types them into the system of record.
Read patternAI follow-up copilot for quotes and proposals
The follow-up copilot
The cheapest revenue an operating business loses is from people who already asked to buy.
Read patternAI agent workflow harness with guardrails
The agent workflow harness
The distance between an agent demo and an agent your business trusts is a harness: scoped permissions, retries, audit logs, human checkpoints, and a ledger of what each run was worth.
Read patternAI pricing engine for complex catalogs
The pricing engine
In most vertical businesses the price isn't a number — it's a computation: catalog, options, manufacturer multipliers, freight, margin policy, and the exceptions your best estimator carries in their head.
Read patternAI research workspace for analysts
The research workspace
Serious research work — diligence, underwriting, analysis — dies in tabs: filings in one, notes in another, the thesis in someone's head.
Read pattern
Next step
Score the wedge before you build.
Bring the workflow, owner, data, and proof line. The review turns that into a build, fix-first, or don’t-build call.