OCR and document intake: go/no-go before you buy model spend

☷ Many teams start with a chatbot because demos are easy to show.

☷ However, daily operational cost sits in email PDFs, scanned forms, invoices, and support tickets that humans re-type into CRM or ERP systems.

☷ OCR and intake automation directly attack that queue if you can clearly describe: Who starts the workflow — What arrives as input — What a correct result looks like — Who handles exceptions.

☷ If you cannot write that paragraph, you are not ready for production AI. The AI ops evaluation checklist exists to keep the conversation concrete.

☷ Volume & Peak Hours: Define daily/monthly volume and peak spikes upfront.

☷ Tool Integration: Name tools already in use (CRM, ERP, Helpdesk) so automation lands where people actually work.

☷ Sample Data: Share sample documents or redacted tickets early; without real data, evaluation is purely theatre.

☷ Quality & Error Thresholds: Set an acceptable error rate and define the exact cost of a bad answer to the business.

☷ Human Review: Decide upfront when a human operator must review outputs.

☷ Compliance & Privacy: Establish privacy limits in the first brief, not the week before launch.

☷ High error cost means human review is designed into the workflow from day one, not bolted on after a bad pilot.

☷ Escalation paths, restricted topics the system must not invent, and PII masking rules are part of the core product.

☷ Lightweight post-launch monitoring is part of the engagement: prompts, model spend, and failure rates need a named owner in week one.

☷ Demo-only pilots without this operational path become unpaid production support.

☷ Say NO when: Data quality is the real blocker — Nobody internal owns the business workflow — Legal blocks every sample document without offering an alternative — The underlying legacy system is too fragile to automate yet. (In these cases, Performance Surgery or Delivery must come first) — Teams insist on a chatbot-first approach when intake or routing is the primary pain point.

☷ Saying no early protects budget and builds organizational trust.

☷ One single workflow.

☷ Clear users, evaluation criteria, integration assumptions, guardrails, and a post-launch monitoring checklist.

☷ That is enough to price discovery and execute a first production milestone under Pragmatic AI.

☷ Bring the checklist to Contact with the AI workflow intent. Keep KPI and guardrail notes as acceptance criteria so they are not renegotiated mid-build.

Do you only build chatbots?

No. Common work includes document OCR, support automation, and forecasting tied to your existing tools. Chat is only one interface pattern, and often not the highest ROI starting point.

What if we cannot share real documents?

Say so early. We discuss data redaction, synthetic samples, or on-premise options before promising timelines. No samples usually means no honest evaluation.

How do we start?

Send the AI ops checklist via Contact with the AI workflow intent. Include one sample workflow and privacy constraints. We reply with fit and whether Performance Surgery or Strategic Delivery should come first.