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

Many teams start with a chatbot because demos are easy to show. Daily cost often sits in email PDFs, scanned forms, invoices, and tickets that humans re-type into CRM or ERP. OCR and intake automation attack that queue if you can describe who starts the workflow, what arrives, what a correct result looks like, and 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.

Define volume and peak times. Name tools already in use so automation lands where people work. Share sample documents or redacted tickets early; without data, evaluation is theatre. Set an acceptable error rate and the cost of a bad answer. Decide when a human must review. Privacy and compliance limits belong in the first brief, not the week before launch. Pick one primary KPI: time saved, accuracy, cost per case, or wait time.

High error cost means human review is designed in, not bolted on after a bad pilot. Escalation paths, topics the system must not invent, and PII rules are part of the product. Light monitoring after launch is part of the engagement: prompts, model cost, and failure rates need an owner in week one. Demo-only pilots without that path become unpaid production support.

Say no when data quality is the real blocker, when nobody owns the workflow, when legal blocks every sample without an alternative, or when the underlying system is too fragile to automate yet. In those cases Performance or Delivery may come first. Say no to chatbot-first when intake or routing is the pain. Saying no early protects budget and trust.

One workflow, clear users, evaluation criteria, integration assumptions, guardrails, and a monitoring checklist. That is enough to price discovery and 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 tools. Chat is one interface pattern, often not the highest ROI start.

What if we cannot share real documents?

Say so early. We discuss redaction, synthetic samples, or on-prem 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 or Delivery should come first.