Executive-friendly GPT demos rarely survive contact with messy operational data unless retrieval, citations, and evaluation harnesses are designed like any other production service. Cherry Tech approaches applied AI the same way it approaches payments: observe real traffic, price failure modes, and bake governance in from the first sprint—not as a later audit patch.
The training signal hides in tickets long before it hides in slide headlines
Anonymized support threads, RFQ emails, underwriting checklists—the places staff already burn hours searching SharePoint—carry hundreds of weakly labeled examples. Those examples become tests for retrieval quality before anyone pays for fine-tuning theater.
Trust attaches to sources, not fluency
Retrieval-augmented generation earns adoption when answers point to internal policy PDFs, CRM rows, or ERP tables with freshness visible in the UI. Reviewers who can reject a bad suggestion without filing a one-line “felt wrong” bug signal a system that fits enterprise reality.
Drafting and committing stay separate when money and mail move
Automations that touch ledgers, customs filings, or client email need human gates with workflow IDs or signatures worth auditing. Model version, prompt hash, and checksum on source documents travel with the decision—audit teams care about traceability, not vendor logos.
Cost and latency budgets behave like SLAs on Gulf mobile paths
Token usage balloons when agents loop blindly. Session caps on tool calls, streamed partial responses, and handoffs to deterministic scripts when latency crosses a line keep customer-facing lanes honest on networks that already feel slow.
“Clever” models lose to measured usefulness
Time-to-resolution, rework, escalation rate, and bilingual spot checks tell whether assistance is real. Metrics that cannot reach finance or satisfaction inside two hops usually mean the instrumentation—not the model—is the bottleneck.
Impressive language is cheap; dependable augmentation is expensive and worth it. The boring scaffolding—indexes, eval suites, escalation paths—turns a model swap from a crisis into a parameter change.