Core USA helps qualified enterprise and upper-mid-market teams test one real workflow with governed AI, measured outcomes, human review controls, and a scoped production recommendation — with no upfront proof-of-concept build fee for approved use cases.
Workflow Selected
One real operational process defined
Sample Data + Systems Reviewed
Documents, APIs, access mapped
Governance Controls Designed
Permissions, gates, audit, escalation
AI Workflow Built + Tested
Real data, governed execution
Measured Outcome Report
Decision-ready evaluation delivered
Workflow Selected
One real operational process defined
Sample Data + Systems Reviewed
Documents, APIs, access mapped
Governance Controls Designed
Permissions, gates, audit, escalation
AI Workflow Built + Tested
Real data, governed execution
Measured Outcome Report
Decision-ready evaluation delivered
The proof of concept is built around one real operational workflow where AI can prepare, route, summarize, classify, extract, reconcile, or surface information for human review.
This is built around your workflow, your documents, your systems, and your operating rules — not a pre-built template with placeholder data.
Outputs are prepared for human review with controls, approvals, confidence scoring, and exception handling built into every stage.
The goal is to validate one practical workflow first, then decide whether production deployment makes sense — not redesign your entire operating model.
The strongest candidates are workflows with measurable volume, clear review steps, and enough repetition to justify AI-assisted execution.
Six categories where governed AI consistently produces measurable cycle-time, accuracy, and throughput results for enterprise teams.
Claims, applications, contracts, invoices, PDFs, forms, and mixed-format documents that need extraction, classification, and routing.
Shared inboxes, service queues, support tickets, approvals, and operational handoffs that need faster triage.
Policies, procedures, contracts, manuals, records, and knowledge bases where teams need traceable answers.
Multi-source reports, variance checks, billing reviews, compliance packs, and recurring analyst workflows.
Processes where AI handles standard preparation while escalating edge cases to the right reviewer.
Call summaries, agent-assist prompts, structured call flows, follow-up notes, and issue classification.
A structured path from workflow selection to measured outcome — not a vague AI brainstorming session.
Define the workflow, users, systems, data sources, volume, exception patterns, and measurable success criteria.
Identify the documents, applications, APIs, repositories, queues, databases, and access requirements involved.
Set permission boundaries, human review gates, confidence thresholds, audit trails, and escalation paths before build.
Test a governed AI workflow against a representative sample of real operational data.
Deliver measured results, governance confirmation, production-readiness view, and a scoped next-step recommendation.
A decision-ready evaluation package.
Most organizations already have enough to begin. The proof of concept does not require perfect data or a full AI team.
One operational process with clear inputs, steps, decision points, and outputs.
A basic understanding of where the documents, data, applications, and approvals live.
A practical baseline such as cycle time, accuracy, throughput, capacity, cost reduction, or backlog reduction.
A stakeholder who can prioritize the workflow, coordinate subject-matter experts, and review outcomes.
Enough sample records, documents, tickets, reports, or workflow examples to validate the AI approach.
Core USA uses the proof of concept to determine whether a real enterprise workflow is a practical fit for managed AI delivery. Approved use cases receive a scoped evaluation before production commitment so both sides can validate operational value, governance fit, and deployment readiness.
Not every workflow qualifies. The proof of concept is reserved for operational use cases with clear governance boundaries and measurable outcomes.
We validate one bounded operational process — not an undefined AI exploration across multiple departments.
After the review, you decide whether to proceed. There is no obligation and no hidden production commitment.
The proof of concept is designed for enterprise review, traceability, and controlled adoption.
AI agents operate within defined permission boundaries. They access only the data sources, systems, and actions approved for the workflow.
AI output is prepared for human review, not dispatched autonomously. Review queues, approval steps, and override paths are built into every workflow.
Every AI action is logged: which agent performed the action, what data was accessed, what output was produced, and who reviewed or modified it.
Each AI output includes a confidence score. Low-confidence results are automatically routed for human review rather than passed through blindly.
AI-generated answers and summaries include source-linked references so reviewers can trace output back to the originating document or data source.
Defined escalation paths for edge cases, anomalies, and out-of-scope inputs. When AI encounters something it shouldn't handle, the workflow routes it to the right person.
A complete evaluation package — not a sales pitch, not a vague recommendation.
Baseline metrics compared against AI-assisted results across speed, accuracy, throughput, backlog, or review effort.
Documentation showing how permissions, review gates, confidence scoring, audit trails, and exception paths performed.
A practical deployment path with timeline, infrastructure needs, system dependencies, and managed delivery scope.
A straightforward recommendation: proceed to production, refine the workflow, improve data readiness, or pause.
Bring one workflow, document-heavy process, reporting bottleneck, service queue, or operational handoff. We'll help determine whether it is a practical fit for governed AI delivery — without a large upfront implementation fee.
No upfront implementation fee for approved use cases. No full-time AI engineering team required. Governed AI delivery from day one.
