OpsCore: Evidence-Grounded AI Operations Brief

OpsCore adds a GPT-5.6 start-of-day brief to an operations control center, ranking fulfillment, inventory, and inbound exceptions with traceable evidence and human-reviewed next actions.

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Inspiration

Operations teams rarely lack data; they lack a fast, reliable way to decide what deserves attention first. A normal start of day means moving between sales orders, inventory balances, inbound purchase orders, spreadsheets, and status reports before anyone can act.

What it does

OpsCore adds an evidence-grounded AI Operations Brief to an existing operations control center. It narrows a production-shaped synthetic ERP workload from 1,248 records to 63 open records, 15 grounded exceptions, and five prioritized actions. Each action explains the operational risk, recommends an owner and next step, and cites the exact source records. Reviewers can open those citations in the corresponding fulfillment, inventory, or inbound workspace.

The AI is deliberately read-only: it cannot update orders, adjust inventory, receive containers, approve transactions, or mark work complete.

How I built it

The judging deployment is isolated from the live product. A deterministic synthetic ERP twin generates 742 sales orders, 386 inventory balances, and 120 purchase orders. Rule-based screening removes routine work before a bounded exception snapshot is sent to GPT-5.6 Sol through the OpenAI Responses API. The server—not the browser—owns the reasoning payload. Display identifiers are removed, store: false is used, and a strict JSON schema makes the output renderable and testable. Post-generation validation rejects missing or category-mismatched evidence references.

How Codex accelerated the build

GPT-5.6 Sol in Codex accelerated the synthetic adapters, dashboard integration, Responses API endpoint, prompt and schema design, security boundaries, tests, documentation, and deployment preparation. I made the core product, operational, privacy, and human-review decisions, then verified every workflow.

Challenges

The hardest part was preserving the mature OpsCore experience while making the AI contribution obvious, traceable, and safe. I avoided building a separate chatbot: the brief is embedded in the dashboard, grounded in the same operational workspaces, and designed around human approval.

Accomplishments

  • One-click ERP analysis and AI prioritization
  • Cross-domain reasoning over fulfillment, inventory, and inbound operations
  • Exact evidence links for every recommendation
  • Synthetic judging data with no production connections
  • Read-only architecture with strict structured-output and citation validation
  • Password protection, rate limits, request-size limits, security headers, and automated tests

What I learned

Operational AI earns trust through evidence, bounded authority, and fit with existing workflows—not by replacing the underlying system of record.

What's next

Next, I would add reviewer feedback loops, configurable prioritization policies, and measured time-to-resolution outcomes while keeping every system update human-approved.

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