AI Compliance Intelligence Platform
LLM scoring becomes useful only when ingestion and failure isolation are dependable.
ResultMade ingestion and scheduled processing safer while improving the prompt execution path behind configurable scorecards.
- Role
- Full-stack and AI workflow engineering
- Period
- Professional engagement
- Context
- Regulated call operations
- Confidentiality
- Anonymized professional work
At a glance
LLM scoring becomes useful only when ingestion and failure isolation are dependable.
Hardened concurrent ingestion with database-enforced uniqueness, isolated scheduled failures per item, improved the LLM prompt runner, and added preflight checks for production scripts.
Made ingestion and scheduled processing safer while improving the prompt execution path behind configurable scorecards.
The choice behind the implementation.
- Challenge
- Concurrent ingestion and scheduled processing could duplicate work or let one invalid item block an entire batch.
- Decision
- Enforce uniqueness at the data boundary and isolate background failures per item.
- Trade-off
- The workflow needs explicit job controls and failure states, but concurrency becomes safer and recovery stays bounded.
The workflow
Recordings arrive from secure file transfer, object storage, and partner APIs. They are transcribed and evaluated against configurable scorecards for required disclosures and agent performance. The system must preserve tenant boundaries and keep partial failures observable.
What I delivered
- Shipped production features and fixes across ingestion from secure file transfer, object storage, and partner APIs.
- Hardened concurrent ingestion with race-safe writes backed by unique database indexes.
- Added per-item isolation to scheduled processing so one invalid record cannot block the remaining workload.
- Improved the prompt runner used by the LLM scorecards.
- Added read-only preflight checks to destructive production scripts so operators can inspect the expected blast radius first.
AI quality depends on the reliability of everything before and after the model call.
The public boundary is deliberate.
Product and client identities, customer data, repositories, and confidential implementation details are intentionally omitted. The page describes only confirmed personal contribution and safe technical context.