Workflow type
PII detection across the narrative notes, forms, and free text that accumulate in a claim file.
Deep dive
Reviewable PII detection for regulated claim workflows.
A concise look at a regulated claim-document pipeline: prompt-routed LLM extraction, deterministic validation, post-processing, release gates, and workflow-level evaluation.
Built for an enterprise-scale historical document archive and a continuous flow of new claim text, noisy PDF extraction, configurable local or platform execution, and traceable outputs staff can evaluate before downstream use.
System boundary
Outputs stay bounded to a fixed set of PII/entity classes and structured results staff can review.
Platform layer
One configuration-driven workflow supports local and platform execution with MLflow, run artifacts, release gates, and audit logging.
Delivery flow
Detection only counts when the evidence is clean.
The pipeline moves from extracted claim text to routed prompts, hybrid validation, scoring, and traceable structured output.
01
Text intake
Load PDF-extracted claim text from approved tabular sources, covering both the historical archive and newly arriving documents.
02
Prompt routing
Route documents by type and text shape before inference.
03
LLM extraction
Run LLM inference through approved local or platform runtimes for bounded PII/entity classes.
04
Hybrid validation
Combine model output with regex signals, dictionary matches, structural lookups, and span repair before scoring.
05
Audit + evaluation
Score against labeled data, write run artifacts, and prepare traceable structured output for downstream use.
System map
The parts a reviewer can point at.
The same pipeline as a structure instead of a sequence.
Runtime
Detection
Evaluation and governance
Output
Runtime config: One workflow contract keeps local and Databricks runs comparable.
Operating principles
Design principles for reliable detection.
Scope
Detect bounded entity types across noisy documents.
The pipeline targets a fixed, documented list of personal-information and entity types rather than open-ended generation.
Grounding
Pair LLM output with deterministic evidence.
Regex matches, structural lookups, dictionaries, and post-processing help remove weak spans and support cleaner evidence.
Runtime
Keep one config contract across local and Databricks runs.
A single configuration-driven runtime path keeps local development and platform execution aligned.
Governance
Make every run inspectable.
Run summaries, event logs, and MLflow metrics make it easier to trace why a result was accepted, rejected, or tuned.
Evaluation rubric
How the pipeline is measured.
Accuracy
Do precision, recall, and F1 hold up against labeled claim-document data?
Evidence quality
Are weak detections removed, overlaps resolved, and spans repaired before writeback?
Runtime consistency
Does the same config behave reliably across local development and Databricks execution?
Traceability
Can each run be explained through summaries, events, and MLflow metrics?
Failure boundaries
Reasons to stop and review.
01
Outputs that cannot be traced back to model or rule evidence.
02
Entity spans that overlap, drift, or stay too weak after post-processing.
03
Runtime differences between local and Databricks execution that change evaluation behavior.
04
Writebacks that are not ready for safe downstream use.
For confidential work, the right public evidence is disciplined: pipeline shape, evaluation logic, auditability, and delivery judgment, with internal data, figures, and system specifics left out.