Intelligent document processing
Turn unstructured documents into reviewed, structured business data.
The value is not OCR alone. The complete workflow securely receives, inspects, classifies, extracts, validates, compares, routes exceptions, and presents approved data inside the existing claims or underwriting experience.
- 01
Input boundary
Receive and secure
Loss runs, medical records, estimates, reports, correspondence, and other approved documents enter a controlled path with source, file-type, size, and malware checks.
- 02
Document preparation
Classify and extract
OCR or document intelligence identifies the class, fields, entities, tables, dates, and confidence while retaining the original source locator.
- 03
Deterministic control
Validate and compare
Identifiers, totals, dates, policy and claim references, source authority, and missing evidence are checked outside the model.
- 04
Human control point
Resolve exceptions
A qualified reviewer sees the source, extracted value, confidence, discrepancy, and allowed action, then corrects, approves, rejects, or escalates.
- 05
System boundary
Write through a governed command
Only an approved, validated command may update the authoritative claims or underwriting system. The service records the audit event and verifies final state.
Authorized claims knowledge
Turn years of records into a permission-aware, source-backed knowledge service.
Claim notes, policies, endorsements, procedures, loss histories, correspondence, manuals, and regulatory guidance can become easier to retrieve without making a language model the system of record. Identity, record-level permissions, source authority, effective dates, citations, lineage, and audit logging remain part of the answer contract.
Authorize before retrieval
Filter by user, role, claim, source, jurisdiction, effective date, and data classification before evidence enters model context or cache.
Retrieve authoritative evidence
Use governed search, APIs, and source registers to distinguish controlling records from supporting or unverified material.
Answer with the basis
Return source title, version, effective date, section or record locator, material limitations, and any unresolved conflict.
Abstain, correct, or escalate
Insufficient, stale, conflicting, or inaccessible evidence becomes a controlled outcome with a correction or human-review path.
The AI does not become the system of record. It retrieves, organizes, extracts, compares, summarizes, or recommends against authorized evidence.
Anomaly and fraud triage
Find the claims and transactions that deserve human attention.
The objective is not a vague “fraud accuracy” score. The system should prioritize limited investigative capacity using documented signals, representative evaluation, explanations, feedback, and monitored operating economics.
- 01
Record observation
Observe the authoritative record
Use approved SQL Server or MySQL views, controlled extracts, CDC, or events without giving models unrestricted transaction authority.
- 02
Feature control
Build governed features
Validate data quality, lineage, historical windows, deterministic indicators, missingness, and relevant business segments.
- 03
Signal comparison
Combine candidate signals
Compare rules, supervised methods, unsupervised anomaly methods, and optional graph analysis under the same review capacity.
- 04
Human disposition
Prioritize an investigator queue
Show the signal basis and route the case to a qualified investigator who owns disposition and feedback.
- Precision at review capacity
- False-positive cost
- Investigator yield
- Review time
- Calibration
- Stability over time
- Explanation usefulness
The model prioritizes review. The investigator remains responsible.
Control boundary
AI is an assistant inside the claims workflow—not the business authority.
Controls must exist in identity, policy, software, operations, and review procedures rather than only in a prompt.
- Deny by default and least privilege
- Separate planning from execution
- No production credentials in model context
- Approval before payment, denial, reserve, or rights-affecting action
- Source-aware retrieval and citation checks
- Model, prompt, data, rule, and code version traceability
- Exception queues and manual fallback
- Drift monitoring, incident response, and tested rollback
Pilot decision
A claims AI pilot earns production through evidence.
The next decision is explicit: scale, change, defer, replace, or stop.
Bounded entry points
Start where preparation is mechanical and judgment remains accountable.
A useful first scope is one recurring queue, document class, knowledge question, or investigative handoff with an observable baseline and a named claims owner.
FNOL and intake preparation
Classify submissions, validate identifiers, identify missing evidence, and route exceptions.
Claim-document workbench
Extract and compare facts, prepare sourced chronologies, and present low-confidence fields for review.
Investigation prioritization
Combine deterministic indicators and models to rank review candidates without allowing an unexplained score to decide an adverse action.
Loss-run and record retrieval
Retrieve authorized history with citations, effective-date context, and correction routes.
Prepare claim messages
Draft from approved templates and evidence while retaining human approval before external transmission.
Claims-system modernization
Expose APIs, isolate database access, improve tests and telemetry, and replace one business capability at a time.
What the first engagement produces
Buy the next decision—not an open-ended claims transformation.
A bounded diagnostic or assessment should make the current workflow, system boundary, evidence, control requirements, acceptance method, and next investment decision inspectable.
Workflow and baseline
Current-state map, queue and exception profile, cycle and touch time, document volume, rework, quality, and available operating evidence.
Decision: Is the problem material and measurable?
System and data boundary
Applications, databases, repositories, integrations, authoritative records, access rules, retention, and failure-sensitive dependencies.
Decision: Where can change be introduced safely?
Option and control comparison
Process redesign, rules, integration, document intelligence, retrieval, anomaly triage, or bounded AI compared with human authority, security, and rollback.
Decision: Which intervention is justified?
Pilot and Scale Gate
Representative cases, acceptance thresholds, shadow or limited release, exception economics, observability, incident path, and scale-or-stop memorandum.
Decision: Scale, change, defer, replace, or stop?