At CourtCorrect, we’ve spent years helping regulated teams resolve complaints with speed and accuracy. The same discipline now underpins a broader ambition: use AI to transform how information moves through customer journeys more widely, starting with the messy, unstructured files that slow processes down.
1) Capture your policy as a checklist.
Every review already has a backbone. Whether that’s checklists, policy questions or even a “what good looks like.” We work with your team to understand the rationale behind these documents and then transform them into a framework that our AI models can understand.
2) Extract answers with evidence.
Once we've defined your checklist, the AI can understand a wide range of documents, including phone-call recordings and transcripts, images, PDFs, emails, and more. It reads all uploaded materials, maps the facts to each field, and returns a populated checklist. Answers can be configured as simple dropdowns (e.g., Yes/No), multiple choice, or free-text responses.
3) Manual Review
After our AI has output its answers, handlers can review them with full context. For every populated field, the platform shows a concise rationale alongside citations back to the original documents. This gives reviewers clear insight into the decision-making process and makes it easy to query or challenge any answer.
4) Aggregate Insights
The CourtCorrect Platform automatically aggregates results into MI dashboards that reveal how your business operates in practice: policy adherence by team or product, common failure points, throughput and rework rates, SLA performance, and outcome trends over time. What’s more, you can use our AI to query this data, enabling greater insights into a larger volume of data than ever before.
Core use cases
We’ve already started putting this new technology to work across a number of different use cases:
Insurance Underwriting
When assessing an insurance application, underwriters traditionally sift through reams of documents, tracking down omissions and inconsistencies that slow the entire process down.
With AI Custom Fields, the workflow is reversed: your underwriting rules shape the checklist. The moment documents arrive, the AI extracts each relevant detail, tags it to the right underwriting factor, and builds a real-time risk profile overview. Instead of poring over paperwork, underwriters verify the insights, probe the outliers, and use the richer picture to calibrate premiums, exclusions, and coverage levels, so every applicant sees a product precisely tuned to their risk.
Mortgage Checks
Behind the scenes, mortgage compliance teams ask a different question: did the broker follow policy, and is the documented rationale sound? The answer used to live in scattered notes and a sample of files reviewed long after the fact.
We will take your internal product‑suitability tests and translate them into questions the AI can answer from the file. When a case closes, the system assembles the evidence of suitability directly from the documents: the product’s term and rate, the customer’s financial profile, the stated rationale, the disclosures acknowledged. The result is a revolution in QA: instead of hunting for problems, teams see where a file diverges from policy and focus their time on those cases. Audits become cleaner because every “yes” and “no” points back to the source.
Quality Assurance
QA leaders focus on a different question: did our people follow policy in how cases were handled, and is the documented rationale sound?
We take your QA checklists and translate them into questions the AI can answer across every interaction, phone-call recordings and transcripts, emails, PDFs, images, and more. When a case closes, the system assembles the evidence of conduct directly from the record: required disclosures, timelines met, approvals captured, and the rationale communicated.
The result is QA that’s continuous, not episodic. Instead of hunting for problems, teams see exactly where a file diverges from policy and focus their time on those cases. Reviews and audits become cleaner because every “yes” and “no” is evidenced and traceable back to the source.
What makes it work: the CourtCorrect playbook
Technology is only half the story. The rest is the careful setup that ensures the checklist mirrors your real‑world process and stays aligned as policies change.
We sit with your policy owners to understand exactly what each field must capture and why. That conversation shapes the schema, acceptance criteria and evaluation sets. We iterate rapidly on feedback and drill into edge cases, to give you the necessary confidence in our AI’s capabilities.
With the checklist confirmed, we map the fields onto the CourtCorrect Platform and switch on access for your team right away. From day one, you can process real cases, see the extracted answers in context, and submit feedback. That feedback routes to our specialists, who adjust field definitions and AI logic quickly, so the model sharpens on your data while your risk, QA and operations teams continue working from a single, structured view. The result is a rapid, low‑friction start that moves smoothly from first access to meaningful throughput.
Why this approach endures
AI Custom Fields build on your existing frameworks to extract answers quickly and consistently, turning documents into structured outputs that cut turnaround times and reduce variation. The output informs better decision making, highlighting where policy is working and where it needs attention. Skilled reviewers stay in the loop for judgement calls, edge cases and higher‑value tasks, while the AI handles the reading, extraction and scoring, delivering faster outcomes without cutting corners.
AI Custom Fields go far beyond lending, mortgages and QA checking. Anywhere you rely on documents and policy checklists, they can turn unstructured files into consistent, decision‑ready answers. If you’d like to see how this could work for your business, get in touch and we’ll take you from PoC to deployment on the CourtCorrect platform.
See your checklist come to life.