Intelligent Document Processing Services Scored Against Your Own Decisions
We build document-to-decision pipelines, prove them on files you already closed, and staff them afterward.
Intelligent document processing services turn recurring documents, such as credit agreements, borrower reporting, insurance submissions and medical files, into checked fields a credit or underwriting decision can rest on, with every field traced back to its page. KORE1 builds that pipeline, tests it against decisions your team already made, and recruits the people who run it.
Last updated: September 21, 2026
Most firms that call us already own an extraction tool. It lifts fields off PDFs, attaches a confidence score to each one, and passes the result to an analyst who checks it by hand anyway, because nobody can say which of those fields a committee should trust. So the work happens twice.
What’s missing sits between the output file and the decision. Definitions, mostly. Rules, too. That’s why this service sits with KORE1’s data engineering and data science recruiters, who place the people that keep both, and why it follows the published document-to-decision method of KORE1 consultant Khurram Tehseen.
One Page of a Medical File, Field by Field
A composite specimen. No real patient. Each numbered box marks a value the pipeline read, and the list beside it shows the check that value had to pass and where it went next.
- 1 service_date 02/11/2025 A valid date inside the file’s date range Straight through
- 2 diagnosis E11.9, type 2 diabetes Maps to one ICD-10-CM code on the impairment list Straight through
- 3 hba1c_pct 7.9 Numeric, in range, and dated by its own collection date Straight through
- 4 tobacco_status former, quit 2019 Page 37, an intake form from 2016, says never smoker To a reviewer
- 5 medications metformin, lisinopril On the drug list and consistent with the diagnoses on file Straight through
- 6 addendum no value written The source region is unreadable, so nothing gets guessed Held for a person
- Passed its checks
- Sent to a person
- Unreadable, nothing written
- The decision a person signs
Start with box 4. The extraction is right, since the note really does say quit 2019, and any model would read it with high confidence. The trouble is page 37. A tobacco answer can move a rating class, so only a rule that compares pages across the whole file catches the conflict.
Box 6 gets no value at all. Guessing would be faster, and it would also plant the one error nobody could trace later, because that part of the page never said anything a machine could read. An underwriter opens it instead.
Box 2 is the quieter win. The words on the page become code E11.9 in ICD-10-CM, the diagnosis code set that CDC’s National Center for Health Statistics maintains for the United States, so the rating rules key off one code instead of a dozen ways a doctor might write diabetes.
What an Intelligent Document Processing Tool Leaves Undone
Extraction is mostly solved. What the tool hands back is a value with a score attached. A high score on a wrong value looks exactly like a high score on a right one.
NIST named it. Its generative AI risk profile from July 2024 calls it confabulation, meaning confidently stated but erroneous or false content. When researchers put GPT-4-Turbo with a retrieval system through FinanceBench in 2023, it answered wrongly or refused on 81% of questions about public company filings. Models are better now.
Fluency still proves nothing. So we leave the model alone at first and build around it, the same order a good AI pilot-to-production checklist puts ahead of any vendor contract.
- KeepThe extraction engine you already license, provided it clears a replay on your own files.
- AddA written schema, rules that compare pages, and a held state for anything unreadable.
- Decide laterWhether to replace anything. Replay results make that call.
Scored Against Decisions Your Team Already Made
We start with a replay. We take a batch of files your people have already decided, run them through the pipeline, and compare its answers with the ones on record, field by field first and then decision by decision.
Proof came from underwriting. At a specialty finance investor, Khurram’s team built an engine that read regulated medical files, split each record by body system, mapped the impairments and drafted a mortality rating for an underwriter to approve. Scored against ratings the underwriters had issued themselves, the engine agreed on 99.9% of decisions over a run of more than 50 million pages.
- field_agreementHow often each field matched the record, reported field by field, never as one average.
- decision_agreementHow often the pipeline reached the decision your team reached on the same file.
- exceptionsEvery miss, with the document, page and region it came from.
Nothing ships without it. The same report answers an investment committee, an external auditor or a reinsurer who wants to know how the model decided, and Khurram runs the same test on loan documents in his credit agreement replay. Life insurers hear the question from regulators as well, because the NAIC’s accelerated underwriting guidance, adopted in August 2024, has state regulators ask how a company validates its data and models for accuracy and reliability.
Four Document Families We Build Pipelines For
Recurring, read by hand today, and feeding a decision with money on it. That’s the whole filter.
Credit agreements and amendments
Defined terms, add-backs and covenant levels, pulled once and rechecked whenever an amendment lands.
Monthly borrower reporting
Compliance certificates, financials and borrowing base certificates, tied to the figures your credit team funds against. Asset-based lenders can go one layer deeper and put borrowing base verification for asset-based lenders underneath the certificate itself.
Insurance submissions and medical files
Hundreds of pages per applicant, sorted by body system and mapped before an underwriter opens the file.
Claim files, loss runs and census data
Third-party tables turned into one record per claim or plan member, with every gap marked.
Insurers have a regulator reason to care how this gets built. The NAIC’s model bulletin on insurers’ use of AI systems, adopted in December 2023 for state insurance departments to take up, expects a written program for AI that makes or supports regulated decisions, with data practices covering currency, lineage, quality and integrity. Its adoption map listed 25 states and the District of Columbia on board as of August 31, 2026. A replay report and a written rule library give that program something concrete to point at, and lightweight AI governance settles who signs off on them.
Funds usually meet it first at month-end, through our private credit data operations consulting, and Khurram has laid out what manual PDF handling costs a credit fund in his own numbers. For one document in particular, he has written up which borrowing base fields a machine gets wrong, and why. Carriers tend to meet it through life and annuity policy data first, and our insurance IT staffing desk covers the system roles around it.
Who Owns Intelligent Document Processing After Handover
Templates change. A borrower switches accounting systems, a lab redesigns its report, a broker starts sending loss runs as images, and rules that passed in March start missing in June.
Name the owners first. A data engineer holds the pipeline and its rule library, a seat we fill as a data engineering staff augmentation placement or a permanent ETL developer hire. When layouts shift, an NLP engineer or LLM engineer retunes extraction, while an integration engineer keeps the handoff into downstream systems working. Handwriting and badly scanned pages are computer vision engineer territory, and the glue between steps usually gets written by a Python developer.
Reviewers stay in-house. An underwriter or a credit analyst who already knows the files works the held fields, so judgment stays where it was. The schema itself is usually a data architect’s job. A data governance analyst tends to inherit the definitions, and some firms put a fractional head of data over the whole program on contract terms while permanent seats get filled.
Five Steps From One Document Type to a Live Decision
Each step leaves something you can inspect before the next one starts.
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1
Pick one document and one decision
A recurring document your team reads by hand today, and the single decision it feeds.
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2
Pull a batch you’ve already decided
Closed files plus the decisions made on them, which become the answer key for scoring.
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3
Write the schema and the rules
Field definitions, cross-page checks and a held state for unreadable values, agreed with the people who do the work.
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4
Replay and score
The pipeline reruns the closed batch, and agreement gets reported by field and by decision.
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5
Go live with owners in place
New files flow only after the replay clears and the engineer and reviewer seats are filled. For a program that already spent its budget, the diagnosis usually starts with the accuracy number.
Common Questions
What do intelligent document processing services include?
They include the work a software license leaves out, meaning a written schema, validation rules, a replay against decisions your team already made, and the people who run the pipeline afterward. KORE1 keeps your existing extraction tool whenever it clears that replay.
How is IDP different from OCR?
OCR turns an image of a page into text. It stops there. Intelligent document processing classifies the document, pulls named fields into a structure and checks them, so what comes out is data a system can act on instead of a transcript.
How accurate is intelligent document processing?
Only as accurate as a test on your own closed files shows. A vendor’s figure describes the vendor’s test documents, so we report agreement per field and per decision on yours, and handwriting and tables that run across pages usually score lowest.
We already pay for an IDP platform. Why would we need KORE1?
Because the platform extracts, and nobody owns the step from its output to your decision yet. We build the schema, rules and replay around the tool you have, keep it if the scores hold up, and recruit the engineer who maintains it.
How long until something works?
About four weeks for the first document type, replay included. Wider rollout then follows whichever documents scored best. Old backlogs come last.
What do we hand an auditor or reinsurer who asks how the model decided?
The replay report. It lists every field, the rule it had to pass and how often the pipeline matched decisions your team had already made, and each exception points back to a page they can open. Insurance examiners are getting a structured version of the same question, too. Twelve states are piloting the NAIC’s AI Systems Evaluation Tool in market conduct and financial exams from March through September 2026.
Does a data team need to be in place before we start?
No. The replay shows which seat you actually need, most often a data engineer, and KORE1 can place that person as a contractor or a direct hire when the time comes.
Bring One Document and the Decision It Feeds
Tell us which document your team still reads by hand and what gets decided from it. One is enough. You’ll get a straight answer on whether a replay earns its cost.
Start With One Document →