Document Review
AI reads the full set of claim documents, summarizes them, extracts key facts and structured data, checks against policy rules, and suggests a decision with an explanation. The agent reviews and decides.
Document-heavy review processes — insurance claims, contract assessments, regulatory submissions, audit cases — follow a common pattern. Someone needs to read a set of documents, extract the relevant facts, check those facts against rules or criteria, form a recommendation, and hand the decision to someone with authority to act on it. The reading and extraction work can represent the majority of the total time, even when the actual decision takes only minutes.
In insurance claim processing, for example, each claim arrives with a collection of documents: a claim form, supporting evidence, images, customer contract, and policy terms. An agent has to read through all of it, pull out the facts that matter — dates, amounts, covered items, stated cause — cross-reference them against the applicable policy, and then write up a summary and recommendation before the case can move to a decision.
This workflow uses AI to handle the reading, summarization, extraction, and policy check automatically. The AI processes all documents the moment the case is opened, presents the agent with a structured summary, answers follow-up questions, and suggests a decision with a clear explanation. The agent reviews the summary, challenges it if needed, and makes the final call. The reading work is removed; the judgment stays with the person.
Theoretical estimates based on a team processing 80 document-heavy cases per month (insurance claims, contract reviews, or similar). The manual baseline assumes 45–90 minutes of reading and extraction work per case before the actual assessment can begin, with full case turnaround of 1–3 days.
The majority of saved time comes from eliminating the manual reading and extraction phase. In the AI-assisted process, the agent opens a case and immediately sees a complete structured summary — facts extracted, policy checked, decision suggested. Their ~8 minutes goes on reading the summary, asking any follow-up questions, and making the final call. Case turnaround drops from 1–3 days to same-day for straightforward cases, because the agent no longer waits for a free slot to read through the documents themselves.
In the manual process, a claim arrives and is registered in the system. The agent opens the claim dashboard, locates the case, and begins by reviewing the claim data alongside the customer and contract records. They then read through each attached document in sequence — the claim form, any supporting materials, photographs, and the applicable policy document — extracting facts manually as they go.
This reading phase is the most time-consuming part. An agent working through a claim with several documents and images can spend thirty to sixty minutes just reading and noting down the relevant information before they can begin the actual assessment. If any information appears to be missing, they must request it and wait, which adds days to the process.
Once the reading and extraction are complete, the agent checks the facts against the policy terms and company rules, prepares a written summary and recommendation, and makes an approve or reject decision. Approved claims are emailed to the finance department; rejected claims require a written explanation. The full process from claim receipt to decision typically takes one to three days depending on document volume and whether any back-and-forth with the claimant is needed.
In the AI-assisted workflow, the agent opens a claim in the dashboard and clicks to proceed. The AI immediately loads all associated documents, images, contract data, and customer records and begins processing. Within seconds it produces a structured summary: what happened, what is claimed, what amounts are involved, what the relevant policy terms say, and what the key facts are that determine whether the claim is covered.
The agent reads the summary and can ask follow-up questions directly — "what does the policy say about exclusions for this type of event?" or "is there any prior claim history from this customer?" — and the AI answers using the actual document content, citing the relevant sections. The agent does not need to search through the documents themselves for these answers.
Once the agent is satisfied with their understanding, the AI checks the extracted facts against the company's policy rules and coverage criteria, and suggests a decision — approve or reject — with a plain-language explanation of why. The agent reviews the suggestion, agrees or overrides it based on their own judgment, and records the final decision. If approved, the finance department is notified automatically. If rejected, the explanation is logged and sent.
The agent's time is spent entirely on judgment: reading the summary, asking questions if something is unclear, and making the decision. They do not read raw documents, they do not extract facts manually, and they do not write the summary. An experienced agent can typically review and decide a straightforward claim in under ten minutes using the AI-assisted interface.
The diagram shows the agent's document processing and decision flow. After automatically loading and summarizing all documents, the agent enters a Q&A loop where the agent can ask follow-up questions before the AI suggests a final decision. Orange nodes are human-in-the-loop pause points where the agent controls what happens next.
flowchart TD
START([START]) --> load_context
load_context["**load_context**\nFetch claim + documents\nMark claim in review"] --> summarize_docs
summarize_docs["**summarize_docs**\nLLM: summarise all attached docs"] --> extract_data
extract_data["**extract_data**\nLLM: structured extraction\n(parties, amounts, flags)\nPersist to Django"] --> present_summary
present_summary["**present_summary**\nPost summary + red-flags to chat"] --> await_qa
await_qa{{"**await_qa** ⏸\ninterrupt — Q&A gateway"}}
await_qa -- "action = ask" --> answer_question
answer_question["**answer_question**\nLLM: answer the question\nAppend to qa_history"] -- "loop back" --> await_qa
await_qa -- "action = proceed" --> check_policy
check_policy["**check_policy**\nFetch policy docs from Django\n(product-scoped + global)"] --> suggest_decision
suggest_decision["**suggest_decision**\nLLM: recommend approve / reject\nPersist recommendation to Django"] --> await_final_decision
await_final_decision{{"**await_final_decision** ⏸\ninterrupt — final decision gateway"}}
await_final_decision -- "decision = approved" --> approve_and_email
await_final_decision -- "decision = rejected" --> reject_and_record
approve_and_email["**approve_and_email**\nLLM: draft finance email\nPATCH claim approved"] --> END_NODE([END])
reject_and_record["**reject_and_record**\nCreate internal record\nPATCH claim rejected"] --> END_NODE
style await_qa fill:#f5a623,color:#000
style await_final_decision fill:#f5a623,color:#000
style START fill:#4caf50,color:#fff
style END_NODE fill:#e53935,color:#fff
The final result is a review interface where the agent opens a case and immediately sees a complete, structured summary — not raw documents. The summary includes the key facts, coverage assessment, and the AI's suggested decision with a clear explanation. The agent reads it, asks any clarifying questions through the same interface, and makes a decision.
For straightforward claims, this takes under ten minutes. For complex cases — those with unusual circumstances, unclear policy coverage, or conflicting evidence — the AI's summary still saves significant time, because the agent starts from an organized picture rather than from raw documents, and can ask specific questions instead of re-reading pages of text.
When the agent overrides the AI's suggestion, they record a brief reason. This data builds up over time and can be used to improve the AI's future suggestions for similar case types, without any additional manual work from the team.
The process that previously took one to three days now typically completes on the same day. The agent's capacity for handling cases increases, the quality of each review is more consistent, and the audit trail — documents read, facts extracted, policy checked, decision made — is recorded automatically for every case.