CodmanAI: Medical Coding Agent
Insurance claims processed faster, coded accurately, and closed with confidence.
CodmanAI
Case Study · 2025
About this product
An AI-powered insurance claims processing platform that extracts critical medical information from claim documents, auto-generates accurate ICD-10 and CPT codes, and delivers a 40% faster, significantly more accurate claims workflow — cutting manual effort, reducing errors, and protecting insurers from costly coding mistakes.
Timeline
14 weeks
Category
AI / ML
Delivered
2025
Stack
Product Preview
Why it works
Measurable impact, by design
40%
Faster Claim Processing
document receipt to code submission
20%
Fewer Coding Errors
AI consistency over manual interpretation
15%
Higher Code Accuracy
NLP captured clinical nuances humans missed
2×
Adjuster Daily Throughput
same team, double the claims volume handled
Overview
The situation
Insurance claims processing is one of the most document-heavy, error-prone, and operationally expensive workflows in the industry. At CodmanAI, adjusters were manually reading through dense medical documentation, interpreting clinical language, and assigning ICD-10 and CPT codes by hand — a process that was slow, inconsistent, and financially risky. A single miscoded claim could mean an incorrect payout or a compliance violation. We built an AI claims processing platform that changed all of this: intelligent document analysis, automated medical code generation, adjustable confidence controls, and strict compliance guardrails — giving CodmanAI's team a system that moved faster than any human workflow while being more accurate than any manual process.
Challenge
What we had to solve
Medical coding for insurance claims sits at the intersection of clinical language, regulatory compliance, and financial accuracy — three domains where errors are not merely inconvenient but costly. The AI had to understand the nuances of medical terminology well enough to assign the correct ICD-10 and CPT codes from unstructured clinical documents, not templated inputs. It also had to operate within strict guardrails: the system could not generate speculative codes or hallucinate diagnoses. Every code assignment had to be traceable to the source document, and adjusters needed the ability to review, adjust, and override AI outputs with full audit trail support — maintaining human control without sacrificing the speed benefit.
What it does
Core capabilities
Medical Document Intelligence & Entity Extraction
CodmanAI ingests claims documents in any format — structured forms, narrative clinical summaries, scanned PDFs — and processes them through a medical NLP pipeline that identifies diagnoses, procedures, symptoms, medications, dates of service, and provider references from unstructured text. Every extracted entity is linked to its source passage and page number, giving adjusters a traceable path from every AI output back to the original document. What previously required an adjuster to read a 40-page clinical record line by line now surfaces the relevant medical information in seconds.
Intelligent ICD-10 & CPT Code Assignment
Extracted medical entities are mapped to ranked ICD-10 and CPT code assignments, each accompanied by a confidence score and the source passage that supports the assignment. The system presents the top-ranked code recommendation with full reasoning transparency — adjusters see not just what the AI recommends, but why and how confident it is. A configurable confidence threshold controls which assignments auto-approve versus route to mandatory human review, giving operations teams precise control over the automation-to-oversight ratio without sacrificing speed.
Adjustable AI Controls & Human Override
CodmanAI was built with human oversight as a first-class design principle, not a compliance afterthought. Adjusters can accept the AI's top recommendation, select from alternative ranked suggestions, or override entirely — with every action captured in a full audit trail. Confidence level displays give adjusters immediate visibility into the AI's certainty before accepting any code, and every override feeds back into the model's continuous improvement loop. The result is a system that gets faster and more accurate the more it's used, while keeping adjusters in genuine control of every submission.
Compliance Guardrails & Full Audit Trail
Every code assignment in CodmanAI is subject to strict guardrails that prevent unsupported outputs from reaching the submission layer. No code is auto-approved without a traceable source passage in the original document — eliminating hallucinated assignments at the architectural level. Every adjuster action, AI recommendation, confidence score, and code change is logged to an immutable audit trail, giving compliance teams complete visibility into every claim decision. The system was designed to meet the documentation and traceability requirements of insurance regulatory frameworks from the ground up.
Outcomes
Claims processed faster, coded accurately, closed with confidence.
40%
Faster claim processing
from document receipt to code submission
20%
Reduction in human coding errors
AI consistency replaced manual interpretation variance
15%
Improvement in code assignment accuracy
NLP captured clinical nuances manual review missed
100%
Traceable AI output
every code linked to its source document passage
2×
Adjuster throughput per day
same team handling double the claims volume
14wks
Delivered on schedule
discovery to production-ready AI claims platform
Eleven Softwares showcased exceptional expertise and professionalism, delivering a seamless AI solution that transformed our claims processing. It has significantly improved accuracy, reduced costs, and accelerated workflows, making a measurable impact on our operations.
David V.
CEO, CodmanAI
Want results like these?
Let's talk about what we can build together.