Ingest & Normalize
Pull clinical data, standardize, and de-duplicate at scale
- Pull notes/PDFs, encounters, and claims
- Standardize and de-duplicate at scale
- Process millions of records reliably
Retrospective Engine
Retrospective Risk Adjustmentwith Audit-Grade Evidence at Scale
Our retrospective risk adjustment engine finds undercoded HCCs while validating existing ones to maximize revenue and minimize audit risk.
Book a DemoWhy Martlet AI?
Proven technology that delivers measurable results at enterprise scale
Industry-leading performance
Validated by peer reviewed publications
Proven ROI
Measurable lift in throughput and captured value.
Scalable by design
High-volume workloads across millions of records.
Outcomes
Built for Trust and Accuracy
Martlet AI is designed for environments where compliance, precision and transparency are non-negotiable.
More captured value
Find supported, under coded HCCs with evidence packaged for action.
Less wasted review time
Page-level evidence and provenance reduce “PDF hunting” and speed decisions.
Stronger audit posture
MEAT-aware findings help prevent overcoding and improve RADV readiness.
Higher trust output
Every recommendation ships with "why" so reviewers can validate in seconds.
Built to run at scale
Designed for high-volume retrospective workloads, process millions of records with consistent output quality.
Deployed inside your environment
Cloud/VPC/on-prem options so PHI stays under your control no external data egress required.
How it works
Retrospective Coding Workflow
Ingest charts → validate MEAT-backed diagnoses with evidence → optimize retrospective risk adjustment workflows → recapture missed HCCs and resolve issues
Detect & Prioritize
Identify gaps with MEAT evidence, ranked by impact
- Identify HCC gaps + validate existing codes
- Link each finding to MEAT evidence (reduce overcoding risk)
- Prioritize chases by impact (RAF lift / yield)
Review & ROI
Fast reviews with linked docs and continuous improvement
- Reviewer UI built for speed (page-level references)
- Push provider suggestions with links to historical docs
- Improve future suggestions with your feedback and standards
