Stories · Healthcare & Life Sciences
A six-month claims cycle, replaced in twelve weeks : by a firm built around agents.
Two things had to be agent-native for KlearTrust to land production HIPAA-compliant claims intelligence in twelve weeks: the platform we built, and the firm that built it. The first is what reaches the headlines. The second is why the timeline was possible at all.
Baseline
6 months manual claims-review cycle. Two prior automation pilots stalled at PHI/audit handoff.
Outcome
12 weeks to production HIPAA-compliant agentic claims platform. Passed first-cycle audit.
Time to Value
12 weeks
The Challenge · 01
Two pilots had already stalled. A third “pilot” was not an option.
KlearTrust's claims-review process was manual and slow. Each claim required clinical-rule application, document review, and clinician sign-off. The six-month cycle bottlenecked throughput and patient outcomes.
The team had run two prior automation attempts : each one stalled at the handoff between strategy and implementation, because PHI handling and audit-defensibility added scope that the staffing model could not absorb.
A traditional SI proposal in front of KlearTrust would have proposed another six-month engagement with an analyst tier mapping the clinical workflow by hand. The CFO's appetite for that path was gone.
How We Delivered · 02
The engagement was agent-native on both sides of the delivery.
The work that would have consumed a four-month analyst tier in a traditional SI engagement : clinical-document ingestion, PHI-safe retrieval, audit-log scaffolding, evaluation harness : ran in days against Aedeon. That collapse is what funded the twelve-week date.
Aedeon's Lane
- •PHI-safe document ingestion across KlearTrust's claims sources
- •Clinical-knowledge retrieval with grounding and lineage capture
- •Audit logging captured at decision time, not reconstructed after
- •Evaluation harness covering regression and behavioral acceptance
- •Observability instrumentation handed over to KlearTrust's operations team
Forward-Deployed Engineers' Lane
- •Clinical-rule architecture and authority boundary for the agent
- •Human-in-the-loop design for edge cases clinicians had to own
- •Cutover plan into KlearTrust's production claims workflow
- •HIPAA and internal-audit defense at the architecture level
- •Customer-signed acceptance on a date both teams committed to
In Production · 03
The outcome KlearTrust runs on today.
Claims now process with auditable agent decisions. Clinicians sign off on the edge cases that actually need clinical judgment, not on the 80% the agent can clear inside its authority.
The platform passed HIPAA and internal audit on first review : because audit defensibility was an architectural property of the system, not a separate workstream.
Time-to-decision dropped from months to days. The capacity that was previously stuck reviewing every claim is now available for the patient-outcome work KlearTrust actually wants to invest in.
Compliance
HIPAA-aligned PHI handling, by construction. Audit evidence captured at decision time. Human-in-loop design for clinical edge cases. Passed HIPAA review on first cycle.
“The auditor signed off on the architecture, not on a separate test. That is what an agent-native firm ships in healthcare.”
Why This Matters · 04
Two pilots failed because the delivery model was wrong.
The agent-native AI you can stand up internally still leaves the audit, governance, and cutover work for a delivery team to absorb. That is where most enterprise AI initiatives stall : not at the model, but at the delivery model.
KlearTrust's third attempt landed because the firm running it was structured for the audit and cutover work, not just the agent build.
If your AI program has stalled at the same handoff KlearTrust's first two pilots stalled at, the model is not the problem : the firm is.