Reserv
Oct 2024 to nowSenior Backend Engineer, Data
I am the primary engineer behind Adjuster Copilot, Reserv's production large language model (LLM) agent for claims adjusters, working full-stack across four codebases. I built its suite of about 20 agent tools with human-in-the-loop approval, its conversation and memory management, and a safety and audit layer that validates every artificial intelligence (AI) generated message before it is sent and records every tool call. Adjuster Copilot doubled claims accuracy and gives adjusters back 40+ hours a month.
In 2026 I rebuilt the agent's knowledge retrieval as an agentic loop, raising retrieval accuracy by 64.3 percentage points on de-identified production fixtures, and shipped it behind a feature-flag ramp with a kill switch. I led a coordinated four-repository protocol migration for the agent stack and root-caused the production memory failures it exposed. I also built the company's generative AI evaluation framework and LLM observability from scratch, including a shared de-identified benchmark dataset and the tracing that measured production LLM usage for the first time.
On the data side, I built and then replaced the claim-document ingestion pipeline with an event-driven, idempotent design with dead-letter replay, which let us retire the original optical character recognition (OCR) integration. I own the pipeline that produces the bordereaux and loss-run reports that about 20 insurance carriers require, cut dbt model runtimes by 28%, reduced Docker image sizes by up to 76%, and built most of the team's Datadog alerting and the queue-depth-driven autoscaling that replaced manual capacity checks.
Adjuster Copilot. The claims adjuster product at Reserv. I support it on call and own its evaluation and observability. reserv.com