PROFILEDM INC. · ENTERPRISE TELECOM · 2025–PRESENT
Helping teams review field evidence
with AI.
How we brought AI into a real inspection process—without treating every model response as a final decision.
From field evidence to
reviewable decisions.
01 / THE PROBLEM
Reviewers need evidence, not just an answer.
After telecom infrastructure upgrades, field crews submit photos and documents for review. Each submission must meet defined criteria, and a useful decision needs to be traceable to the evidence provided. The team wanted to reduce routine review effort without removing human judgment from uncertain cases.
02 / THE APPROACH
Rules where possible. AI where useful.
We combined deterministic checks with multimodal Claude validation and human escalation. Rule-eligible checks avoid model inference; negative, uncertain, or higher-risk results go to reviewers. Event-driven AWS services orchestrate the process, with LangGraph used for stateful validation workflows in later iterations.
03 / MY CONTRIBUTIONS
Where I focused my engineering work.
Making model decisions verifiable
I worked on rubric-driven prompts, structured outputs, and evidence-reference validation. We found responses that passed schema checks but cited evidence never submitted. I implemented a deterministic check to catch these references and route mismatches for human review.
Evaluating changes before release
I built an evaluation framework around 250 reviewer-labeled cases. It tracked reviewer agreement, false rejections, and citation validity, and was used to gate eight production releases.
Connecting implementation to real workflows
I designed workflow contracts and routing logic and worked with client reviewers and engineers during shadow testing, reviewer-assistance rollout, and selective automation. Deterministic logic handled approximately 35% of evidence checks, leaving nuanced cases for model reasoning or human review.
04 / IN PRACTICE
A production system with measurable checks.
The workflow supports thousands of inspections each month, with a reviewer-labeled dataset used to assess model behavior before releases. Human review remains part of the process for cases outside the automation boundary.
Agreement was measured offline against reviewer labels; it does not represent end-to-end production decision accuracy.
Generalized case study for portfolio use. Project details and metrics should be shared publicly only when permitted by the relevant employer or client.