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NavidBroumandfar/README.md

Navid

NAVIDBR Applied AI Systems

AI ideas are easy. Working systems are harder.

Website Work Records LinkedIn

Business -> Systems -> Data -> AI -> Products

I work on applied AI systems where the difficult part is not the model alone. The work starts with the business workflow, source material, evaluation boundary, and evidence needed before an AI or product surface should be trusted.

navidbr.me is the canonical public layer for identity, notes, work records, and routing. GitHub holds source evidence for selected public proof records.

Public Focus

Layer Practical question
Workflow What repeated decision, document flow, or handoff actually needs support?
Source preparation What data, record, trace, or citation must exist before AI can help?
Evaluation What behavior should be tested before a system is trusted?
Boundaries What should be refused, escalated, or left to a human?
Product path What is proven publicly, what is not proven, and what should happen next?

Public Proof Records

Each record links a public NAVIDBR work page to its source repository. The work pages state the claim and boundary; the repositories provide inspectable source evidence. These are public proof records, not client claims or production deployment claims.

NAVIDBR work record Source evidence Public signal
Databricks CaseOps Lakehouse Repository Source preparation, provenance, validation, evaluation, and AI-ready handoff records.
AWS Bedrock CaseOps Control Tower Repository Grounded retrieval, citations, structured outputs, validation, and escalation boundaries.
Agent Behavior Evals Lab Repository Approval gates, refusal boundaries, uncertainty handling, traces, and quality gates.
E-commerce Purchase Intention MLOps Repository Reproducible ML workflow, evaluation, local serving, tests, and model-card notes.

Working Pattern

Business pressure
  -> workflow and owner
  -> source shape and constraints
  -> evaluation and boundaries
  -> AI or product surface
  -> public proof before product claims

Reading Paths

Contact

Popular repositories Loading

  1. agent-behavior-evals-lab agent-behavior-evals-lab Public

    Policy-mapped evaluation lab for AI assistant behavior: approval gates, refusal boundaries, uncertainty handling, tool-use grounding, traces, and quality gates.

    Python 1

  2. Agentic-Excel-Review-Template Agentic-Excel-Review-Template Public

    A fully modular, open-source template for building agentic AI workflows on top of structured Excel processes. Includes Excel ingestion, LLM reasoning (local or API), RAG document retrieval, JSONL l…

    Python 1

  3. NavidBroumandfar NavidBroumandfar Public

    Public GitHub profile for Navid and NAVIDBR Applied AI Systems.

  4. statagent statagent Public

    Statistical analysis toolkit for probabilistic modeling, hypothesis testing, and Bayesian inference

    Python

  5. ai-decision-traceability-engine ai-decision-traceability-engine Public archive

    Reference implementation for governed LLM decision traceability and replay

    Python

  6. bedrock-caseops-control-tower bedrock-caseops-control-tower Public

    Grounded AWS Bedrock document review proof with retrieval, validation, citations, structured outputs, and escalation boundaries.

    Python