Chris Cordaro

AI strategy · architecture · delivery

Turning ambitious AI ideas into working systems.

I connect business strategy, product thinking, and hands-on engineering to take AI initiatives from opportunity through adoption.

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Proof

Outcomes, not slideware.

  1. <2 min

    Opportunity research, down from ~2 hours

    A RAG copilot grounded across 50,000+ federal solicitations, with hybrid retrieval and streamed, cited answers.

  2. ~6 min

    15-page intelligence reports, down from 8+ hours

    A four-phase multi-agent pipeline that researches, analyzes, writes, and reviews — with live progress the reader can watch.

  3. 60%

    Less manual analysis across a federal agency

    AI and predictive analytics for workforce planning at the DoD Inspector General, alongside a 10% cut in time-to-hire.

  4. 400+

    Users on a self-service analytics platform

    A modern-data-stack migration over 40+ sprints — 35% cost and scalability gain, 98% on-time delivery, and public dashboards cited by CNN, C-SPAN, and Congress.

Selected impact

Build. Transform. Scale.

Three stories that cover the full arc — from a blank repo, through an agency-wide change program, to leading the teams and platforms that carry it.

  1. Build

    Scout — an AI opportunity-intelligence platform, from schema to ship

    Federal business development runs on manual searching, duplicated data, and days of research per pursuit. I built the platform that replaces it.

    The challenge

    Opportunities surface across SAM.gov, USASpending, FPDS, and SBA with conflicting metadata and heavy overlap. Teams spent hours per pursuit re-reading solicitations and assembling competitive intelligence by hand — work that does not scale and rarely gets done well under deadline.

    What I did

    I designed and shipped the whole system alone: multi-source ingestion with four-tier deduplication, an AI composite score blending structured signals with vector similarity, a RAG copilot with hybrid retrieval and streamed citations, and a four-phase multi-agent pipeline that produces full competitive-intelligence reports. Underneath it sits a 14-persona agent workforce with persistent memory and coordination protocols, and a packaging contract that compiles agent products into skills, MCP servers, and plugins for three host CLIs.

  2. Transform

    Moving a federal agency from manual reporting to AI-assisted decisions

    Technology was never the hard part. Getting senior leaders, analysts, and program owners to change how they work was.

    The challenge

    The DoD Office of Inspector General ran on manual reporting and spreadsheet analysis. Analytics work was disconnected from mission priorities, workforce planning was reactive, and leadership had little confidence that data investments would change outcomes.

    What I did

    As Enterprise Business Innovation Manager I led the agency-wide, data-driven transformation: aligned the analytics strategy to mission priorities, secured senior-leadership sponsorship, and directed AI and predictive-analytics solutions for workforce planning. I ran the delivery program in Agile with Requirements Traceability Matrices tying every technical deliverable to a mission outcome, and drove the change management that moved teams onto self-service analytics. The same playbook now runs across the Military Health System — an AI-augmented evaluation framework for pharmaceutical supply-chain illumination, and executive communications recommending an OTA-funded pilot to ASD(HA).

  3. Scale

    Growing the teams, platforms, and public trust behind the work

    Impact compounds when the systems outlast the project and the team can carry them without you.

    The challenge

    Sustaining delivery across a multi-year federal program means growing the team, modernizing the platform under it, winning the next contract, and doing all of it without dropping the on-time record.

    What I did

    Over six years with the DoD Inspector General I directed a 15+ person team of data scientists, engineers, and BI analysts; led a modern-data-stack migration across 40+ sprints; built a self-service platform for 400+ users; and founded a Data Science Center of Excellence for methodology, governance, and mentorship. I designed and delivered UkraineOversight.gov, the public dashboards for oversight of federal Ukraine-related funding. Today I lead a 16-person team on the Health Readiness Policy & Oversight contract while running capture planning and Shipley-method proposal development for the next pursuits.

How I work

Strategy to execution, without the handoff gap.

Most AI initiatives fail between the deck and the deployment. I stay accountable across the whole arc, so the strategy is buildable and the build serves the strategy.

  1. Frame the opportunity

    Start with the mission or business outcome, the constraints that actually bind — funding, security, data rights, procurement — and a clear definition of what "adopted" means. If the value case does not hold, say so early.

  2. Architect for the real environment

    Choose the simplest architecture that can be secured, operated, and explained: FedRAMP, CMMC, and Zero Trust are design inputs, not afterthoughts. Prototype the risky part first so the plan is grounded in something that runs.

  3. Deliver in working increments

    Agile delivery with traceability from every deliverable to an outcome. I stay hands-on in the code and the data, so estimates are honest and the team ships working software rather than status reports.

  4. Drive adoption and measure it

    Change management, self-service, and executive communication are part of the deliverable. The work is done when people use it and the numbers move — time saved, decisions made faster, work that no longer needs doing.

Experience

Ten-plus years across federal, defense, and product.

Concise on purpose — the case studies above carry the detail.

  1. 2025 – Present

    Knowesis Inc.

    Senior Associate — Program Manager & AI Engineering Lead

    Leads a 16-person Military Health System contract while architecting and shipping production AI platforms end to end.

  2. 2024 – 2025

    DoD Office of Inspector General

    Enterprise Business Innovation Manager

    Led agency-wide, data-driven transformation and AI-enabled workforce planning with senior-leadership sponsorship.

  3. 2019 – 2024

    Knowesis Inc. — DoD Inspector General

    Program Manager / Technical Lead

    Directed a 15+ person analytics team, the modern-data-stack migration, and UkraineOversight.gov.

  4. 2018 – 2019

    Knowesis Inc. — FEMA / Defense Health Agency

    Solution Architect / Technical Lead

    Architected cross-platform federal systems on AWS during live disaster events and enterprise records modernization.

  5. 2013 – 2018

    Atkins (SNC-Lavalin) — FEMA

    Data Manager

    Built the enterprise data solutions behind $300M+ in FEMA funding coordination across 22 counties.

Download the full résumé →

Capabilities

Five ways I create value.

Grouped by the problem they solve, not by the tools involved. The tools change; the accountability does not.

  1. AI strategy

    Turn an ambition into a portfolio of buildable, fundable initiatives — with the value case, risks, and sequencing leaders can act on.

    Value cases · pilot design · executive communication · federal AI, FAR/DFARS, FedRAMP, CMMC

  2. Solution architecture

    Design AI systems that survive contact with real data, real security requirements, and real operations.

    Multi-agent orchestration · RAG and hybrid retrieval · MCP · Postgres/pgvector · Zero Trust · AWS, Azure, Cloudflare

  3. Product delivery

    Run delivery from roadmap to release: Agile programs, traceability, and the judgment to cut scope without cutting value.

    Agile / PMI-ACP · requirements traceability · 98% on-time delivery · Shipley capture and proposals

  4. Transformation and adoption

    Move organizations from manual work to self-service and AI-assisted decisions, and make it stick.

    Change management · self-service analytics · centers of excellence · leadership sponsorship

  5. Hands-on engineering

    Ship the thing myself when it matters — schema to deployment — so plans stay honest and teams have a working reference.

    Python, TypeScript, SQL · FastAPI, Next.js, React · Docker · CI/CD · Tableau, Power BI

Certifications PMI Agile Certified Practitioner (PMI-ACP) — 2022 · Tableau Certified Associate — 2019 · Data Science Professional Certificate, HarvardX — 2021 · DataWalk Technical Professional — 2022

About

Builder first. Leader by necessity. Curious on purpose.

I lead the way I want to be led: clear on the outcome, honest about tradeoffs, and close enough to the work to know when a plan has stopped matching reality. I have carried the delivery schedule and I have carried the pager, and I think the best leaders on AI programs are the ones who can still do both.

What motivates me is the gap between what AI could do for a mission and what actually gets adopted. Federal and defense work has taught me that the hard problems are rarely the models — they are trust, security, procurement, and change. I like those problems.

I build things when I am not being paid to: this site, its self-publishing daily AI briefing, and a steady stream of agent tools other engineers actually install. Raleigh, NC. Active SECRET clearance.

Contact

Let’s talk about what you’re trying to build.

Open to AI leadership, product, and architecture roles — and to conversations about ambitious AI initiatives that need someone who can take them from strategy through adoption.

chriscordaro@gmail.com
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