ENTERPRISE AI ARCHITECT · FOUNDER · TECHNICAL OPERATOR

MICHAELDI IACOVO

I architect. I build. I lead enterprise AI into production.

I build the systems — and the operating models — organizations need to put enterprise AI into production. Twenty years spent taking responsibility for larger and larger layers of the stack: infrastructure, cloud, enterprise architecture, production AI, and now the control planes and operating models that decide whether AI actually ships. Not just diagrams. Systems, operating models, and production outcomes.

20+
Years Enterprise Tech
69%
Federal Cost Reduction
10x
Processing Speed
30M+
Docs/Year AI-Processed
Talk Enterprise AI See the Work
The Trajectory
FROM INFRASTRUCTURE TO ENTERPRISE AI

My career stopped being about knowing more technologies a while ago. Each step took responsibility for a larger layer of the system — and the question I own kept getting bigger.

Infrastructure
→
Cloud & Security
→
Enterprise Architecture
→
Production AI
→
Founder / 0→1
→
Operating Models
→
Enterprise AI Technical Leadership
Can I make the infrastructure work?
Can I architect the platform?
Can I solve the customer's business problem?
Can I put AI into production under real enterprise constraints?
Can I set the technical direction for the organization — and build the operating model around it?
What I'm Building
SYSTEMS BUILT FROM ZERO
Built From Zero
BLOC mascot
BLOC
// FLAGSHIP
GOVERNED AI EXECUTION PLATFORM
AI at build time. Determinism at runtime.

A control plane for enterprise AI. BLOC lets probabilistic systems reason where reasoning creates value, and hands execution to deterministic systems where reliability and accountability matter. Agents get bounded authority, systems of record stay protected, and every important action leaves a receipt.

→ REASONING LAYER
Model-agnostic routing across any model — local or frontier. AI does the thinking at build time — planning, drafting, deciding — on whichever model fits the job, the budget, and the data-sensitivity.
→ EXECUTION LAYER
Step Functions orchestration and Lambda execution enforce a deterministic runtime. Human approval boundaries gate high-risk operations. Systems of record are never at the mercy of a model.
→ GOVERNANCE LAYER
MCP governance, identity attribution, Git-based policy, and runtime cost controls. Every important AI action produces a receipt: who initiated it, what policy applied, what model ran, what it touched, what it cost.
Step Functions
Lambda
Model-Agnostic Routing
Local + Frontier LLMs
MCP Governance
Identity Attribution
Git-Based Policy
Runtime Cost Controls
CRES mascot
CRES
// 0→1 PRODUCT
CAREER REBRAND & ENHANCEMENT SUITE
View Live Site →

An AI-powered career platform that connects job seekers with recruiters through intelligent resume optimization, interview preparation, and a two-sided talent marketplace. Built end-to-end — architecture, backend, frontend, AI integration, billing, and deployment.

→ FOR JOB SEEKERS
AI resume optimization with ATS scoring. Mock interviews with real-time feedback. Career coach agent that knows your full profile. Opt-in talent marketplace with privacy controls.
→ FOR RECRUITERS
Semantic candidate search via vector embeddings. Pipeline management with AI copilot. Job board with public careers pages. Smart briefings and automated outreach drafting.
→ THE PLATFORM
Two-sided marketplace where optimized candidates become searchable supply. Each side makes the other more valuable. Stripe billing with subscription management.
React / TypeScript
AWS Lambda
DynamoDB
LLM APIs
Pinecone
Step Functions
Cognito
Stripe
S3
WebSockets
Production Signal
THE NUMBERS, IN CONTEXT
69%
Cost reduction in federal document processing via AWS-native re-architecture at the U.S. Department of Veterans Affairs.
300s→30s
Medical record processing time cut 10x using AI/ML on AWS — directly improving care-delivery speed for Veterans.
160K+
Staff-hours returned annually across thousands of users nationwide (~13K/month) — roughly 77 FTE-years of manual effort eliminated. Per-user savings that compound at national scale.
30M+
Documents processed annually through AI-powered automation pipelines built on serverless cloud infrastructure.
30K+
Enterprise users supported with secure backend and identity-management systems at the State of Michigan.
TOP 5%
Nationally ranked in the Federal AI Tech Sprint for GenAI tooling innovation in government healthcare automation.
AI-Native Delivery
THE METHOD
Operating Models

Putting AI agents into the software delivery lifecycle without losing accountability — and moving an organization onto it. Long-form, vendor-neutral, and written from delivery work, not from a slide.

Operating Model // 9 min read
THE DELIVERY CONTRACT

Generated code is cheap. Merged code isn't. A method for putting AI agents into the software delivery lifecycle without losing accountability — responsibility boundaries, bounded delegation, stop conditions, deterministic evidence, and human approval.

READ MORE →
Adoption // 9 min read
MOVING AN ORGANIZATION

Training teaches people. Rollout changes an organization. A model for turning AI training into real organizational capability — rollout mechanics, internal capacity, and outcomes measured in business results, not seat counts.

READ MORE →
Playbook // Enterprise AI
AI-NATIVE DELIVERY PLAYBOOK

The implementation layer: readiness assessment, reference architecture, governance, structured intake, enablement, outcome contracts, economics, and quality-adjusted measurement.

ASK ME ABOUT THIS →
Architecture // 8 min read
STOP THROWING COMPUTE AT BAD ARCHITECTURE

I've watched organizations spend $40,000 a month on AWS trying to solve a problem that a 15-minute code review would have caught. More instances. Bigger nodes. Wider load balancers. You don't need more capacity — you need to fix what you built.

READ MORE →
Systems I've Shipped
CASE FILES
Shipped Into Production
01 //
THE $1M DOCUMENT PROCESSING CRISIS
U.S. DEPT. OF VETERANS AFFAIRS
+
⚠ THE PROBLEM
The VA was burning nearly $1M per year processing medical records and documents on legacy infrastructure. Processing times were crippling. Veterans' claims were delayed. Staff was drowning in manual workflows.
◈ SYMPTOMS
300-second average processing time per document. Massive manual review bottlenecks. Compliance reporting was slow and error-prone. Healthcare outcomes were tied to paperwork throughput.
→ WHAT I DID
Designed and deployed AI/ML on AWS — migrating to a Textract + Lambda serverless architecture. Built automation pipelines that eliminated manual stages. Re-architected the full processing flow from intake to output.
✓ OUTCOME
Processing time dropped from 300s to 30s. Cost reduced 69%. ~160K staff-hours returned annually (~13K/month across thousands of users) — roughly 77 FTE-years of manual effort eliminated. Top 5% nationally in the Federal AI Tech Sprint.
RESULT: ~$690K ANNUAL SAVINGS // 10x SPEED INCREASE // TOP 5% NATIONAL AI INNOVATION RANKING
02 //
AI TOOLS THAT SAVED THE SALES CYCLE
STACKHAWK // DEVSECOPS
+
⚠ THE PROBLEM
Sales and Customer Success teams were spending enormous time on repetitive documentation — call summaries, success plans, POV briefs. Deal velocity was suffering. Institutional knowledge lived in people's heads.
◈ SYMPTOMS
Hours lost per week on manual Gong transcript review. Inconsistent MEDDICC capture. POV documents took days. CS success plans were inconsistent and slow to produce.
→ WHAT I DID
Built an LLM-powered internal toolsuite: Callsheet (Gong → Salesforce-ready MEDDICC insights), Successplan (auto-generated CS account plans), Solutionbrief (pre-sales POV docs), Stacktrace (AI debug assistant). Also built HawkAuth, which evolved into a customer-facing feature.
✓ OUTCOME
Dramatically reduced documentation burden across Sales, CS, and SA teams. HawkAuth evolved from internal tool to customer-facing product feature. Faster time-to-value for customers. Deal velocity improved.
RESULT: 4 AI TOOLS SHIPPED // INTERNAL TOOL → PRODUCT FEATURE // SALES CYCLE VELOCITY INCREASED
03 //
FEDRAMP COMPLIANCE OR WE LOSE THE CONTRACT
VETSEZ // FEDERAL
+
⚠ THE PROBLEM
Federal contracts require FedRAMP compliance. The hybrid-cloud architecture wasn't there. Security controls were inconsistent. IAM was fragmented across systems. One audit failure = contract loss.
◈ SYMPTOMS
Identity sprawl across cloud providers. No unified access-control framework. Kubernetes clusters without proper security boundaries. Compliance documentation was manual and error-prone.
→ WHAT I DID
Delivered FedRAMP-compliant hybrid-cloud solutions integrating secure identity and access controls. Implemented Kubernetes with proper security posture. Built Zero Trust boundaries across the stack.
✓ OUTCOME
FedRAMP compliance achieved and maintained. Contract secured. Architecture served as the blueprint for future federal deployments across the organization.
RESULT: FEDRAMP AUTHORIZATION ACHIEVED // CONTRACT SECURED // REPEATABLE COMPLIANCE BLUEPRINT
04 //
PRISON SYSTEM SECURITY BREACH RISK
STATE OF MICHIGAN // MDOC
+
⚠ THE PROBLEM
Michigan Department of Corrections ran a full penetration test. The results were not good. Critical vulnerabilities in offender-accessible systems. TLS 1.0 still live. Authentication framework was weak and fragmented.
◈ SYMPTOMS
Pen test findings across multiple attack surfaces. Outdated encryption standards across all systems. No unified SSO/SAML framework. Identity perimeter was effectively non-existent.
→ WHAT I DID
Served as Blue Team lead for full remediation. Designed and executed a TLS 1.0 deprecation plan across all state systems. Implemented SSO, OAuth, and SAML unified authentication. Hardened all offender-accessible infrastructure.
✓ OUTCOME
All pen test vulnerabilities remediated. TLS 1.0 fully deprecated across a 30K+ user environment. Unified authentication framework deployed. State and federal compliance standards met.
RESULT: FULL PEN TEST REMEDIATION // 30K+ USERS SECURED // STATE/FEDERAL COMPLIANCE ACHIEVED
05 //
HARDWARE TURNAROUND: 2 DAYS TO 20 MINUTES
JACKSON COUNTY, MI
+
⚠ THE PROBLEM
Every time a new machine needed to be deployed or reimaged, it took two full days. There was no standard process, no automation, no repeatable workflow. IT was a bottleneck to every department.
◈ SYMPTOMS
Manual, inconsistent hardware provisioning. Staff waiting days for machines. No documented process. Active Directory was disorganized. File-share permissions were unaudited and risky.
→ WHAT I DID
Built a standardized, automated provisioning process. Restructured Active Directory and GPOs. Audited and remediated file permissions and licensing. Virtualized all physical servers. Elevated all processes to current industry standards.
✓ OUTCOME
Hardware turnaround time dropped from 2 days to 20 minutes. Clean, secure, repeatable process. Infrastructure fully virtualized. Organization running at current industry standards.
RESULT: 144x FASTER PROVISIONING // FULL INFRASTRUCTURE VIRTUALIZATION // ZERO-DRIFT PROCESS
How I Think About Enterprise AI
MY ENTERPRISE AI THESIS

Seniority at this level isn't tool count. It's having defensible principles that survive contact with production. These are mine.

PRINCIPLE 01
PROBABILISTIC WHERE IT HELPS, DETERMINISTIC WHERE IT MATTERS
AI should reason where reasoning adds value, and execute deterministically where reliability and accountability are non-negotiable. This is the principle BLOC is built on.
PRINCIPLE 02
AGENTS DON'T GET UNCONSTRAINED AUTHORITY
Writes, high-risk operations, and policy boundaries over systems of record need explicit controls. Delegation is bounded, not blind.
PRINCIPLE 03
EVERY IMPORTANT AI ACTION LEAVES EVIDENCE
Who initiated it, what policy applied, what model ran, what system it touched, what it cost, what the outcome was. No receipts, no trust.
PRINCIPLE 04
MODEL CHOICE IS AN ECONOMIC DECISION
Latency, reliability, capability, cost, and vendor strategy are all part of the architecture — not just benchmark scores.
PRINCIPLE 05
POCS EXIST TO RETIRE UNCERTAINTY
Prototype to answer a real technical or business question on the path to production. Prototyping for theater is waste.
PRINCIPLE 06
GOVERNANCE ENABLES, IT DOESN'T BLOCK
Good governance creates clear boundaries and accountability so responsible deployment can happen. It is not a committee that stops work.
How I Operate
PROBLEM → VALUE
FIND THE REAL PROBLEM
Start from business outcome and constraint. Separate the actual problem from the technology that was requested.
DESIGN THE SYSTEM
Architecture, data, integration, models, security, governance, latency, reliability, and cost — decided together, not bolted on.
PROVE THE UNCERTAIN PARTS
Prototype only where real uncertainty lives. Use it to answer the question, then move on.
BUILD THE PRODUCTION PATH
Runtime, observability, failure handling, approval boundaries, auditability, and clear operating ownership.
MEASURE VALUE
Did cycle time improve? Did cost drop? Did adoption happen? Did the business outcome land? Was it worth building?
Pattern Recognition
PATTERNS I WATCH FOR

The failure modes I look for first when I walk into an enterprise AI program.

🤖
AI POC PURGATORY
// THE TRAP: Endless pilots that never ship
THE FIX Most orgs have five AI pilots and zero production systems. The gap isn't the model — it's integration, data, security review, and change management. Getting AI from demo to deployment is the actual work.
🔥
COMPLIANCE AS AN AFTERTHOUGHT
// THE TRAP: "We'll add governance later"
THE FIX In regulated environments, retrofitting compliance costs 5–10x more than designing it in. FedRAMP, Zero Trust, approval boundaries — architecture decisions, not sprint tickets.
⚡
MONOLITH DISGUISED AS MICROSERVICES
// THE TRAP: Same deployment, smaller containers
THE FIX Kubernetes scales a bad architecture — it doesn't fix it. If every "service" shares one database and fails together, it's a distributed monolith. Domain boundaries first, containers second.
Technical Leadership
BEYOND IMPLEMENTATION

My job increasingly isn't to personally implement every component. It's to create enough clarity that a strong team knows what outcome matters, what architecture applies, where the boundaries are, and how quality will be measured.

// Executive
EXECUTIVE ALIGNMENT
Translate technology into risk, cost, opportunity, and business value the C-suite can act on — without dumbing it down or drowning them in detail.
// Architecture
ARCHITECTURE OWNERSHIP
Set technical direction, decision principles, system boundaries, and the quality bar — then hold the line as the system meets reality.
// Enablement
ENGINEERING ENABLEMENT
Give engineers enough context and guardrails to execute with judgment — without micromanaging implementation.
// Customer
CUSTOMER LEADERSHIP
Build trust across engineering, security, procurement, legal/compliance, and executive stakeholders — the whole buying committee, not just the technical seat.
Capabilities
THE STACK

Judgment over keyword volume. The homepage stays high-level; the full stack is one click away.

Technical depth — view full capabilities ▸
AI Systems
LLMs (Multi-Model)
RAG Architecture
Vector DBs / Pinecone
Agents
MCP
Model Selection
Evaluation
Prompt Engineering
Cloud & Runtime
AWS
Azure
Lambda / Serverless
Step Functions
Kubernetes / EKS / AKS
Governance & Security
IAM
FedRAMP
Zero Trust
SAML / SSO
Approval Boundaries
Auditability
Enterprise Delivery
Architecture
Executive Discovery
Technical Strategy
DevSecOps
Productionization
Adoption
Value Measurement
SAFe 5
Advisory & Engagements
WORK WITH ME

The headline is the work above. But if you have a specific problem to move, these are the ways I engage.

// Architecture
ARCHITECTURE REVIEW & STRATEGY
Deep-dive assessment of your architecture, infrastructure, and cloud posture. You get a prioritized roadmap with specific recommendations — not a generic slide deck.
  • Infrastructure & cloud audit
  • Security posture assessment
  • Cost optimization analysis
  • AI production-readiness review
// Delivery
PROJECT-BASED ENGAGEMENTS
Hands-on architecture and implementation for defined initiatives. AI integration, cloud migrations, compliance buildouts, platform modernization — from design through production.
  • AI/ML pipeline design & deployment
  • Cloud migration & re-architecture
  • FedRAMP / compliance buildouts
  • DevSecOps pipeline implementation
// Advisory
FRACTIONAL CTO / ADVISOR
Ongoing strategic guidance without the full-time cost. I embed with your leadership team to guide architectural decisions, vendor evaluations, and technical strategy.
  • Weekly strategy sessions
  • Vendor & tooling evaluations
  • Team mentorship & hiring support
  • Board/investor technical briefings
// Rescue
INCIDENT RESPONSE & TRIAGE
Your production system is down, your migration went sideways, or your audit is in two weeks and you're not ready. I've been the person who gets the call. Short-term, high-intensity engagements.
  • Production incident triage
  • Compliance audit preparation
  • Failed migration recovery
  • Security remediation sprints
Social Proof
DON'T TAKE MY WORD FOR IT

I let my work and the people I've worked with speak for themselves. Recommendations from colleagues, clients, and leadership are on LinkedIn.

View Recommendations on LinkedIn
Organizations I've Worked With
Connect
LET'S TALK ENTERPRISE AI

Whether you're putting AI into production, standing up an operating model, or trying to get out of POC purgatory — if it's a hard enterprise AI problem, I want to hear about it.

Talk Enterprise AI Send an Email
EMAIL
Diiacovo@gmail.com
LINKEDIN
linkedin.com/in/michaeladiiacovo
GITHUB
github.com/mdiiacovo
LOCATION
Michigan — Remote (ET preferred)
CLEARANCE
Tier 4 Public Trust (Active) · U.S. Citizen
GitHub Activity
GitHub Contribution Graph