Founding AI resource
Standing up an enterprise AI function
Leading secure enterprise AI delivery across a national behavioral health network — 12 production workflows, independent accuracy validation, governance, adoption, and measured value.
// Joseph Bisaccia · AI Engineering & Enterprise AI Leader
I build AI systems —
and the organizations
that trust them.
I connect secure AI delivery in regulated environments with enterprise adoption, sustainable billable-hour growth, and measurable value.
Focus
Enterprise AI Governance, Security, Infrastructure
Currently
Lead AI Engineer, Behavior Frontiers — building the AI function from the ground up
Open to
AI engineering leadership, strategic collaborations & speaking
Scroll
// 01—THE SYSTEMS
A short tour of the work.
Expertise
Production AI systems for regulated and enterprise environments.
- [00]Enterprise AI Governance
- [01]AI Security
- [02]Enterprise AI Infrastructure
- [03]HIPAA-Compliant AI Systems
- [04]Agentic Workflow Development
- [05]Retrieval-Augmented Generation (RAG)
- [06]Company-Wide AI Enablement
- [07]Technical Program Leadership
- [08]Change Management & Adoption
- [09]LLM Evaluation
// 02—ORGANIZATION
Building the systems — and bringing the organization along.
01
0.00%
Run success across 18,454 production workflow executions since August 2026
02
0
Records independently verified across eight accuracy checks with zero errors
03
0
Production AI workflows deployed across clinical, HR, and operational processes
Leadership & enablement — a snapshot
Governance & security
Compliance-oriented implementation
Building governance and evaluation into regulated AI delivery so adoption and value can be measured without compromising trust.
Training enablement
Change management for distributed teams
Led a 72-seat Microsoft Copilot deployment to 66 active users and 8,021 prompts in 30 days through focused enablement and daily measurement.
Frontier model work
Model training & evaluation
Ongoing contract work with Handshake AI, Outlier AI, and Mercor: dataset curation, reward-metric evaluation, and RLHF preference workflows.
// 03—PERSPECTIVE
“Enterprise AI success is an organizationalchallenge, not an engineering one.The model is the easy part.”
The hard part is governance, trust, and adoption — the slow work of building something a regulated organization can actually stand behind.
The leaders who win the next decade won’t just ship models. They’ll build secure systems and bring entire organizations along — training the skeptics, designing the guardrails, and turning AI from a pilot deck into infrastructure.
I work at exactly that intersection: hands-on engineering depth, paired with the organizational enablement that makes the engineering matter.
// 04—WORK
Nothing ships until it passes a gate.
Open-source evaluation and governance harnesses — runnable, tested, and CI-checked. Every dataset is synthetic and every repository documents its limits.
~/projects/verify-gate▍
github.com/jbisaccia-9
jbisaccia-9/verify-gate
Document verification gate: 9/9 valid synthetic packets passed; 6/6 counterexamples refused until checks and human approval cleared.
VerificationHuman approvalCI gate
View repository ~/projects/fanout-gate▍
github.com/jbisaccia-9
jbisaccia-9/fanout-gate
Private-message delivery gate: 22/22 valid synthetic messages delivered; all 22 duplicate retries and 6/6 failure modes refused.
DeliveryIdempotencyCI gate
View repository ~/projects/perm-gate▍
github.com/jbisaccia-9
jbisaccia-9/perm-gate
Permission-layer enforcement measured against prompt-only guardrails; scoped access must produce zero leaks on synthetic tests.
AuthorizationGuardrailsSecurity
View repository ~/projects/target-gate▍
github.com/jbisaccia-9
jbisaccia-9/target-gate
Data-quality gate blocks delivery until identifier, freshness, dedupe, coverage, and grounding checks pass on fixtures.
PipelinesData qualityCI gate
View repository Professional engagements
Behavior Frontiers · Healthcare · Jul 2026–Present
Building the AI function in regulated healthcare
Founded the AI function across a national behavioral-health network and deployed 12 governed production workflows sustaining 99.97% run success across 18,454 runs, with independent accuracy validation and reporting tied to quality, efficiency, and billable-hour growth.
See detailsCapital Energy · Energy · Sep 2025–Jul 2026
Customer-facing AI for response speed and pipeline growth
Shipped a RAG assistant that reduced response times by 40%, then expanded the customer-engagement stack with a Voiceflow, Twilio, and ElevenLabs lead-reactivation agent.
Open source
Top projects, public and runnable.
Twelve public Python and JavaScript gate repositories, with selected projects featured here. Each demonstrates a measurable release threshold on synthetic fixtures; employer work stays private.
github.com
jbisaccia-9
// 05—CONTACT