AI systems I led or built for clients, described without naming the client; the products I built; and public open-source work. Each entry runs from the business problem to what was built, the architecture and AI pattern behind it, and the outcome and my role.11 Client Case Studies · 2 Products · 1 Open-Source Project
Outcomes first. Clients stay anonymous.
Client work is described by its business problem, architecture, stage and outcome, never by the client’s name. ABACare, Tellgence and the open-source projects are named and linked. Every figure on this page is one I can evidence; no product revenue or unverified accuracy claims are shown.
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Client case studies
Engagements I led or architected. Clients and their products are not named; the business problem, architecture and outcome are.
Agents that delegate, under approval
A coordinator delegates to specialist agents, a knowledge tool and customer MCP servers
Production platform
Support AI That Resolves Tickets: Ground-Up Rebuild
Software that answers a company’s support tickets, resolves the routine ones itself and hands the rest to people
Researcher, writer and editor agents cycle on editor feedback under a retry limit, and an evaluation harness scores each article before authors finalize it
Client delivery
Multi-Agent Technical White Paper Generator
Researcher, writer and editor agents in a private workspace, measured by an evaluation harness
Multi-agent
RAG
Human-in-the-loop
Recover the path to release
RuntimeAPI + TLS recovery
MobileReal-device checks
TeamRelease governance
TestFlight betaTechnical handover
Delivery boundary
API and TLS recovery, mobile checks and release governance converge on TestFlight beta delivery and technical handover
Beta Delivered
AI Health-Coach App Recovery
Interim CTO: production recovery, release governance and a TestFlight beta in three months
React Native / Expo
NestJS / Prisma
PostgreSQL / TimescaleDB
An evidence-led case file
CASE EVIDENCE
Case material
Research, if needed
Agent workspace
Structured report
Reflection + quality score
Expert review
Research runs only when a case needs it
Client Delivery
Corporate Credit Case-Analysis AI Agent
Risk, recovery outlook and draft collection mandates, self-critiqued before expert review
Human-in-the-loop
Products I built
ABACare, my product now going to market, and Tellgence, a Pendoah product I built. Each links to its live site.
Get paid for every session delivered
AuthorizationActive
Session noteSigned
ClaimHeld: supervision missing
PayerNothing sent
Payer- and parent-facing work always stops for a named person
Authorizations, session notes, claims and denial follow-up stay connected from first visit to payment
Preparing design-partner pilots
ABACare
Helps ABA therapy clinics get paid for every session they deliver, from authorization to claim
Human-in-the-loop
MCP
Deterministic guardrails
abacare.ai
Every call answered, booked or handed off
Caller: “Can I come in Thursday?”
Turn detection
Barge-in
Calendar booking
Booked · durable workflowTransfer to a person
Evidence pack Transcript · tools · approvals
A caller speaks to a realtime voice agent with turn detection and barge-in
Beta · first business customers live
Tellgence
Voice AI that answers a small business’s phone line, books the appointment and hands off to a person
One canonical set of skills, MCP servers and policy is written into each coding agent’s native configuration
Open Source · Apache-2.0
AgentHub
Keeps every AI coding agent on your machine running the same skills, MCP servers and policy, and tells you when one drifts
MCP
Open-source repositories
Open-source developer tooling I maintain under github.com/shmindmaster.
Active · Apache-2.0
AgentHub
Keeps every AI coding agent on your machine (Claude Code, Codex, Cursor, Gemini CLI, Copilot CLI and more) running the same skills, MCP servers and policy, and tells you when one drifts.