Production AI Systems for B2B Products and Operations
I build and operate reliable AI capabilities for B2B SaaS and data-heavy products — from new product features and knowledge systems to agent-assisted workflows integrated with existing data and software.
Three Areas of AI Work
I work across the product, operations, and data layers so the result solves a real business problem—not just an isolated model demo.
AI Product Features
Add a reliable AI capability to an existing SaaS product, from the user experience and API through evaluation and rollout.
- Copilots and product assistants
- Document intelligence and personalized generation
- AI search, recommendations, and product-facing agents
- Real-time and streaming interfaces
AI Workflows & Agents
Turn multi-step product or operational work into a controlled agent-assisted flow connected to the tools your team uses.
- CRM, helpdesk, and internal-tool integrations
- Structured extraction and classification
- Approvals, escalation, and human review
- Tool use, persistent state, and audit trails
Knowledge & Decision Systems
Make proprietary documents and structured data usable through grounded search, matching, analytics, and decision support.
- RAG, enterprise search, and hybrid retrieval
- Opportunity matching and analytics assistants
- Source-grounded answers and safe fallbacks
- Data-quality and schema-drift controls
From Opportunity to Operation
The delivery process I follow — connecting product discovery to reliable production ownership.
- 01Discover
- 02Baseline
- 03Prototype
- 04Integrate
- 05Evaluate
- 06Roll out
- 07Operate
Built for Real Users, Not Demos
Every production system I build is designed around quality measurement, safe failure modes, and maintainability for the team that operates it.
Production AI Stack
The tools I use to build, integrate, evaluate, and operate AI systems across the stack
Selected Production AI Systems
Melio MealPlan AI
AI Product Feature
Personalized meal plans with nutrition guardrails, not a generic chatbot.
Built the LangGraph AI service, USDA and Qdrant recipe retrieval, validation pipeline, pre-release evaluations, observability, and a cross-provider LLM fallback — 105 users in production.
GovChime Analytics Platform
AI Content Pipeline
Blog, social and SEO content grounded in federal contract data, not invented figures.
Built the Python AI content service — human review queue and a guard that strips any figure not in the source data — on top of the data platform.
AI Engineering FAQ
What types of AI systems do you build?
I focus on three connected areas: AI product features, agent-assisted workflows, and knowledge or decision systems grounded in company data. At BinaryBuilders I built the Melio meal-generation pipeline (LangGraph agent, USDA grounding and Qdrant recipe retrieval) and the GovChime AI content service that drafts human-reviewed copy from contract data.
Can you integrate AI into an existing application?
Yes. My AI work is designed around existing products and tools — whether that means a SaaS platform, internal system, data warehouse, or a custom backend. Integration, evaluation, and monitoring are part of the build, not an afterthought.
What makes your AI systems production-ready?
Production-ready means measurable quality and safe operation: structured outputs, validation, evaluation baselines (RAGAS, golden datasets), observability (Langfuse, OpenTelemetry), error handling, provider fallbacks, and clear human escalation where it matters.
Hiring for an AI Engineering Role?
I'm open to B2B contracts and full-time senior AI / full-stack engineering roles. Reach out and let's talk about what your team is building.
Get in Touch