Intelligence on Top of Your Existing Stack
When rule-based automation isn't enough. RAG for grounded knowledge. AI agents for autonomous work. Predictive models for forward-looking decisions. Built to ship, not to demo.
Three core AI services, each shipped as a real system
Every service below has its own dedicated page with deliverables, tools, who it's for, and FAQs.
Built for production, evaluated continuously
Every AI system ships with grounding, guardrails, and an eval harness — not a demo dressed up as a product.
1 · Discovery
We define the job, the success metric, and the data the AI needs to ground itself in.
2 · Design
Retrieval strategy, prompt design, schema definition, guardrail rules, and evaluation harness.
3 · Build
Implementation against your real APIs and data, with monitoring and confidence thresholds baked in.
4 · Handover
Eval suite, runbooks, monitoring dashboards, and a clean handover to your team.
Common Questions About AI Engagements
How is this different from just using ChatGPT?
ChatGPT is a tool. AI services are systems. The difference is grounding (knowing your data), guardrails (staying on-script), integration (connecting to your CRM, support tool, database), and accountability (citations, evaluation, monitoring). Production systems need all of that.
Will the AI hallucinate or go off-script?
Properly engineered AI systems include retrieval grounding (RAG), strict output schemas, confidence thresholds, evaluation suites, human-in-the-loop checkpoints, and clear failure modes. The AI does one job and only one job — and gets evaluated continuously.
How do you handle data privacy?
Sensitive data stays in your environment. I work with service-scoped credentials, principle-of-least-privilege access, encrypted secrets management, audit logs, and self-hosted vector stores when data residency matters.
What's the typical AI project size?
RAG and agent systems typically take three to six weeks for the initial build, then ongoing iteration. Predictive analytics models depend heavily on data readiness — clean data ships in weeks, messy data takes longer.
Got an AI use case you want to ship for real?
Bring the problem. We'll scope the grounding, guardrails, and eval criteria — and decide whether RAG, an agent, or a predictive model fits.