Hire an AI & Full-Stack Consultant for One Conversation — or the Whole Build

Not every problem needs a twelve-week engagement. Sometimes it needs forty-five minutes with someone who's built the thing before, telling you whether your plan holds up — before you spend three months finding out the hard way.

I get hired for both ends of that range, and everything in between. Some clients want a second opinion on an architecture before their team writes a line of code. Others want the whole thing built, shipped, and handed over. Most land somewhere in the middle: a scoped proof-of-concept, a stalled project that needs an outside eye, a system that works but nobody trusts.

Here's what I cover, and how to figure out where you fit.

What I Help With

• AI Integration — LLM pipelines and RAG systems that answer from your own data, built with hybrid retrieval, not a demo that falls over past a few hundred documents.
• AI Agents & Automation — multi-step agents that call tools, talk to your systems through MCP, and keep a human in the loop on anything risky.
• Fine-Tuning & Self-Hosted Models — your own tuned, distilled models running in your VPC or on-prem, for when API cost, latency, or data residency rules take hosted models off the table.
• LLMOps & Evaluation — eval suites, tracing, and guardrails for AI you already shipped, so "it seems to work" becomes a number you can watch.
• Full-Stack Development — the application around the AI, or the application on its own: frontend, backend, database, CI/CD.
• DevOps & Infrastructure — containers, Kubernetes, Terraform, and monitoring that scales quietly instead of paging someone at 2 a.m.
• Data Pipelines — ETL, vector stores, and streaming that turn raw, messy data into something a dashboard — or an LLM — can actually use.

Every one of these is also where the industry's actual attention is in 2026: agents that do real work instead of demoing well, retrieval that's grounded instead of hallucinated, models you own instead of rent, and the operational discipline to trust any of it in production. I'm not chasing trends here — these seven areas are the trend, and I've been building in them long enough to have opinions about what's real and what's marketing.

Pick Your Depth

Think of it as a spectrum, not a package:

• A conversation. You have a direction and want a sanity check — is this architecture going to hold up, is this the right model for the job, is this agent design going to fall apart under real traffic. One call, straight answers, no obligation to hire me for anything after.
• A review or proof-of-concept. You want someone to stress-test an existing system, or build a small, scoped piece to prove an idea works before you commit real budget to it.
• Full implementation. You want it built end to end — designed, shipped, documented, and handed off, with me available after launch for the inevitable "one more thing."

None of these is the "real" engagement and the others a lesser version of it. A single good conversation has saved clients more time and money than some full builds — because it caught a wrong assumption in week one instead of week ten. I price and scope each one on its own terms, not as an upsell funnel toward the biggest option.

Let's Talk

If you're not sure which of those you need, that's a perfectly good reason to reach out — figuring that out together is part of the first conversation, not a prerequisite for having it. Get in touch through the contact form and tell me what you're working on. If you want the full detail on scope, stack, and typical timelines for any of the seven areas above, the services page has it broken down one by one.

Related Posts

• Production RAG Implementation: How I Build Retrieval Systems at Million-Document Scale — What "AI Integration" actually looks like at real scale, past the demo stage.
• Your Website, Built End to End: From First Conversation to Live, Monitored, and Supported — The same conversation-to-launch arrangement described here, applied to full-stack web projects specifically.
• Bounded Agents: The Only Multi-Agent Pattern That Actually Works in Production — The design philosophy behind the agent systems I build, and why bigger and more autonomous isn't the goal.