An AI engineer, embedded in your team. Building agents that stay.
Skip the six-month hire and the disappearing agency. Frenti embeds a senior AI engineer into your team this week — shipping production agents in weeks, and leaving you owning systems that keep working after I step back.
You have two options for AI right now. Both are broken.
Hire it full-time
Three to six months to find a senior AI engineer. $200K+ before they ship a single line. And the best ones already have jobs.
Hand it to an agency
A flashy pilot, a polished demo, an invoice — then they're gone. Your “AI initiative” stalls in POC purgatory and quietly rots.
Embed an engineer
Embedded, not outsourced. I join your team this week, ship agents in weeks, and hand off systems your team owns — not slideware.
From audit to autonomous — without the handoff cliff.
Audit & architecture
Two weeks. I map your workflows, find the highest-ROI place to start, and design the agent architecture. You walk away with a build plan even if we stop here.
Embed & build
I work inside your stack and your standups, shipping production agents in weekly increments. You see working software every week — not status decks.
A handoff that holds
Every agent ships with evals, docs, and a runbook. When I step back, your team owns a system that keeps running — and I'm a message away.
Production agents, not prototypes.
LLM workflows
RAG pipelines, retrieval, grounding and citation — on AWS Bedrock or your existing cloud.
MCP servers
Connect Claude to your internal tools, data, and APIs through clean, secure MCP integrations.
Sub-agents & skills
Task-specific agents and reusable skills that slot into your team's real workflows.
Eval harnesses
So you can trust the output: LLM-as-judge scoring, regression tests, and guardrails.
Workflow automation
The manual, repetitive operational work — handled end-to-end and monitored.
The runbook
Docs, dashboards, and clear ownership, so the system survives long after I leave.
Senior judgment. Startup velocity. Enterprise scars.
One senior engineer, amplified
Twenty-plus years across healthcare, enterprise, and consumer products. Amplified by Claude Code, I ship at the pace of a small team — without the coordination tax.
I've shipped in regulated rooms
PHI-aware pipelines, EHR integrations, compliance-sensitive systems. I know what "production-ready" actually means when the data is sensitive.
Agents that stay
I don't optimize for a pretty demo. I optimize for the thing that's still running and earning its keep six months after I've stepped back.
Start small. Expand when it works.
Embedded Sprint
Two weeks, fixed scope. One working agent or automation, plus a roadmap. The clearest way to see how I work before committing.
- One production-ready agent or automation
- Architecture & build plan you keep
- Eval harness + handoff notes
Embedded Retainer
An ongoing weekly cadence. I'm part of your team — roadmap, build, ship, repeat — and you own everything we make. Cancel anytime.
- Senior AI engineer embedded in your team
- Continuous shipping in weekly increments
- Agents, evals, docs, and ownership transfer
- Direct access — your standups, your Slack
Straight answers.
Q.What is an embedded AI engineer?
An embedded AI engineer joins your team directly — your standups, your codebase, your tools — to build and ship AI systems alongside you, instead of delivering a fixed-scope project from the outside. It's the forward-deployed model: senior engineering presence plus velocity, with knowledge transfer built in.
Q.How is this different from hiring or using an agency?
Hiring takes months and a full-time commitment; most agencies build a pilot and leave. Embedded engineering starts in days, ships production software in weeks, and hands off systems your team owns — with no headcount on the books.
Q.Do we own the code and the agents?
Yes. Everything built during an engagement is yours, delivered with documentation and a runbook so your team can run and extend it without me.
Q.What do you build agents with?
Production LLM stacks: Claude and other frontier models, RAG over vector databases, MCP servers, sub-agents and skills — on AWS Bedrock or your existing cloud, always with evals and guardrails.
Q.How fast can we see something working?
A focused Embedded Sprint ships a working agent or automation in about two weeks. Retainers ship in weekly increments from the first week.
Q.Can you work in regulated, sensitive-data environments?
Yes — including PHI-aware and compliance-sensitive systems, drawing on 20+ years in enterprise healthcare IT.
Have a workflow that should be an agent?
Tell me the bottleneck. I'll tell you whether it's worth automating — straight, no pitch.