AI integration engineer.
I get embedded, connect the systems your business already runs on, and build autonomous agents on top of that foundation. The integration layer is the hard part - structuring your data sources into a unified model that agents can actually reason over. Once that exists, the agents work. They own workflows end-to-end, run without supervision, and surface only what genuinely needs a human. Not prototypes. Production systems, shipped fast.
What I build
System integration across your existing stack
I connect the tools and data sources you already run on - your CRM, ERP, POS, databases, third-party APIs - into a unified model that agents and LLM pipelines can reason over. No rip-and-replace. The stack stays the same. It just starts working together.
LLM pipelines built for production
The value is not in the model. It is in structuring your business context well enough that the model can act on it reliably. I build the data layer, the tooling, and the evaluation logic that makes LLM-powered automation stable enough to actually run unsupervised.
Autonomous agents wired into real workflows
Not chatbots. Not copilots. Agents that own a workflow end-to-end - monitoring inputs, executing decisions within defined rules, and surfacing only the exceptions that require human judgment. High-frequency, high-stakes processes handled without someone babysitting them.
Fast integration cycles, production-ready output
I work embedded, move fast, and ship to production. A focused integration - one workflow, one data layer, one agent - typically goes from scoping to live in four to six weeks. That is a working system in your stack, not a prototype on a demo environment.
Work
Retail / E-commerce
Cut 40 hours of weekly manual review. Headcount stayed flat through a 40% volume increase.
A retail operation with a growing online channel had four people manually reviewing every return and refund request before issuing a decision. Volume was increasing faster than capacity. Resolution times were approaching 48 hours. The team had no bandwidth left for anything else.
We audited every case type and extracted the underlying decision logic. We then structured the relevant business data into a coherent model: order history, customer tier, product condition rules, policy thresholds. An agent was deployed to run the full review process, resolving cases within its confidence bounds and routing the remainder with a recommendation and supporting context already prepared.
80% of cases now resolve without human involvement. The remaining 20% surface in a managed queue where a team member can close them in under a minute. Resolution time dropped to under 4 hours. When transaction volume increased 40% the following quarter, the operation absorbed it without adding staff.
B2B Services
Lead response time from 6 hours to 4 minutes. Inbound close rate up 28%.
A professional services firm was generating consistent inbound demand but losing a significant share of it before the first conversation. Leads arrived through the website, entered a shared inbox, and waited for availability. By the time someone followed up, the prospect had typically already engaged a competitor.
We built an agent that activated on every new inquiry. It pulled company data from external sources, evaluated fit against the firm's client profile, and drafted a contextual first response specific to the prospect's situation. The relevant team member received a notification with the lead scored, qualified, and a message ready to send.
Median response time dropped from 6 hours to 4 minutes. Inbound close rate improved 28% in the first quarter. The team stopped losing the timing advantage on leads they had already paid to generate.
Writing