AI Agents & Automations
LLM-powered systems that earn their place in your business.
Most AI projects ship as demos and never make it to production. They hallucinate, they don't know your data, they fail silently, and three weeks in someone quietly turns them off. We build the other kind — agents and automations that hold up under real users, integrate with the systems your business actually runs on, and pay for themselves in the first quarter.
We've shipped AI inside funded startups and inside our own products. We know the difference between "this is cool in a Jupyter notebook" and "this just runs the back office now." That difference is mostly the unglamorous work — eval harnesses, logging, fallbacks, retrieval quality, prompt versioning, knowing when an LLM is the wrong tool. We do that work because skipping it is what kills AI projects.
Answering calls 24/7 — never missing a lead.
Custom AI Agents
Autonomous agents that take real work off your team's plate. We use structured tool use over free-form prompting because it's more reliable, easier to debug, and easier to scope. We build eval harnesses before launch so you can actually measure whether the agent is doing its job. We design human-handoff into the system from day one because that's what makes agents shippable. The result: a workflow your team used to spend hours on, now running in the background — and a system that gets smarter as you feed it more real cases.
LLM Integration
Claude, OpenAI, and open-source models wired into your existing product. Not a sidebar chatbot bolted on for a demo — features that change how your product works, ship to paying users, and survive contact with reality. We've integrated AI into Node.js and TypeScript backends, into existing SaaS dashboards, into ecommerce flows, and into legacy PHP and Rails codebases. Where it lives in your stack is a design decision, not an afterthought. The outcome: AI that customers feel in the product, not a feature your team has to convince anyone to use.
RAG & MCP Pipelines
Retrieval-Augmented Generation pipelines that ground AI responses in your actual data so it stops making things up. We get the chunking, embedding, and retrieval evaluation right because that's where most RAG systems fail — not at the LLM call. We also build Model Context Protocol (MCP) servers that let agents act on your real business systems: CRMs, inventory, support tickets, internal tools. Less "interesting demo," more "Claude can now actually do the work." For teams adopting AI internally, this is what turns it from a toy into infrastructure.
Business Automation
AI receptionists and phone agents that book appointments while you sleep. Lead capture that doesn't lose hot leads on weekends. Follow-up automation that closes more jobs without the owner doing admin at 9pm. Review and reputation systems. Quoting, invoicing, and internal ops workflows. Lead pipelines from website, Google Ads, and Facebook into one place. Same engineering rigor whether you're a Series A startup or a busy service business — the stack is the same, the requirements are the same, and "it almost works" is unacceptable in both cases.
Evaluation first. Code second.
We start with evaluation, not code. Before we build an agent, we work with you to define what success looks like in measurable terms — 50 to 100 real cases with the right outputs. That eval set becomes the test harness for everything we ship. It's also the conversation that surfaces whether AI is actually the right tool, or whether a Postgres trigger and a cron job would solve the problem better and cheaper.
From there: tool use over prompts, retrieval over fine-tuning, observability from day one, human-in-the-loop for anything ambiguous. We document the architectural decisions so your next engineer knows why things are the way they are.
Models
Backend
Retrieval
Tooling
Infra
- Funded startups building AI products who need a senior engineer, not someone learning on your dime.
- Product teams adding AI features to existing software and want it done right the first time.
- SMB operators who want AI to actually save them time, not a chatbot that frustrates customers.
- Agencies looking for white-label AI engineering on tight timelines.