AI engineering
Agents that do real work.
Everyone has seen the demo: an AI agent that researches, writes, books, and builds. The hard part is turning that demo into a system your business can trust with real work. That is the part we do.
Why we are different.
Most AI consulting ends at the slide deck. Ours starts at the repository. One of our founders builds AI-native software for a living, real-time execution systems and the infrastructure that lets agents cooperate, and that experience shapes how we engage. Small scopes. Working software early. Honest answers about what agents can and cannot do yet. We would rather ship you one agent that quietly saves twenty hours a week than a roadmap for ten that never leave staging.
What we build.
Agent systems design.
What should the agent own, and what should it never touch? We start there. Scoping an agentic system is mostly about drawing the boundary between judgment and execution, and we design that boundary before we write a line of code: tools, permissions, escalation paths, and the human checkpoints that make the whole thing trustworthy.
Custom agent development.
From single-purpose agents that handle one workflow flawlessly to multi-agent systems that research, draft, review, and deliver. We build on the models and frameworks that fit your stack, not whichever vendor bought lunch last.
AI integration.
Your business already runs on software that works. The fastest wins come from connecting intelligence to it: your CRM, your inbox, your docs, your data. We wire AI into the tools your team already lives in, so adoption is not a separate project. Usually that means MCP servers and retrieval systems built for your stack.
Evaluation and trust.
How do you know it works? Not vibes: evals. Before an agent touches production we define what correct looks like, build the test harness that measures it, and keep measuring after launch. If the system degrades, you hear it from the dashboard, not from a customer.
Production hardening.
Demos are forgiving. Production is not. Rate limits, retries, fallbacks, observability, cost ceilings. The unglamorous engineering that separates a clever prototype from a dependable system.
Where this experience comes from.
Public Accessory is two people, so "we" means us, and the receipts are specific. Systems our founding team has designed and shipped include:
- Production MCP servers connecting GitHub, Slack, Linear, Notion, and Google Calendar to AI assistants, with vector search and autonomous tool calling across more than 30 tools.
- RAG knowledge systems over real company data: pgvector embeddings, entity extraction, multi-source retrieval, and a Slackbot that answers grounded questions from it.
- A multi-format AI agent competition platform: evaluation pipelines, risk-adjusted rankings, and real-time portfolio tracking, from database schema to React frontend.
- Agent evaluation infrastructure: controlled experiments, multi-model comparison, and observability, because model behavior gets measured, not guessed at.
- Multi-LLM production workflows across OpenAI, Anthropic, and open-source models, built in TypeScript and Python.
Popular AI engineering requests we receive.
Project-based.
- Agent system design and build
- AI feature development
- Integration sprint
- Eval harness and model selection
Ongoing needs.
- Agent operations and monitoring
- Iterative capability expansion
- Advisory for in-house teams
Let's start something new. Say hello!
Thanks. We will reply within two business days.