Autonomous AI agents that do the work.
Tool-using, multi-step AI agents that reason, plan, and execute against real business workflows - with guardrails, memory, and human-in-the-loop checkpoints.
We've shipped 25+ AI products to production - so our agents come with the guardrails, evals, and audit trails most teams only add after their first incident.
Agentic AI
Most 'AI features' stop at a chat box. Agentic systems go further: they take a goal, break it into steps, call the right tools, and carry a task through to a finished outcome - drafting the response, updating the record, escalating the edge case.
We build agents that are safe to put in front of a workflow: scoped permissions, audit trails, evaluation on your own data, and a human checkpoint wherever the cost of being wrong is high.
Scoped, shipped, and supported.
Tool-using agents with policies
Agents that call your APIs, search your knowledge, and act in your systems - inside permission boundaries you define.
Long-horizon planning and memory
Multi-step task decomposition with persistent memory, so an agent can pick up context across a long-running job.
Guardrails and human-in-the-loop
Approval checkpoints, content filters, and fallbacks for the cases that genuinely need a person.
Evaluation and audit trails
Every decision logged and scored against your metric, so you can prove the system behaves before it scales.
- OpenAI
- Anthropic
- LangGraph / orchestration
- RAG
- Vector stores
- Eval harnesses
GenAI competitor-analysis product
A GenAI system that monitors competitor pricing, assortment, and positioning across channels - turning multi-source data into merchandising decisions.
Read the case studyQuestions we get a lot.
How is an AI agent different from a chatbot?
A chatbot answers a question. An agent takes a goal, breaks it into steps, calls the tools it needs, and carries the task to a finished outcome - updating records, drafting responses, and escalating the edge cases.
How do you keep agents safe in production?
Scoped permissions, content and action guardrails, human-in-the-loop checkpoints wherever the cost of being wrong is high, and full audit trails so every decision is logged and reviewable.
What does a first agentic project usually look like?
We scope one high-value workflow, build it against your data with an evaluation harness, and put a working agent in your hands with clear metrics - then expand once it's proven.
The rest of the stack.
Computer Vision
Production vision pipelines - custom-trained models, real-time inference at edge or cloud, drift monitoring. Medical imaging, quality QA, retail analytics, and beyond.
Learn moreWeb & Mobile Applications
Modern web on React and Next.js, cross-platform mobile for iOS and Android - performant, accessible, and built to hold up past the launch demo.
Learn moreVoice Agents & Chatbots
Real-time voice agents and RAG-grounded chatbots - with persona, tone, eval, and the guardrails to ship into customer-facing production safely.
Learn moreData Warehousing & Pipelines
Multi-source ingest, transformation orchestration, and warehouse modeling - the data plumbing that turns messy operational data into AI-ready foundations.
Learn morePowerBI, Fabric & Analytics
Microsoft PowerBI and Fabric implementations, custom analytics engines, executive dashboards - the daily-driver tools decision-makers actually open.
Learn moreLet's build what's next - together.
Whether you have a brief or just an idea, we'll help you scope a path from concept to production - with compliance built in.