// Consulting — Agentic AI for Robotics
Agentic AI for robotics.
An LLM and agentic layer on top of your robots — conversational ops, fleet orchestration, evaluation — with the guardrails that make it safe to actually ship.
Who this
is for
Robotics teams that want a language layer on top of their robots — for operators, for engineers, for customers — and don’t want it to be a demo that breaks the first time something unexpected happens. Often the team is somewhere between “we’d like to try this” and “enterprise procurement says we can’t deploy it without guardrails we haven’t built yet.”
What we
usually see
General-purpose LLM consultants don’t know what’s safe to do near a robot. Robotics teams haven’t built agentic systems past a prototype. The buyer wants natural language, conversational debugging, agentic orchestration — and every implementation opens up a new safety question no one on the team is set up to answer. Anything LLM-controlled near a physical actuator now sits inside the AI Act too, which adds another dimension to questions that were already hard.
Where we
can help
Conversational ops layers on top of the existing observability stack — operators asking questions in English, the agent answering against real telemetry.
Agentic orchestration over heterogeneous fleets — LLM front-end, verifiable scheduler underneath, closed-loop replanning when something deviates.
RAG over the operational corpus: manuals, SOPs, incident history.
Natural language interfaces for cobots and industrial robots, usually productizable into the OEM’s own stack.
Computer vision (CV) models — perception pipelines, custom model training, deployment, and evaluation across the conditions you actually run in.
AI / ML Ops — basically, what happens after the model is trained: training pipelines, model registry, versioning, deployment, drift monitoring, and the update path when a new version lands.
Observability and tracing for AI and agentic workflows — seeing what your agents are actually doing, what they decided and why, where they failed, and how that’s changing over time.
Evaluation harnesses for VLA and agentic systems — task batteries, simulation stress tests, real-world A/B protocols.
The safety side: threat modeling, intent monitoring, action authorization, prompt-injection defenses, red-teaming.
AI gateway design when the agent is talking to enough internal systems that identity, policy, and audit start to matter.
Code-generation pipelines tuned to the team’s robotics stack and conventions.
And more — what’s listed here is a sample, not a menu. Most engagements pull in whatever’s most painful that month.
Not exhaustive. We pick up what the team is already on and bring in alternatives when it’s worth it.
Why teams
work with us
Most agentic work for robotics ends up getting built twice: once as the demo that opened the conversation, then again — slower — after the safety review, the procurement reviewer, and the AI Act all weigh in. We try to skip the first version. From the start we design with the guardrails, the audit trail, the regulatory exposure, and the on-call rotation in mind, and we treat the capability as something that has to live inside all of that.
Adjacent
work
Engagements rarely live alone. A couple of the areas this one most often pulls in.
Data Platform & Observability
A useful conversational ops agent needs a queryable data substrate underneath. The data platform is almost always a prerequisite for serious agentic work.
Production Engineering
Deploying an LLM near production robots requires a release process that can actually carry it. The production work is what makes the agentic deployment safe to ship.
Get in
touch
Reach the team directly. Tell us what you’re trying to ship and we’ll tell you what an engagement on this would look like.
Looking for the broader practice? Back to the consulting overview.