Four service lines. Most engagements combine at least two — usually a build, paired with training for the people who'll run it day to day.
Backends, internal tools and data platforms built around how your team actually works. We design for maintainability from day one — clean handoff, real documentation, no dependency on us to keep it running.
Fine-tuning, evaluation harnesses, and deployment for models that need to perform on your data — not a public benchmark. Includes reinforcement learning and multi-agent systems where they're the right fit.
Multi-agent systems and validation pipelines that process data reliably at scale — the infrastructure layer that makes everything else in a company's AI stack actually work.
Practical, role-specific sessions that get a non-technical team comfortable using AI tools in their actual daily work — not a generic slide deck. This is how we've worked with every one of our auto-service partners.
The tools we reach for most — picked for the problem, not the resume line.
Built for teams with no ML background — the goal is confident daily use, not certificates.
We spend time with the team before writing a single slide — what tools they already use, where the friction actually is, what "using AI" would even mean for their job.
Front-desk staff, technicians and managers get different sessions, because they need different things from the same tools.
No sandbox exercises — sessions run on the team's actual tickets, quotes, and customer messages.
We check back in after the team has been using the tools on their own, to fix whatever the first sessions didn't cover.
Tell us what you're working with — we'll tell you honestly whether AI is the right tool for it.
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