AI that does real work.

Take over the repetitive reading, writing and lookup work that slows your team down: answering questions from your data, drafting replies, processing documents, routing tickets, writing reports, reviewing code, and taking actions in your systems with a person in the loop where it matters.

Whichever is simplest for the job. A fixed workflow with one model call is often more reliable and cheaper than a free roaming agent. We use agents when the task genuinely needs planning and tools, and we tell you which one you are getting and why.

We treat the model as untrusted and check its work in plain code. Answers are grounded in sources it actually retrieved, numbers are recomputed, links are verified, and anything that changes data needs a dry run and a person's approval.

No. We design AI to act as the person asking, with that person's permissions, so it can never show someone more than they could already see.

Evals. Before launch we build a set of real tasks with known good answers and run the system against it on every change, measuring accuracy, latency and cost. Failures become tests, so they stay fixed.

Model spend is designed in, not discovered later. Every call is metered, prompt caching cuts repeated context, cheaper models handle the steps where evals show they are good enough, and a budget limit can switch it off before a bill surprises you.

Assistants that talk to the same people over time usually do. We built MenteDB, an open source memory engine for AI, so an assistant can remember what it learned, notice contradictions and recall the right context later.

Mostly Claude, on the Anthropic API or Amazon Bedrock, plus other models where they win your evals. Tool use over MCP, retrieval with vector and keyword search, LangGraph where orchestration helps, and infrastructure on AWS.