Where our AI work actually lives today: not a roadmap, but a list of what we've shipped and what we're building toward.
Building products powered by LLMs and task-specific agents, including conversational interfaces, automated report generation, and reasoning over unstructured input.
See it in a real productRetrieval-augmented systems that let teams ask questions in plain English and get answers grounded in your own documents, data, and org-specific knowledge.
See it in a real productEnd-to-end product work where models, UX, and backend are designed together from day one, not AI bolted on to an existing product as an afterthought.
How we approach itUsing AI-assisted tooling to speed up the unglamorous parts of legacy modernization (code comprehension, test generation, and incremental migration) without a risky big-bang rewrite.
How we approach itPII redaction, prompt-injection controls, and governance patterns suited to regulated and high-trust environments building with AI.
How we approach itTrustworthy pipelines, analytics, and MLOps: the operational foundation RAG, agents, and governed AI systems actually run on.
How we approach itEvals, guardrails, and regression testing for AI systems in production. We're actively building this practice out, so reach out if you want to talk through how we approach it today.
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