Technology that makes the work believable.

We build across the major model families — Google Gemini (including the Gemini Live API that powers our JARVIS voice assistant), NVIDIA NeMo, and open-weight models we can self-host. We're model-agnostic: we pick the model that fits the task, cost, and latency budget, and we wire in retrieval (RAG) and tool-use (MCP) so the model works against your real data instead of reciting from memory.

They're internal engineering tools and infrastructure we've built and use in production. They represent our AI infrastructure and model-tooling stack — the deployment, orchestration, and evaluation layer that sits between a raw model and a reliable product feature. Our public GitHub organization shows the source-backed proof behind this work.

Yes. We build Model Context Protocol (MCP) servers that give AI assistants safe, structured access to tools, APIs, and internal data. MCP is how we let an agent take real actions — look up a record, trigger a workflow, filter a live view — instead of just generating text. Our JarvisNano hardware prototype and JARVIS voice assistant both run on MCP workflows.

Yes. When data residency, cost at scale, or latency requires it, we deploy open-weight models on your own cloud or on-prem infrastructure with NVIDIA NeMo tooling. When a hosted frontier model is the better trade-off, we use one behind a server-side broker so keys never reach the browser. We'll recommend the right split based on your requirements.

Retrieval-Augmented Generation (RAG) grounds an AI's answers in your own documents and data rather than its training memory, which cuts hallucinations and keeps answers current. We implement full RAG pipelines — ingestion, chunking, embeddings, vector search, and re-ranking — and wire them into assistants, search, and internal tools.

Credentials stay server-side (we use short-lived token brokers so long-lived keys never reach the browser), model calls run behind rate limits and evaluation harnesses, and agent actions are scoped through MCP with explicit tool permissions. We benchmark model behavior — our DRQ Benchmark project is public proof of that discipline — before anything ships.