LLM applications and retrieval
Extraction, classification, search, and generation over your own corpus, with retrieval, embeddings, and prompt engineering tuned to the domain rather than taken off the shelf.
We build models for text, time series, image, video, and 3D, deploy them on servers and edge devices, and optimize inference for performance. With partners like Google, Apple, and Stanford, we bring frontier AI to enterprise problems.
Extraction, classification, search, and generation over your own corpus, with retrieval, embeddings, and prompt engineering tuned to the domain rather than taken off the shelf.
Building and deploying agents on real workflows, and the infrastructure they run on: tool use, orchestration, human-in-the-loop controls, and guardrails enforced in the architecture, not the prompt.
The orchestration layer around models: specification-driven execution, quality gates between steps, and evaluation loops, so model output becomes dependable engineering output.
The retrieval and knowledge systems that let AI reason over an organization’s own corpus, with provenance and review built in, so every answer traces to its source.
Quality measured against expert ground truth on precision, recall, and ranking, with an evaluation harness that runs on every change, so accuracy is a number, not an impression.
Models for text, time series, image, video, and 3D, including computer vision, image search, and quality assessment in production.
Model serving, MLOps, performance and load testing, and inference optimization on servers and edge devices, so the economics work at scale.
The pipelines, annotation, and labeling operations that AI actually runs on, at the scale of tens of millions of items.


















Startups from Y Combinator, Techstars, StartX, 500 Global, Lightspeed, and more.
Tell us the data and the outcome you need.