The leading builder of
AI models and applications.

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.

The gap

Prototype to production
is where AI stalls.

Most teams can prototype against a model API in a week. The distance from that prototype to production is where AI projects stall: accuracy has to be measured against real ground truth rather than eyeballed, retrieval has to surface the right context every time, latency and cost have to survive real traffic, and the system has to fail safely when the model is wrong. We have crossed that distance many times, and the patterns below are what it takes.

Patterns we bring

Repeatable across AI engagements, not one-off.

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.

Agents and agent infrastructure

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.

Harness engineering

The orchestration layer around models: specification-driven execution, quality gates between steps, and evaluation loops, so model output becomes dependable engineering output.

Knowledge layer infrastructure

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.

Evaluation benchmarked to experts

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.

Model development and vision

Models for text, time series, image, video, and 3D, including computer vision, image search, and quality assessment in production.

Inference and deployment

Model serving, MLOps, performance and load testing, and inference optimization on servers and edge devices, so the economics work at scale.

Data engineering and labeling

The pipelines, annotation, and labeling operations that AI actually runs on, at the scale of tens of millions of items.

Clients

Enterprises, research, and startups.

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Startups from Y Combinator, Techstars, StartX, 500 Global, Lightspeed, and more.

Where it shows up

AI feeds the verticals.

A capability rarely sells on its own. This one runs inside our legal, health, construction, and consumer work.

How we engage

Scoped or sustained.

A Blueprint scopes the first build and proves it on your data. An engineering subscription carries sustained AI work at a fixed fee with quality gates. For non-tech enterprises, AI for your business is the four-stage path from uncertainty to running software.

Have an AI problem
worth solving?

Tell us the data and the outcome you need.