MFMODEL FOUNDRYStart a brief

Applied machine intelligence · research and engineering

Build the intelligent age.

Model Foundry is the technical partner for teams that want more than AI theater. We turn data, GPUs, models, and hard operating problems into systems that learn, see, reason, and ship.

Build the hard thing Explore the model atlas
MODELSDATAGPUSEVALUATIONDEPLOYMENTAGENCY

The new industrial stack

The revolution is not a chatbot. It is a new production capability.

Every serious AI system is a chain: a problem worth solving, a trustworthy data loop, the right model, enough compute, rigorous evaluation, and a product people can actually use.

We build the entire chain, for organizations ready to make intelligence part of how they operate.

01A problem worth solving
02A trustworthy data loop
03The right model
04Enough compute
05Rigorous evaluation
06A product people use

Model atlas

Technical capabilities, not hype categories.

Each one is a system we can deploy against a real business problem, with the evaluation to prove it works.

01

Language systems

LLMs turn text, code, documents, and tool outputs into a reasoning interface for your business.

HOW WE BUILD
Transformers, retrieval-augmented generation, structured outputs, tool use, fine-tuning, guardrails, and evaluation harnesses.
WHAT IT DELIVERS
Private copilots · document intelligence · research agents · workflow automation
Design this system
02

Computer vision and OCR

Vision models turn pixels, scans, video, and physical environments into searchable, measurable operational data.

HOW WE BUILD
OCR, document layout models, object detection, segmentation, multimodal embeddings, anomaly detection, and edge inference.
WHAT IT DELIVERS
Inspection systems · paperwork extraction · visual search · video intelligence
Design this system
03

Generative media

Diffusion models learn to reverse noise into images, designs, synthetic data, and controlled visual output.

HOW WE BUILD
Latent diffusion, image-to-image, inpainting, control conditioning, LoRA adaptation, and automated quality checks.
WHAT IT DELIVERS
Product imagery · creative tooling · synthetic training data · document generation
Design this system
04

Deep reinforcement learning

DRL learns a policy through feedback, which is what you want when a system must make sequential choices under uncertainty.

HOW WE BUILD
Simulation environments, reward design, offline RL, policy evaluation, constrained optimization, and human-in-the-loop rollout.
WHAT IT DELIVERS
Scheduling · routing · production optimization · intelligent control policies
Design this system
05

GPU and MLOps systems

A model is only useful when data, compute, latency, cost, security, and observability work together in production.

HOW WE BUILD
GPU workload design, vector retrieval, batch and streaming pipelines, model serving, telemetry, evaluation gates, and CI/CD.
WHAT IT DELIVERS
Production inference · governed data loops · cost controls · monitored deployments
Design this system

From notebook to production

Research is the beginning. The system is the deliverable.

We work like an applied research lab and an engineering team in the same room: inspect the data, establish baselines, test models, measure failure modes, then deploy with controls.

CUDAPYTORCHTENSORFLOWRAYVECTOR DBEDGE
model_foundry.ipynbGPU READY

01import torch

02from foundry import evaluate, deploy

03

04model = Transformer.from_pretrained(

05 "your-domain-model"

06).to("cuda")

07

08metrics = evaluate(

09 model, validation_set,

10 checks=["quality", "safety", "latency"]

11)

12

13deploy(model, target="production",

14 observability=True)

Evaluation suite passed · p95 latency tracked · deployment gated

What this unlocks

OCR that reads the paperwork.
Vision that sees the line.
Agents that move work.

  1. 01
    DATACollected, labelled, versioned, governed
  2. 02
    GPU CLUSTERTraining, fine-tuning, batch inference
  3. 03
    MODELEvaluated against quality, safety, latency
  4. 04
    DECISIONServed into the workflow that needed it

Research shelf

The work we build on.

Foundational papers we use to teach, choose architectures, and challenge our own assumptions.

Transformers / LLMs
“based solely on attention mechanisms”

Attention Is All You Need

Vaswani et al. · 2017

Deep RL
“learn successful policies directly from high-dimensional sensory inputs”

Human-level control through deep reinforcement learning

Mnih et al. · Nature, 2015

Diffusion
“a near-optimal point between complexity reduction and detail preservation”

High-Resolution Image Synthesis with Latent Diffusion Models

Rombach et al. · 2021

OCR / Vision
“end-to-end trainable”

Image-based Sequence Recognition for Scene Text

Shi, Bai, Yao · 2015

How to work with Model Foundry

01

Foundry Sprint

A fast technical discovery: data reality, model fit, risk, architecture, and a build plan your team can execute.

02

Build with us

We design, train, integrate, evaluate, and operate the production system, from notebook to monitored service.

03

Enable your team

Your team gets the models, infrastructure patterns, notebooks, evaluations, and engineering handoff to keep building.

Hire us to build the intelligent system, or use our technical patterns to build it yourself. Either way, start with the operating problem, not the buzzword.

Start a Foundry conversation

Open a build brief

Bring us the impossible-looking problem.

Tell us where the decision, workflow, dataset, or physical system is breaking down. We will help you find the right model of work.

Prefer email? [email protected]