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.

Start a build brief See services and pricing

Fixed-fee discovery from $9,500. Credited in full against the build.

MODELSDATAGPUSEVALUATIONDEPLOYMENTAGENCY

Published pricing

Every engagement price is on this site. No discovery call required to learn what things cost.

Your cloud, your code

Systems deploy into accounts you own. Source, weights, and evaluations transfer to you.

Evaluation gates every deploy

Quality, safety, and latency thresholds block a release the way a failing test does.

We will say no

A discovery that ends in a documented recommendation not to build is a successful one.

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.

Most projects break at link two, quietly, months before anyone says so out loud. We build the entire chain, and we measure each link.

How we build

Who this is for

Three kinds of organization, one recurring problem.

The decision, the document, or the inspection is already happening. It is just happening slowly, inconsistently, and inside the head of whoever is on shift.

Manufacturing and industrial operations

Plants, distributors, and equipment businesses between roughly $5M and $250M in revenue, where the work is physical and the paperwork is relentless.

Where it hurts

  • Quotes and specs that take days because someone has to read every PDF by hand
  • Inspection and quality calls that depend on who happens to be on shift
  • Scheduling and routing decisions made on instinct because the data never arrives in time

What we build instead

  • Document extraction that turns scanned specs, POs, and certs into structured records
  • Visual inspection that flags defects and logs evidence automatically
  • Quoting and scheduling assistants that draft the answer for a human to approve

Professional services and B2B teams

Firms whose product is expertise, sitting on years of contracts, reports, and case files nobody can search properly.

Where it hurts

  • Institutional knowledge locked in file shares and the heads of two senior people
  • Junior staff repeating research that was already done three years ago
  • Client deliverables that are 70 percent assembly and 30 percent judgement

What we build instead

  • Private assistants grounded strictly in your own documents, with citations
  • Research agents that draft the first pass and show their sources
  • Intake and triage that routes work before a human touches it

Software teams shipping AI features

Product and engineering groups with strong developers who have never had to evaluate a model, and are discovering that demos are easy and reliability is not.

Where it hurts

  • A feature that impresses in the demo and embarrasses in production
  • No way to tell whether a prompt or model change made things better or worse
  • Inference costs and latency that nobody forecast and nobody owns

What we build instead

  • Evaluation harnesses that turn 'it seems better' into a number you can gate a release on
  • Retrieval and context pipelines that hold up on real, messy corpora
  • Cost, latency, and failure-mode instrumentation wired in from the first release

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

Language models 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 assistants · 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, adapter tuning, and automated quality checks.
WHAT IT DELIVERS
Product imagery · creative tooling · synthetic training data · document generation
Design this system
04

Deep reinforcement learning

Reinforcement learning 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.ipynbILLUSTRATIVE

01import torch

02from foundry import evaluate, deploy

03

04model = Transformer.from_pretrained(

05 "example-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)

Example output · evaluation passed · p95 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

Shipped systems

Live properties you can open right now.

Systems built inside the Gallimore Enterprises portfolio. Not screenshots, not a slide. Working software with real users and real data behind it.

Document intelligence and quoting

Sawyer Priceleaf

Quote intelligence for nutraceutical and supplement manufacturing, where a request for pricing arrives as a spreadsheet, a PDF, or a paragraph of email and has to become a structured, costed quote.

Document extractionStructured outputsDomain pricing logic
Search and catalog systems

Rubber Machinery Exchange

An independent marketplace for used, surplus, and hard to source process equipment, where listings arrive unstructured and buyers search in language that never matches the spec sheet.

Catalog normalizationRetrievalListing intelligence
Research workspaces

EquinAtlas

A workspace for organizing horse records, pedigrees, and observations into something a person can actually reason over across generations of data.

Record linkageGraph modellingResearch tooling
Industrial operations

DPA Machinery and Appraisals

Machinery sales, appraisals, and solutions for rubber, plastic, and polymer processing, built with the vocabulary and workflow of the people who do the appraising.

Domain modellingOperational workflowIndustrial data
More on how these were built

How to work with us

Three ways in. All of them priced.

Most studios in this market will not tell you a number until the third call. Here are ours, before you talk to anybody.

01

Foundry Sprint

$9,500 to $15,000

2 to 3 weeks

A fixed-fee technical discovery that answers one question honestly: can this actually be built, with your data, at a cost that makes sense?

What you get
02

Build with us

$40,000 to $150,000

6 to 16 weeks

We design, train, integrate, evaluate, and ship the production system, then stay on while it settles into real use.

What you get
03

Enable your team

$20,000 to $60,000

4 to 8 weeks

Your engineers keep the work. We bring the patterns, the evaluation discipline, and the review that stops the expensive mistakes.

What you get

Research shelf

The work we build on.

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

Transformers
“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 and vision
“end-to-end trainable”

Image-based Sequence Recognition for Scene Text

Shi, Bai, Yao · 2015

Straight answers

The questions people actually ask.

The rest are on the services page, along with what each engagement includes.

What does an engagement actually cost?

A Foundry Sprint is $9,500 to $15,000 fixed. A production build runs $40,000 to $150,000 depending on integration surface and evaluation burden. Ongoing operations retainers are $2,500 to $8,000 per month. We publish these because a discovery call should be about your problem, not about extracting your budget.

Do you train your own models or use existing ones?

Whichever the measurements support. Most production value comes from retrieval, structured outputs, and careful evaluation around a strong existing model. Fine tuning earns its place when the task is narrow, repetitive, and well represented in your data. We show you the comparison rather than asking you to trust the choice.

Where does our data go?

Into your cloud account by default. We build in infrastructure you own, so your records stay under your contracts, your retention policy, and your auditors. If a hosted model is part of the design, we tell you exactly what leaves your boundary and what the provider's retention terms are before you approve it.

Who owns what you build?

You do. Source code, trained weights, evaluation suites, and documentation transfer to you, with no license back to us and no dependency on our hosting. If you fire us the week after launch, the system keeps running.

Open a build brief

Bring us the impossible-looking problem.

Tell us where the decision, workflow, dataset, or physical system is breaking down. If we are the wrong people for it, we will say so and point you somewhere better.

Start a build brief hello@model-foundry.com