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TECHNOLOGY / THE SCIENCE OF EXPERT WORKNEUROLOGIC AI

Intelligence begins
with experience.

Experienced people know which detail changes a decision. We train specialist models on approved examples of that work.

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THE SPECIALIZATION METHODARCHITECTURE OVERVIEW
01 / THE STARTING POINTOpen foundation

General language and reasoning

02 / THE DIFFERENCEApproved expertise

Your rules, decisions and exceptions

POST-TRAINED INSIDE YOUR ENVIRONMENTYour specialist.
One bounded workflow.
THE HELD-OUT CHECKSeparate held-out work

Validate before expanding responsibility.

Customer deployment scope and controls are agreed per engagement.

“We can know more than we can tell.”

MICHAEL POLANYI
THE TACIT DIMENSION · 1966
FROM EXPERIENCE TO INTELLIGENCE

Follow the knowledge as it changes form.

Start with an expert’s decision. Learn from the work that meets the rules. Test what the specialist has learned.

THE METHOD01 / 03
Observe
Select
Specialize
Keep the reason with the action.Capture design · visible and participant controlled
01

Keep the reason with the action.

Keep what the expert saw, what they decided and what happened next. The context matters as much as the action.

Capture design · visible and participant controlled
01 / AN APPROVED DEMONSTRATION

Keep the decision
with its context.

  1. ContextWhat did the expert see?

    Sources, current state and the exception.

  2. DecisionWhy this next step?

    The action, its reason and any correction.

  3. OutcomeWhat happened next?

    The result that makes the decision testable.

A learning trace preserves the relationship between all three.

02

Compliance is the first filter.

Learn only from work that follows the rules. Give the strongest measured examples more influence.

Learning method · stronger examples carry more weight
02 / SELECT THE LEARNING MATERIAL

Compliance first.
Expertise next.

  1. ExcludeWork that fails the rules

    Noncompliant traces do not enter the learning path.

  2. QualifyWork that meets the rules

    Compliant demonstrations are eligible for synthesis.

  3. WeightAccurate, efficient work

    Expert weighting favors the strongest measured examples.

The selection method is illustrated here. Weighting depends on measured expertise.

03

One workflow comes into focus.

Focus on one task. Test on separate work the model has never seen during training.

Training and evaluation remain distinct
03 / TRAIN, THEN TEST

Learn one workflow.
Test on separate work.

  1. TrainApproved demonstrations

    Post-train a specialist for the agreed workflow.

  2. SeparateUnseen evaluation tasks

    Keep the test material outside the training set.

  3. EvaluateDecisions, errors, escalation

    Use the evidence to decide what the specialist can take on.

The method is illustrated here; reported results have their own evaluation scope.

01 / AN APPROVED DEMONSTRATION

Keep the decision
with its context.

  1. ContextWhat did the expert see?

    Sources, current state and the exception.

  2. DecisionWhy this next step?

    The action, its reason and any correction.

  3. OutcomeWhat happened next?

    The result that makes the decision testable.

A learning trace preserves the relationship between all three.

Capture design · visible and participant controlled

THE INSIGHT

The most valuable knowledge in your company was never written down.

Experienced people notice what a manual misses: the detail that prevents a claim dispute, or the design change worth testing next.

Digital Human is designed to learn from that judgment, connecting approved expert work with its outcomes.

How the learning works

With consent, we capture what experts see and do in their applications. We connect those actions to their results, then train a specialist using validated examples.

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THREE PILLARS

Built on three foundations.

01

Learn from the best work

Check that work follows the rules, then give stronger examples more influence. The customer controls the priorities.

02

Keep the context

Learn from decisions, corrections and results—not just a sequence of clicks.

03

Check before acting

Designed to check what is allowed, verify the result and involve a person when the answer is uncertain.

LEARNING BEYOND THE EXAMPLE

Let the result be the judge.

Learning begins with expert examples. The next step is to test new approaches and keep improvements that meet the rules.

What counts as a useful result?

In engineering, a design change faces a real circuit simulation and a manufacturability check. In operations, it must satisfy the documented process and its required outcome.

The aim is to learn how to recover when something goes wrong, as well as how to complete the usual path.

EVALUATION PACK

A dated internal evaluation pack, reviewable under NDA and not independently audited.

Dated engineering and process evaluations are available to qualified teams under NDA.

ILLUSTRATIVE LEARNINGIMITATION CEILING
Behavior cloningVerified-outcome learning

Dashed line: best observed demonstration.

Behavior cloning approaches the best observed human. Reinforcement learning, rewarded only by verified outcomes and bounded by compliance rules, can cross that ceiling.

Learn the best compliant behavior already present in expert demonstrations.

Explore each step
THE VERTICAL WORLD MODEL

Broad intelligence. Deeper specialization.

Three layers, each with a different job. Scroll to see what each contributes.

SPECIALIZATION01 / 03
Foundation
Industry
Your model
Begin with broad capability.01 / OPEN FOUNDATION
01

Begin with broad capability.

Begin with an open foundation model. Then teach it the work of your industry and organization.

01 / OPEN FOUNDATION
THREE LAYERS OF SPECIALIZATION

General capabilities.

  1. 01
    Open foundation

    General capabilities

  2. 02
    Vertical world model

    Industry structure

  3. 03
    Your Digital Human

    Customer expertise

01 / Open foundation

The starting model supplies broad language and reasoning capability.

Architecture · customer instance data is not pooled across organizations.

02

Give it a model of the work.

Add knowledge of an industry’s processes, rules and exceptions. This is the industry layer of the intended architecture.

02 / VERTICAL WORLD MODEL · architecture
THREE LAYERS OF SPECIALIZATION

Industry structure.

  1. 01
    Open foundation

    General capabilities

  2. 02
    Vertical world model

    Industry structure

  3. 03
    Your Digital Human

    Customer expertise

02 / Vertical world model

Workflow logic, exceptions and compliance patterns add domain depth.

Architecture · customer instance data is not pooled across organizations.

03

Make the expertise your own.

Train your specialist on approved expert work, inside your environment. Your instance data does not train another customer’s model.

03 / YOUR DIGITAL HUMAN · deployment design
THREE LAYERS OF SPECIALIZATION

Customer expertise.

  1. 01
    Open foundation

    General capabilities

  2. 02
    Vertical world model

    Industry structure

  3. 03
    Your Digital Human

    Customer expertise

03 / Your Digital Human

Approved expert work specializes your model inside your boundary.

Architecture · customer instance data is not pooled across organizations.

THREE LAYERS OF SPECIALIZATION

General capabilities.

  1. 01
    Open foundation

    General capabilities

  2. 02
    Vertical world model

    Industry structure

  3. 03
    Your Digital Human

    Customer expertise

01 / Open foundation

The starting model supplies broad language and reasoning capability.

Architecture · customer instance data is not pooled across organizations.

01 / OPEN FOUNDATION

DESIGNED FOR YOUR ENVIRONMENT

Your knowledge. Inside your boundary.

YOUR KNOWLEDGE. YOUR ENVIRONMENT.

The boundary stays. The capability grows.

Designed to keep the learning and the work inside your environment.

YOUR BOUNDARY01 / 03
Capture
Specialist
Review gate
Knowledge enters with permission.Product requirements · your retention and privacy controls
01

Knowledge enters with permission.

People can see and pause capture. Approved work is processed locally; raw captures are removed after the required processing.

Product requirements · your retention and privacy controls
CUSTOMER SECURITY BOUNDARY

The knowledge stays
in your environment.

YOUR VPC / ON-PREMISE
  1. Approved knowledge

    Visible capture and local privacy handling.

  2. Customer specialist

    Train and serve inside your environment.

  3. Review gate

    Your policies determine when a person must decide.

Permitted application action

Raw capturesRemoved after required processing.

External reuseOnly audited abstracted structure may inform synthetic data.

Intended architecture and controls. Customer instance data is not pooled.

02

A specialist lives inside.

Designed to train and run in your private cloud or on-premise. Your instance data does not train the foundation or another customer’s model.

Deployment target · topology agreed per engagement
CUSTOMER SECURITY BOUNDARY

The knowledge stays
in your environment.

YOUR VPC / ON-PREMISE
  1. Approved knowledge

    Visible capture and local privacy handling.

  2. Customer specialist

    Train and serve inside your environment.

  3. Review gate

    Your policies determine when a person must decide.

Permitted application action

Raw capturesRemoved after required processing.

External reuseOnly audited abstracted structure may inform synthetic data.

Intended architecture and controls. Customer instance data is not pooled.

03

You govern the path to action.

Review the evidence before an action. Your policies define what is allowed and when a person must decide.

Architecture commitment · human control remains explicit
CUSTOMER SECURITY BOUNDARY

The knowledge stays
in your environment.

YOUR VPC / ON-PREMISE
  1. Approved knowledge

    Visible capture and local privacy handling.

  2. Customer specialist

    Train and serve inside your environment.

  3. Review gate

    Your policies determine when a person must decide.

Permitted application action

Raw capturesRemoved after required processing.

External reuseOnly audited abstracted structure may inform synthetic data.

Intended architecture and controls. Customer instance data is not pooled.

CUSTOMER SECURITY BOUNDARY

The knowledge stays
in your environment.

YOUR VPC / ON-PREMISE
  1. Approved knowledge

    Visible capture and local privacy handling.

  2. Customer specialist

    Train and serve inside your environment.

  3. Review gate

    Your policies determine when a person must decide.

Permitted application action

Raw capturesRemoved after required processing.

External reuseOnly audited abstracted structure may inform synthetic data.

Intended architecture and controls. Customer instance data is not pooled.

Product requirements · your retention and privacy controls

A specialist you can own

A compact model designed for work in enterprise applications and a small GPU footprint.

Room to improve

Run repeated tests in your environment, without sending workflow or design data to an outside provider.

Your knowledge stays yours

Your instance data does not train the foundation model or another customer’s system.

MODEL-BUILDING HERITAGE

Built by people who build winning specialist models.

Our team brings experience building specialist models and testing them against public benchmarks. Digital Human applies the same discipline to expert work.

A patent was filed on the capture-and-train method in 2026. International filings are in progress.

More about the team’s experience

Team members previously trained a healthcare imaging foundation model that outperformed comparable big-tech models on public benchmarks. That earlier work is distinct from Digital Human’s evaluations.

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A CLOSER LOOK

The next layer deserves a conversation.

Explore the architecture, security approach and evaluation evidence with the people building Digital Human. Detailed materials are available under NDA.

Talk with the model team Request the technical paper
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