Digital Human BY NEUROLOGIC AILet’s talk
TECHNOLOGY / THE SCIENCE OF EXPERT WORKNEUROLOGIC AI

Intelligence begins
with experience.

The most valuable knowledge in your company lives in the decisions people make. We build models that learn from those decisions.

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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.

A decision becomes a trace. The best compliant traces become the starting point for a specialist.

01

Keep the reason with the action.

An approved demonstration connects what the expert saw, what they did and what happened next. Context makes the trace useful.

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.

Only compliant work enters the learning path. Expert-weighted synthesis then favors the strongest measured examples.

Learning method · expert weighting
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.

The model learns a bounded task. Separate, held-out work tests whether that learning transfers beyond the demonstrations.

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.

Real expertise is tacit. The twenty-year adjuster knows which four minutes of policy review prevent the appeal three weeks later. The senior analog engineer knows which device parameter to touch before the simulator confirms it.

Digital Human recovers that expertise from the traces of execution. We observe expert work at the interface level, with consent, align actions with outcomes and train a purpose-built model on validated demonstrations of the best work an organization produces.

THREE PILLARS

Built on three foundations.

01

Break the averaging fallacy

A hard compliance gate first, then expert-weighted synthesis toward the best measured work. The customer holds the dial.

02

Trajectories, not rules

Expertise lives in context, micro-actions, corrections and outcomes. Dense traces teach judgment rather than sequence alone.

03

Compliance-grade execution

Compliance, efficiency, accuracy and usability, in that order. The execution design checks preconditions before an action and verifies the resulting state afterward, with human escalation for uncertainty.

PAST THE IMITATION CEILING

A reward grounded in reality cannot be flattered.

After behavior cloning, the model re-attempts captured work and proposes approaches no single expert tried. It is scored by verified outcomes, with compliance as the first term.

In engineering that outcome is a manufacturability check plus a real circuit simulation. In operations it is the documented process and its postconditions. The model learns both the happy path and the recovery.

EVALUATION PACK

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

Dated engineering and procedural packs are reviewable by 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.

01

Begin with broad capability.

An open foundation supplies the starting capabilities. It does not yet know your industry’s workflow or your organization’s decisions.

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.

The vertical layer adds workflow logic, exception patterns, compliance structure and value flows. Reusable structure informs owned synthetic training data.

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.

Post-train the customer specialist inside your environment, using your approved expert work. Customer instance data is never pooled into another customer’s system.

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

SOVEREIGNTY IS THE ARCHITECTURE

The complete learning and execution loop belongs inside the boundary.

YOUR KNOWLEDGE. YOUR ENVIRONMENT.

The boundary stays. The capability grows.

Follow the intended lifecycle inside one continuous customer environment.

01

Knowledge enters with permission.

Visible, pausable capture and local privacy handling prepare approved work. Raw captures are removed after the required processing step.

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.

Train and serve the bounded workflow in your VPC 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.

The evidence reaches a review gate before an application action. Your policies determine the permitted scope 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

Small enough to own

A compact multimodal model, purpose-built for screen-level enterprise work and designed for a small GPU footprint.

Built for iteration

Self-hosted inference makes repeated optimization practical without sending workflow or design data to an external provider.

No pooled customer corpus

No customer instance data enters the foundation model, and nothing of yours trains another customer’s system.

MODEL-BUILDING HERITAGE

Built by people who build winning specialist models.

Members of this team previously trained a healthcare imaging foundation model that outperformed comparable big-tech models on public benchmarks. The same discipline is applied here: compact, owned models built for one difficult job and evaluated on outcomes that hold up.

The capture-and-train method and expert-weighted synthesis mechanism are covered by the patent filed in 2026, with international filings in progress.

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TECHNICAL DILIGENCE

Ask the model team the hard questions.

Qualified partners and investors can review the security overview, evaluation packs and technical white paper under NDA.

Discuss the architecture Request the white paper
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