VIHA MODULAR EXECUTION

Industrial AI for production

AI that executes.
Not improvises.

VIHA turns process intent into a versioned execution graph. Learned models handle perception and ambiguity; physics, geometry, CAD, and control systems provide precision. Every action earns permission before it runs.

Enter the system → See how it works ↓
VIHA / EXECUTION GRAPH LIVE
01 ESTIMATE state + confidence ✓
02 ORCHESTRATE select the next approved node ✓
03 AUTHORIZE skills + deterministic gates
04 EXECUTE verify + recover + evidence •••
CONFIDENCE 0.997 GRAPH REV 06 ENTER SYSTEM ↗
≈5 µmcustomer precision requirement, medical-device program
Modularevery skill tested independently
Policy-gatedno action executes until gates pass
Edge-firstproduction architecture
01 WHERE TO START

Start with the process.
Build the graph.

We begin with one consequential manufacturing workflow and turn its intent, constraints, and acceptance criteria into an inspectable execution system.

A / PRIMARY — PRECISION MANUFACTURING

Industrial work that cannot be improvised

Inspection, deburring, grinding, and finishing on precision parts, where presentation varies but the process cannot. VIHA composes perception, geometry, planning, control, and verification into a versioned graph.

  • Variable part presentation
  • Contact-rich process control
  • Modular, testable skills
  • Run-level traceability

In build: an FDA-approved nitinol implant process for a U.S. medical-device manufacturer.

Bring us a precision workflow ↗
B / EXTENSION — DIGITAL OPERATIONS

The same operating discipline beyond the cell

The orchestration model can also govern consequential digital workflows. Learned reasoning remains one node inside typed interfaces, permissions, policy, verification, and human escalation.

  • Grounded data access
  • Permissions and policy
  • Structured outputs
  • Audit and escalation

A secondary application of the architecture—not the proof point we lead with.

Scope a bounded workflow ↗
02 THE MISSION

Any manufacturing process should be automatable.

Traditional automation delivers precision only when the world stays fixed. End-to-end AI can absorb variation, but probability alone is not a production system.

VIHA composes learned skills with model-based engineering. Each node has a defined contract; deterministic systems retain authority; every result is verified and turned into evidence for the next improvement.

03 THE ARCHITECTURE

Intelligence is modular.
Authority is deterministic.

The system selects the best primitive for each node—learned model, geometry, planner, controller, or human—and verifies the result before advancing.

PROCESS INTENTCAD + GEOMETRYQUALITY CRITERIALIVE PROCESS STATE
01 / STATE ESTIMATION What is true now?

Fuse sensors, process signals, and context into a structured state estimate.

02 / CONFIDENCE + VIABILITY Is the state usable?

Measure observability, uncertainty, and whether the process remains inside its approved envelope.

03 / VIHA ORCHESTRATORSelect the next approved node

A versioned meta-policy routes work through an inspectable graph—not one model asked to control the process end to end.

GRAPH / MED-NITI-014@6
LEARNEDPerception

Detect, segment, classify, estimate.

EDGE MODEL
MODEL-BASEDGeometry + planning

CAD alignment, kinematics, trajectories.

ENGINEERING
CONTROLMachine execution

Motion, force, timing, safety state.

DETERMINISTIC
VERIFICATIONMetrology + evidence

Compare expected and observed outcomes.

ACCEPTANCE
04 / AUTHORITY LAYERRecipes · tolerances · safety · change control
ACTION MUST EARN PERMISSION
RELEASEExecuteall criteria pass
RECOVERYReobserve or retrystate can be recovered
STOPHold or escalateauthority remains narrow
VERIFIED RUN EVIDENCE→Improve the relevant node. Preserve the operating contract.↺
04 THE EXECUTION GRAPH

Give every skill
a contract.

Each node is independently testable. The graph defines how the system advances, recovers, or stops.

01

Estimate

Combine perception and process signals into a structured state with explicit confidence and viability.

STATE + CONFIDENCE
02

Orchestrate

A versioned meta-policy selects the next approved node based on state, objective, and operating envelope.

GRAPH POLICY
03

Compose

Use the best-fit primitive: a learned model, CAD operation, planner, rule, controller, or human decision.

MODULAR SKILLS
04

Authorize

Recipes, tolerances, safety systems, and deterministic gates decide whether an action can execute.

AUTHORITY + CONTROL
05

Execute + learn

Verify the observed result, recover safely when needed, and turn every run into evidence for node-level improvement.

VERIFY + RECOVER
05 BUILT FOR PRODUCTION

Manufacturing first.
Extensible by design.

The architecture is proven against physical processes where geometry, force, timing, and quality all have to hold.

Digital twin of a dual-robot precision finishing cell PHYSICAL SYSTEM / CELL 01
A / PRIMARY — PHYSICAL

Execution graphs for industrial processes

Translate process intent, CAD, perception, and quality requirements into verified actions on proven industrial hardware.

  • State and pose confidence
  • Versioned skill graphs
  • Safety and quality authority
  • Run-level evidence
B / EXTENSION — DIGITAL

The same operating model for software

Apply typed nodes, narrow authority, policy, verification, and human escalation to consequential digital workflows.

  • Grounded data access
  • Permissions and policy
  • Structured outputs
  • Audit and escalation
06 DEPLOYMENT

The graph stays stable.
The hardware can change.

VIHA separates the process graph from vendor-specific execution. Choose the right robot, camera, learned model, planner, and controller for each node without rebuilding the operating contract from scratch.

LEARNED MODELSCAD + GEOMETRYMOTION PLANNINGINDUSTRIAL CONTROLMETROLOGY
VIHA edge runtime connecting a precision robot, vision, sensing, and motion hardware
EDGE RUNTIMEHARDWARE ABSTRACTION / ACTIVE
THE CASE

Useful work now.
Better skills every run.

Rigorous engineering gets the first process into production. Structured evidence improves the exact node that limited performance.

A / PRODUCTION ROI

Value from the first bounded process

  • Scrap and rework avoided per lot
  • Cycle time and machine utilization
  • Operator hours moved off repetitive, abrasive work
  • Engineering hours per changeover
  • Documentation and audit effort per run
B / DATA FLYWHEEL

Evidence compounds without weakening control

  • Real observations and outcomes captured together
  • Failure modes become replayable test cases
  • Weak nodes improve independently of the full system
  • Validated skills transfer to adjacent parts and processes
  • Approved graphs remain versioned and recoverable

Start with one useful process. Improve from real production evidence. Expand only when the acceptance data supports it.

07 EARLY PROOF

CONFIDENTIAL U.S. MEDICAL-DEVICE MANUFACTURER

A process where failure is measured in microns and parts per million.

VIHA’s first customer program is automating an FDA-approved manufacturing process for a cardiovascular nitinol implant on industrial equipment. The work demands variable-part handling without compromising process control, traceability, or precision.

  1. 01EstimateVision and process state locate the part and quantify uncertainty.
  2. 02ComposeCAD, geometry, learned perception, and the approved process recipe determine the action.
  3. 03AuthorizeTolerances, safety state, tool readiness, and revision control must pass.
  4. 04VerifyMetrology compares expected and observed results; evidence closes the loop.
Discuss a precision workflow ↗
≈5µmcustomer precision requirement
LowPPMcustomer reliability requirement
≈3monthsto build the first system
5peopletechnical build team

Program details and requirements supplied by VIHA. Customer identity remains confidential.

08 THE TEAM

Built by operators
who have shipped it.

CO-FOUNDER / ROBOTICS

Robotics systems

25 years

Deploying production-grade robotics and industrial automation systems.

CO-FOUNDER / DATA + TECHNOLOGY

Data + AI systems

20 years

Building data, technology, and AI systems for real operating environments.

1IIT PhD founding engineer
2robotics engineers

A small technical team built the first precision system in approximately three months.

Start with one bounded objective

What process should
work this reliably?

Talk to VIHA ↗
VIHA, INC. [email protected] DALLAS–FORT WORTH, TEXAS