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AI Strategy & Architecture
Determine where AI belongs, which use cases justify investment, what architecture is required, and how AI should fit into the existing operating environment.
AI Strategy, Implementation & Decision Control for Regulated Organizations
Deploy consequential AI without giving up control.
GNS-AI helps health plans, hospitals and health systems, medical-device and digital-health companies, and federal agencies design, implement, validate, govern, and control AI in workflows where decisions carry meaningful clinical, operational, financial, regulatory, or public consequences. We work on the data, interoperability, workflow, implementation, validation, governance, and Decision Control problems that determine whether AI can create value in the real operating environment.
Ph.D. Biomedical EngineeringHealthcare data-science leadershipClinical and biomedical AI experienceCreator of Decision Control Plane™
Proof points that travel with the work.Ph.D. Biomedical EngineeringHealthcare data-science leadershipClinical and biomedical AI experienceEnterprise AI, analytics, data, and workflow
What GNS-AI does
Five capability areas for consequential AI.
GNS-AI is an AI consulting and technology company for regulated organizations. Work spans strategy through runtime Decision Control.
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Data & Interoperability
Improve the data quality, semantic consistency, master data, APIs, FHIR connectivity, integration, virtualization, and context access required for AI and automation to work effectively.
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Workflow, Automation & Implementation
Analyze, redesign, automate, and implement workflows involving AI, human review, handoffs, exceptions, and real operating constraints.
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Validation, Governance & Scale Readiness
Evaluate whether an AI initiative works sufficiently well to deploy or scale and establish the Responsible AI, AI Governance, monitoring, oversight, and accountability practices required around it.
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Decision Control
Apply runtime Decision Control when AI materially influences consequential actions and the organization needs to determine where AI creates value, how much responsibility it should receive, and why that authority is justified.
Who We Help
Four primary markets for consequential AI.
Health Plans, Hospitals & Health Systems, Medical Devices & Digital Health, and Federal & Public Sector.
Health Plans
AI, automation, data, and interoperability across prior authorization, utilization management, claims, payment integrity, provider operations, member operations, and other payer workflows.
Explore Health PlansHospitals & Health Systems
AI in clinical, administrative, access, and operational workflows where data fragmentation, workflow variability, implementation, human review, and accountability affect real-world value.
Explore Hospitals & Health SystemsMedical Devices & Digital Health
AI-enabled medical devices and digital-health products where evidence, workflow integration, real-world performance, monitoring, and consequential AI behavior matter beyond initial development.
Explore Medical Devices & Digital HealthFederal & Public Sector
AI in mission workflows where implementation, data, policy, accountability, security, and public consequences shape what can responsibly move into production.
Explore Federal & Public SectorRecognizable buyer problems
Where consequential AI work commonly stalls.
These are the operating problems GNS-AI is built to address.
- An AI pilot performs well in a demo but has not proven enough value to scale.
- Human review, verification, and exception handling are consuming the expected return from automation.
- AI depends on fragmented or inconsistent data across systems.
- Workflow handoffs and exceptions prevent automation from working end to end.
- Leadership lacks a clear basis for deciding where AI can take on more responsibility.
- The organization can show that AI ran, but cannot clearly explain why AI was permitted to influence a consequential action.
- An AI initiative is advancing without enough evidence that it is technically, operationally, clinically, or economically ready.
Consequential AI
AI is moving deeper into workflows. The consequences of being wrong are moving with it.
As AI moves from generating content to recommending, prioritizing, routing, and acting, model performance becomes only one part of the operating problem. Organizations also need to know where AI is creating value, where it has enough operating support to take on more responsibility, and what level of control is appropriate under the actual conditions. They also need to preserve the basis for why AI was permitted to influence or take a consequential action.
Where is AI actually creating value?
AI may reduce one category of work while adding execution cost, review, verification, delay, exceptions, rework, escalation, and failure exposure elsewhere.
Where can AI take on more responsibility?
AI responsibility should reflect the actual task, context, operating history, consequences, and use.
Can you explain why AI was allowed to act?
Decision Control should preserve the basis for why a particular level of AI authority was justified under the conditions that existed at the time.
Operating system, not only the model
The constraint is not always the model.
Consequential AI often exposes problems in the surrounding operating system. GNS-AI starts with the actual constraint and works from there.
Data & Interoperability
Workflow & Automation
AI Strategy, Build & Validation
Responsible AI & AI Governance
Decision Control
Decision Control Plane™
Decision Control for consequential AI
GNS-AI's Decision Control Plane™ helps organizations determine where AI can create value, how much responsibility it should receive, and what level of control is justified under the actual conditions.
Engagement options
Start with the engagement that matches the decision.
Public terms below. Other work is scoped to the operating problem.
$3,500 fixed | 5 business days
AI Initiative Review
A bounded review of an AI, automation, workflow, or data initiative before more capital is committed.
See AI Initiative ReviewScoped
Workflow, Data & AI Architecture
Define the workflow, data, interoperability, integration, operating-model, and AI architecture required for a viable implementation.
See SolutionsScoped
Build & Integration
Implement bounded AI, automation, data, integration, and workflow capabilities when requirements are sufficiently clear.
See Build & IntegrationScoped
Pilot Efficacy & Scale Readiness
Determine whether an AI initiative is producing enough real-world efficacy and value to justify broader deployment.
See Pilot Efficacy3-4 weeks | Starts at $30,000 | Typical $30,000-$45,000
Decision Control Assessment
Evaluate one consequential workflow to determine where AI is creating value, where responsibility can expand, what level of control is appropriate, and whether persistent Decision Control is warranted.
See Decision Control AssessmentScoped
Decision Control Plane
Persistent runtime Decision Control for consequential AI workflows.
See Decision Control PlaneWhy GNS-AI
Technical depth without losing the operating problem.
Founder-led work across biomedical engineering, healthcare data science, clinical AI, and enterprise AI systems.
- Ph.D. Biomedical Engineering
- Healthcare data-science leadership
- Clinical and biomedical AI experience
- Enterprise AI, analytics, data, and workflow experience
- Founder-led AI and analytics work associated with approximately $1.8M in recurring revenue at Abbott Diagnostics
- Creator of the Decision Control Plane™
Dr. Amit K. Shah, Ph.D. | Founder & CEO, GNS-AIFounder-led engagements for regulated organizations deploying consequential AI.
Next step
Have an AI initiative, workflow, or data problem that is becoming harder to control?
Start with the operating problem. GNS-AI can determine whether the next step involves data, workflow redesign, architecture, implementation, validation, governance, or Decision Control.