The pilot worked because the team worked around it.
Manual routing, cleanup, and exception handling may still be doing the hard part.
AI decision systems and control
Why was AI allowed to make this decision?
GNS-AI designs, builds, validates, and controls AI-enabled decision systems for consequential workflows. Most tools can explain the model output. They cannot show why it was allowed to shape the outcome, who had authority, or when the action should have been held.
Built for high-stakes work.Health systemsGovernmentRegulated enterpriseEnterprise dataWorkflow automation
The production gap
Model performance does not establish decision authority.
A workflow still needs evidence, authority, and stopping conditions.
The system sees documents, not the whole case.
A missing prerequisite can matter more than a high-confidence answer.
A score does not show who was allowed to decide.
The organization still has to explain why the action was permitted.
Three ways to begin
Buy the next decision, not an open-ended transformation.
Each engagement has a defined buyer trigger, output, duration, and commercial range.
AI Decision System Working Session
Choose the workflow, assign responsibility, and decide what happens next.
Prep plus half or full day
Typical investment: $15,000–$30,000
AI Decision System Blueprint
Determine what the complete human and AI decision system must be before implementation fixes it in place, who holds authority at each step, and what evidence must exist before an action proceeds.
4–6 weeks
Typical investment: $60,000–$125,000
Production Assurance
Decide whether a pilot, vendor, or workflow is ready to buy, scale, redesign, or stop.
4–6 weeks
Typical investment: $45,000–$95,000
Not ready to scope a larger engagement? Start with a fixed-fee AI Initiative Review: five business days, one call, and a written decision on what the initiative has established, what remains unresolved, and what should happen next.
Review the $3,500 entry offerAbbott DiagnosticsApprox. $1.8M ARRLaboratory benchmarking and reporting program associated with recurring revenue.
Apricity HealthASCO-aligned triageMapped patient-reported symptoms to severity and clinic triage logic.
Federal health educationHRSA-sponsored speakerPrograms focused on accelerating cancer screening and practical AI adoption.
Third-party validationHAVI · INFORMS · DAMAInvited panels and speaking on enterprise AI, decision systems, and reliability.
Dr. Amit K. Shah, Ph.D.Founder and CEO, GNS-AI
Founder-led by design
The person responsible for the decision work is visible.
Dr. Shah brings a biomedical engineering doctorate, enterprise data and AI leadership at Abbott Diagnostics, healthcare AI work at Apricity Health, and federal health education through HRSA-sponsored programs.
Founder experience
Scientific training in human adaptation and decision boundaries, followed by applied work in diagnostics, healthcare AI, enterprise data, simulation, and production systems.
Current GNS-AI capability
Founder-led architecture, decision design, production assurance, and Decision Control Plane shadow-mode work for consequential workflows.
Customer-validated outcomes
GNS-AI is still building customer-validated evidence for the current platform and packaged engagements. Prior operating results are identified as Dr. Shah’s experience, not presented as GNS-AI client outcomes.
Review Dr. Shah’s background and the company’s evidence boundaries
Where we work
GNS-AI works where AI decisions carry real consequences.
Healthcare and payer, federal and public-sector, and enterprise and industrial workflows.
Healthcare & payers
Healthcare Decision Systems
Care, access, utilization, referral, and rehabilitation.
Explore healthcareFederal & public sector
Federal Decision Accountability
Connect mission authority, evidence, acquisition, and operational review.
Explore federal
Enterprise & industrial
Operational Continuity & Modernization
Preserve system knowledge and expose dependencies before critical change.
Explore enterprise & industrialDecision Control Plane™
DCP is the product. Shadow mode is the entry path.
Observe one live consequential workflow, produce decision-level evidence, and define where AI influence should be allowed, held, escalated, or blocked.
Shadow pilot
10–16 weeksBegins at $125,000
Production
Licensed platformWorkflow-volume basedScoped separately
Lead with a real diagnostic
Is the pilot actually ready for production?
Assess before scaling.
Buyer questions
Questions that come up before the first engagement.
The answers determine whether GNS-AI is relevant and what the first piece of work should be.
What kind of AI initiative is a good fit for GNS-AI?
GNS-AI is best suited to consequential workflows where AI influences care, access, money, rights, safety, operations, or public trust. The strongest starting points have a real operating decision, an executive owner, and a problem the organization is already expected to solve.
What can an organization buy first?
The first purchase is a bounded Working Session, AI Decision System Blueprint, or Production Assurance engagement. When AI already influences a consequential live workflow, the entry path can be a DCP Shadow Pilot. Each option ends with a specific decision and defined deliverables.
Does the first engagement commit the organization to a larger program?
No. The first engagement answers a specific decision. Implementation, production deployment, or DCP licensing is scoped only after the evidence supports moving forward.
Start with the current decision
Request a scope for the decision in front of you.
Bring one workflow, the decision that must improve, and the outcome the organization needs.