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.
As organizations adopt more specialized AI tools, the question shifts from whether each product works on its own to whether their outputs form a decision system that can be measured and controlled.
Most systems can explain how a model produced an output. They cannot show why AI was authorized to determine or materially shape the outcome in this case.
Healthcare and payer workflowsFederal and enterprise operationsBuilt for consequential decisions
Build the system. Test the workflow.Then control the authority AI is permitted to exercise.
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.
The recommendation arrives, but the work still waits.
People still move the case, resolve conflicts, and recover failures.
The cases that matter most are rarely the average case.
Missing evidence, conflicting records, and changing conditions appear in production.
A score does not show who was allowed to decide.
The organization still has to explain why the action was permitted.
Explainability cannot show why AI was permitted to decide.
System approvalNot enough
Tool accessNot authority
Model explanationNot authorization
Decision-level recordRequired
Decision control in production
Explainability is not an explanation of authority.
DCP addresses that gap before an action creates a business consequence.
Three ways to enter the work
Build the decision system. Validate the workflow. Control the authority.
GNS-AI can begin with the system that needs to be designed, the initiative that must be tested, or the live decision where AI authority needs stronger control.
Design the system around the real decision.
Connect the workflow, evidence, policies, operational data, people, and AI so the system can support the decision the organization actually needs to make. Knowledge graphs, context engineering, simulation, and agents are tools within the design, not the offer itself.
See the decision-system pathTest what happens outside the demo.
Use realistic cases, exceptions, handoffs, and recovery conditions to determine what deserves to proceed, change, or stop.
Assess production evidenceGovern why AI is permitted to decide.
Make evidence, delegated authority, workflow state, and consequence visible before the action reaches the business.
Explore DCPAbbott 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.
Where we work
GNS-AI works where AI decisions carry real consequences.
Healthcare and payer, federal and public-sector, and enterprise and industrial workflows.
Prior authorization
Prior Authorization Intelligence & Decision Control.
A consequential test case for evidence, review, and accountability.
How we help
Start with the problem that already has an owner and a budget.
The right entry point depends on the decision already in front of the buyer.
Workshops & Training
Get the people who own the workflow in one room and decide what happens next.
Explore programsAI Governance
Define who may approve, pause, override, or stop the use.
Explore AI governanceHow We Work
See what happens in the first engagement and what must be proved before scope expands.
See how we workAI Pilot and Commercial Assurance
Determine whether the pilot justifies a commercial commitment.
AI Pilot and Commercial AssuranceProduction Validation
Test the cases, handoffs, and failures the demo avoided.
Prove readinessDecision Control Plane
Control whether an AI-influenced action may proceed in the current case.
Explore DCPHow we work
Start with one workflow, not a company-wide transformation.
Define the decision, test the workflow, and expand only if the evidence supports it.
Entry points
Working sessionVendor assuranceProduction validationDCP shadow mode
Decision gates
ProceedRedesignProveScale
A decision the organization can use.A usable next step.
Workshops and training
A working session should settle a real question.
Use a briefing or workshop when leaders need to choose a workflow, assign responsibility, or decide whether an initiative should move forward.
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.
Does GNS-AI begin with strategy or implementation?
The starting point depends on the decision the organization must make next. Some teams need a focused working session, others need production validation, vendor assurance, or decision control. The work begins with one workflow and a defined outcome rather than a broad technology program.
How can an organization start without committing to a large transformation?
An organization can begin with a workshop, scorecard, vendor decision, production-readiness assessment, or shadow-mode DCP pilot. Each entry point should answer a specific question before the organization spends more or grants AI more authority.
Start with the current decision
Bring the decision that AI is expected to improve, influence, or make.
Share the workflow, current stage, and consequence of a wrong decision.