Understand the business across systems.
Nodes joins permitted records, rules, decisions, and results into company knowledge. Each team draws on the context and tools it is allowed to use.
Platform capability
Outcome-Based Decision Engine
AI that runs the work and finds what to improve next.
Nodes learns how your business works and builds AI teams to take on ongoing work across your systems. They investigate opportunities, coordinate approved workflows, and bring you the next useful action without waiting for another prompt. Nodes measures the results and carries relevant learning forward.
Your people approve consequential decisions. Your company keeps the knowledge.
Human Gate
Named owner
required
required
First production proof
Fortune 500 insurance carrier
Candidates scored since January 2025
900,000+
Observed
Q1 net savings, CFO-validated
$1.58M
Validated
Deployment footprint
200+ locations
Observed
What Nodes is
It keeps working between your requests.
You define the objective and the area of responsibility. Work can start from your request or from a relevant change Nodes identifies. Its agents keep observing permitted business records, assemble the evidence, and prepare the next steps across systems.
Agents and workflows keep approved work moving. A named human approves consequential decisions and changes to how the workflow operates. Results return to the company knowledge that informs the next action and proposed improvement.
Assemble teams and run the work.
Nodes organizes specialists around the objective and coordinates investigation, planning, approvals, execution, exceptions, and follow-through.
Platform capability
Find useful next actions.
As connected records change, agents investigate delays, waste, and opportunities. A proposal arrives with evidence, expected value, limitations, and the next steps prepared.
Inspectable record
Learn from measured results.
Decision Traces connect evidence, human input, action, and the later result. Relevant knowledge carries forward; every new decision program still needs its own validation.
Customer-specific
Prove it on your history first
One decision. 72 hours. A written $1M evidence bar.
Bring one repeated high-stakes decision and at least two years of linked decisions and measured outcomes. Nodes first checks volume, linkage, outcome coverage, policy overlap, and data quality. Your finance team defines the $1M evidence bar and calculation in writing.
The 72-hour clock starts only after the agreed deidentified dataset is delivered under a mutual NDA and accepted by Nodes. The Replay is historical and read-only. Nodes returns the evidence, limitations, and whether the record supports the written bar. No live decision is touched.
Clock starts after the agreed dataset is delivered and accepted
Historical replay only, with no live decisions touched
No production fee if the written bar is unsupported
The customer keeps the analysis
One engine · four decision domains
Broad platform. Separate proof for every decision.
Each decision program starts with its own outcome definition, historical replay, evidence rules, and human authority. Results from talent do not transfer into risk, customer, or operating decisions.
Who to hire, develop, or support.
Connect candidate and workforce decisions to production, development, and approved performance outcomes.
Hiring proven in production
Which risks warrant action.
Replay underwriting, credit, claims, or policy decisions against the risk outcomes the business already records.
Decision Replay first
Where intervention creates value.
Connect prioritization and service decisions to retention, expansion, resolution, and approved customer outcomes.
Decision Replay first
What the organization should prioritize next.
Link operating choices to cost, service, capacity, and risk so the next workflow starts with evidence.
Decision Replay first
The missing connection
The decision and the outcome live in different systems.
A CRM records customer activity. An HRIS records performance. An ATS records the hiring decision. Finance records revenue and cost. Claims and policy systems record risk outcomes.
When those records stay apart, a rule can remain in place for years without being tested against the result it was meant to predict. Nodes reconnects the evidence, recommendation, reviewer, approved action, and measured outcome.
The architecture
One governed platform above the systems you already use.
The outcome engine, specialist agent workforce, customer context graph, and named human gate run as one inspectable system. Your existing systems of record remain the source of truth.
01 · Learn
Connect prior decisions to results.
Nodes reads approved records across systems and finds which signals held up against the outcome.
02 · Recommend
Return the next action with evidence.
The recommendation includes coverage, uncertainty, limitations, and a drafted workflow.
03 · Decide
Keep a named human in authority.
The decision owner can question, edit, approve, delay, or decline the proposed action.
04 · Act
Execute across the systems involved.
Agents and workflows carry out only the permitted work the named owner approved.
05 · Measure
Measure the result. Investigate what comes next.
Observation continues as work runs and outcomes arrive. Results join the Decision Trace; further opportunities return as proposals for review.
First production proof · insurance talent
Four years of outcomes exposed the cost of one untested filter.
At one Fortune 500 insurance carrier, a retrospective replay found 2,863 producing agents that an industry-experience filter would have rejected. Their annual production represented $17.7M at risk. This is a counterfactual finding, not incremental revenue caused by Nodes.
127 to 38 days · requisition to hire · observed
$1.58M · Q1 net savings · CFO-validated
2,863 producing agents · retrospective counterfactual
$17.7M annual production at risk · modeled
17 days from first call to legal approval. Six vendors were rejected before us.
Fortune 500 insurance carrier · procurement + legal + security review · 2024
Deployment
One engine. Three deployment models.
Choose the boundary that fits the operating and security requirements. The decision model, human gate, agents, workflows, and outcome loop remain consistent.
Nodes Cloud
A Nodes-managed cloud environment with the operating boundary defined in the customer agreement.
Managed
Customer VPC
Single-tenant and VPC-resident inside the customer's AWS, Azure, or GCP environment.
Private
On-premises
Customer-managed deployment for environments that require infrastructure inside an on-premises boundary.
Customer-managed
Model optionality
Use approved commercial or open models without rebuilding the customer context graph and decision history.
Model-agnostic
Zero customer production-data egress applies to private deployments whose approved boundary is configured for it. Deployment terms are defined per customer.
Ownership and portability
Swap the model. Keep the intelligence.
Models change. Your outcomes, Decision Traces, calibration history, and approved policies remain customer-controlled. A new model starts with the decision history your organization has accumulated.
Your competitors can buy the same foundation model. They cannot buy the history of which decisions worked inside your company.
Decision Replay
Choose one high-stakes decision. Test it against what happened next.
Bring one repeated decision, one accountable owner, and the outcomes already recorded across your systems.