Read: The Outcome-Based Enterprise
Proactive intelligence · by industry

Connected work. Industry-specific outcomes.

Start with a responsibility your business wants to improve. The Nodes platform design connects relevant company intelligence with the capabilities, people, and permitted systems needed to address it.

Insurance candidate evaluation at one Fortune 500 carrier is the production reference. Broader autonomous teams, capability creation, and outcome learning are product direction. The additional examples below are proposed applications to scope and validate, not claims of production deployment.

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Work
People · sales · operations
Deployment
Cloud · VPC · on-premises
Control
Configured permissions
Proof
Insurance talent
Choose the work

A shared platform. A specific operating problem.

Operational responsibilities include onboarding, activation, productivity, and exception handling. Predictive decision programs such as selection or underwriting need additional outcome validation. A job may use one system or several; scope follows the work.

Insurance · talent Proven in production

Connect hiring with producer performance.

The production application evaluates candidate and assessment evidence against configured hiring criteria. Historical research connects hiring records with later production. Those findings inform the broader intelligence design without establishing an automated learning loop.
Candidates scored · since Jan 2025
900,000+
Study cohort · four years of production data
10,765
Time-to-hire · requisition to hire
127 → 38d
First production milestone
62 vs 109d
The Fortune 500 insurance deployment has been live since January 2025 across 200+ locations nationwide. A named person makes the final hiring decision. Timing figures are observed within this reference deployment.
Outcomes the customer may define
Performance milestones Productivity Ramp completion Quality
Reference carrier Post-hire outcomes Human approval
See insurance talent → Explore your workflow →
Insurance · operations Illustrative · validation required

Carry a policy review through the handoffs.

Connect policy records, claims history, and the review queue. An AI team could investigate an observed change, assemble the supporting file, and propose the follow-up workflow. Approved steps update the permitted systems while the responsible underwriter retains the decision.
The carrier defines which outcome to measure, the permitted evidence, and the escalation rules. This is an illustrative application, separate from the production insurance talent program.
Outcomes to validate
Claims frequency Claims severity Loss performance Review time
Carrier-defined outcome Permitted evidence Human review
Explore the workflow →
Financial services Illustrative · validation required

Connect advisor ramp and customer follow-through.

For advisor ramp, an AI team could connect CRM activity, HRIS milestones, and LMS training. It investigates delays, prepares a development workflow, and coordinates approved assignments and follow-ups across those systems.
A separate service workflow could connect customer requests to the operating team responsible for resolution. Banking and wealth-management applications have no claimed production results here. Credit decisions require their own legal review, outcome definition, and historical validation.
Outcomes to validate
Time to productivity Training completion Service resolution Follow-up completion
Legally permitted evidence Named operating owner
Explore financial-services talent →
Higher education Illustrative · validation required

Connect student progress with the support workflow.

An illustrative AI team could connect permitted student records, LMS progress, and advising notes to identify a missed milestone. It prepares a support plan, routes the required approval, and coordinates authorized follow-up across those systems.
The institution defines success and measures what followed the intervention. Admission recommendations are a separate high-stakes decision program requiring their own historical validation and named human authority.
Nodes reports where historical evidence is incomplete and does not claim to know how every previously rejected applicant would have performed.
No higher-education production deployment or measured customer result is claimed.
Outcomes to validate
Academic progress Credit completion Persistence Graduation
Institution-defined outcome Evidence limits Admissions team decides
Explore the workflow →
Cross-industry operations Illustrative · validation required

Take a vendor renewal from signal to result.

Connect contract dates, software usage, and finance records. An AI team could flag an upcoming renewal with unused capacity, prepare a change for procurement review, then coordinate approved updates and communications across the connected systems.
The workflow checks later invoices against the agreed baseline. A newly discovered opportunity returns as a proposal; it does not silently broaden the team's authority or establish a customer savings claim.
Required before validation
Defined outcome Historical evidence Named owners Human approval
Separate Blueprint Separate validation
Explore the workflow →
Customer control

Choose the deployment boundary for the work.

procurement · data-flow review questions a regulated buyer should resolve
deployment review

Agree the boundary before connecting systems.

  • Where does customer data move?
  • Where do model weights and inference run?
  • Who controls retention and access?
  • Can the customer reconstruct each decision?
  • Can one customer's history affect another?
procurement gate
Nodes · three deployment models

Match the configuration to your controls.

  • Nodes Cloud: a Nodes-managed environment
  • Customer VPC: a single-tenant customer boundary
  • On-premises: customer-managed infrastructure
  • Access, retention, model paths, and ownership agreed per deployment
  • Decision Traces preserve the evidence and result
Private VPC and on-premises boundaries can enforce zero customer production-data egress. Review the deployment controls
Deeper review · high-stakes decision programs

Explore a decision program.

Selection, underwriting, lending, and admissions are predictive programs. Each needs its own outcome, permitted evidence, policy rules, historical validation, and human authority. This deeper review is separate from the operational workflow examples above.

decision validation boundary select a decision program
production proof · enterprise talent
The first production proof came from hiring

At one Fortune 500 insurance carrier, Nodes connected candidate evidence to observed post-hire outcomes. These results do not transfer to another company or decision type by assumption.

decision Enterprise talent
defined outcome Role-defined performance milestone
Time-to-hire
38d
down from 127 · requisition to hire
Candidates scored
900K+
since January 2025 at the reference deployment
Study cohort
10,765
agents · four years of production data
Q1 net savings
$1.58M
CFO-validated at the reference carrier
validation checksstatus
Outcome definition
approved by the customer
required
Permitted evidence
bounded in the Decision Blueprint
required
Historical validation
held-out results reviewed before promotion
required
Human authority
named owner approves, edits, or declines
required

Where the architecture can extend next

Nodes works within configured permissions. Actions requiring approval remain gated by the customer's policy. Named people control consequential decisions and new production scope. Routine work already authorized by that policy can continue.

Explore your use case
customer-specific evidence · selected deployment boundary separate validation per decision
Production evidence

Production proof today comes from enterprise talent.

These results came from one Fortune 500 insurance carrier. Every new company and decision type must establish transferability through historical validation.

Insurance · Q1 2025 net savings $1.58M

Customer-attributed Q1 net savings, validated by the carrier's CFO.

One Fortune 500 insurance deployment
Insurance · time-to-hire 127 to 38d

Requisition to hire moved from 127 to 38 days at the same carrier. Separately, time to the first production milestone moved from 109 to 62 days, a 47-day acceleration.

Fortune 500 insurance carrier · reference deployment
Insurance · screening exposure $17.7M

Annual production associated with 2,863 agents an experience rule would have excluded in a retrospective counterfactual. This is not realized savings.

Published research · arxiv.org/abs/2604.19819
Private deployments · customer production-data egress 0bytes

Applies where the approved customer VPC or on-premises boundary enforces it. Nodes Cloud uses a separately defined Nodes-managed boundary.

Configuration-specific control

Each decision type receives its own validation.

A talent model does not become a credit, underwriting, or admissions model. Each program receives its own outcome definition, permitted evidence, policies, validation, approval path, and Decision Trace.

Request a walkthrough See how the loop works
Attestation and decision-program review

SOC 2 attested. Alignment and deployment reviewed separately.

SOC 2 Type II is the current Nodes attestation. HIPAA aligned and GDPR aligned describe operational alignment, not certifications or automatic compliance. The formal DPA and subprocessor schedule are in preparation with counsel. Review the applicable obligations for each deployment.

review matrix attestation · documents · domain scope
Framework Insurance talent Underwriting Lending Admissions Other decisions
SOC 2 Type II
Formal DPA
Formal subprocessor schedule
Domain-specific legal review
Historical validation
Production proof
current or proven in preparation or validation required no production proof SOC 2 materials available under NDA

One process. One owner. A place to start.

Tell us what you want to improve. We will show how Nodes would approach it across your systems.

Request a walkthrough See the architecture

No dataset required for the initial walkthrough.

  • The goal and the process it affects
  • The systems and deployment options
  • The approval path and operating owner
  • A suitable starting point and measurement plan
Platform architecture Decision Replay
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