Insurance candidate evaluation.
Live at one Fortune 500 carrier since January 2025. Reported Q1 2025 net savings of $1.58M were validated by the customer's CFO.
Review the result and its scope The proactive intelligence layer for your business
Company intelligence. The work it makes possible.
Start with a business result worth improving. Nodes' platform design connects company evidence with the AI teams and workflows needed to pursue it. Your people set the authority. The result determines what to change next.
Insurance candidate evaluation in production since January 2025. Review the production reference, delivery scope, and platform design.
Start with a defined engagement
Know what you are buying. Know who delivers it.
Agree on the business result, working capabilities, implementation required, and acceptance criteria before committing to a rollout.
A responsibility with a business owner.
The proposal identifies the capabilities available for your use case, required configuration or development, delivery owners, and acceptance checks. Implementation effort and customer responsibilities are explicit.
See how the agreement is scopedAdaptive work and retained intelligence.
Dynamic capability creation, the general autonomous runtime, and automated outcome learning are product direction. Review demonstrated behavior and development dependencies for your proposed scope.
Explore the architecture How the work gets done
Four steps. One continuing responsibility.
In the platform design, an assigned objective or an observed change starts the same loop. Observation uses permitted events, scheduled checks, or reporting feeds at an agreed cadence.
01
Connect the context
Bring relevant records, company rules, and past results together across permitted systems.
02
Assemble the AI team
Investigate first. Reuse a tool or workflow, compose specialists where needed, or ask for missing evidence. Support expected value with explicit assumptions.
03
Carry out approved work
Coordinate steps across the connected systems. Surface approvals and exceptions to the accountable owner.
04
Measure the result
Verify the action separately from its later effect. Test whether a proposed lesson helps the next applicable case.
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.
One governed pathPlatform design illustration
Illustrative workflow: reduce avoidable software spend. A renewal assumes the old seat count although usage has fallen. The proposed response reuses contract extraction, adds a usage comparison, and asks about a missing service dependency. Procurement can amend the plan before authorizing outreach and updates.
The job remains open for later invoice evidence. If the expected saving never appears, completion is recorded separately from the disappointing result. A revised recommendation needs supporting evidence.
Under the hood · product direction
One foundation. Reusable capabilities. Active work.
The company keeps the intelligence. The coordinating engine decides what response fits. A bounded AI team takes responsibility for the authorized work. These are different parts of the design, rather than a fixed catalog of agents.
Keep what the business has learned.
A permission-aware context graph connects evidence, human judgments, decisions, actions, and measured outcomes. Decision Traces preserve why a choice was made, what followed, and where the lesson applies.
Prepare the response the problem needs.
The coordinating engine investigates and selects reusable capabilities. A specialist Engine packages tools, instructions, and evaluations. The capability factory is designed to compose or test missing pieces. The Outcome-Based Decision Engine supports decision evaluation within this platform.
Give the work an owner and limits.
A Decision Pod applies those capabilities to one mission. Agents do assigned work; the workflow defines dependencies and effects. Its owner, permissions, budget, checkpoints, and exception path bound the responsibility.
Preserve the result. Test the lesson.
Human approval, successful execution, and business improvement are separate evidence. Failed and inconclusive outcomes remain in the record. A proposed lesson needs scope and evaluation before reuse or a production change.
Decision Blueprint
Define the decision before a model evaluates it.
For predictive and high-stakes decision programs, define the outcome, population, permitted evidence, policies, decision rights, actions, measurement window, success threshold, and abstention rules. Other tasks use the objective and controls appropriate to their scope.
Outcome and population defined first
Evidence and policy approved by the customer
Owner and action controlled by a human
Proof and abstention validated separately
The proposed review surface
A recommendation needs a reviewable record.
An Evidence Drawer should expose sources, counter-evidence, gaps, and human corrections beside the proposed response. Ask Nodes provides a way to question or refine that same record.
A decision-specific likelihood, priority, or next step tied to one approved outcome, with confidence and evidence coverage.
The evidence, signals, models, policies, recommendation, human input, execution, and measured outcome.
The next permitted workflow. Actions requiring approval remain gated by customer policy; routine work can continue within existing authority.
Evidence limits
Sometimes the right next step is a question.
A missing source, conflicting evidence, or an unsupported population can justify asking for information, waiting, or leaving the process unchanged. A useful response does not always need a new team or workflow.
Evaluation must test these cases as carefully as successful execution. Missing evidence and unresolved uncertainty belong in the Decision Trace.
Software-assisted implementation
Built around your business. Reusable for the next job.
Our deployment team scopes the work with you. The software-assisted implementation design below targets repeat discovery, configuration, and maintenance effort. Your delivery plan separates software capability from work owned by our team and yours.
01
Understand the environment
Inventory permitted systems, schemas, available actions, and missing evidence. Record what can be reused.
02
Prepare the capability
Identify missing mappings or tools, draft proposed extensions, and test them before activation. Uncertain mappings need human verification.
03
Put it into operation
Agree the owner, approval boundary, success measure, and acceptance checks. Name the work retained by your team and ours.
04
Keep it usable
Test detection of changed schemas or permissions, safe pauses, repair proposals, and escalation. A generated repair needs validation before release.
Implementation design: test connector coverage, schema-change handling, and the human work required for your scope.
Deployment
Run Nodes in the environment the decision requires.
The same platform can run in Nodes Cloud, inside a single-tenant customer VPC, or on customer-managed on-premises infrastructure.
Managed by Nodes.
Use a Nodes-managed cloud environment with security, data, and operating terms defined in the agreement.
Run inside AWS, Azure, or GCP.
Use a single-tenant, VPC-resident deployment within the customer's approved cloud boundary.
Run on customer-managed infrastructure.
Support operating environments that require the complete platform inside an on-premises boundary.
Change models without discarding history.
Use approved commercial or open models while the governed context and outcome history carry forward.
Data residency, model access, telemetry, ownership, and exit terms are defined for the selected deployment model. Zero customer production-data egress applies where the approved private boundary supports it.
Two proof layers
Measure execution and decision value.
Track completion and business value separately. Compare the later result with the agreed baseline, including implementation cost and retained customer effort. Record uncertainty about what caused the change.
Operational proof
- Did the workflow complete?
- Did each agent follow the approved process?
- How did time, capacity, and exceptions change?
- Where did execution fail or abstain?
Decision proof
- Which recommendation did the evidence support?
- What did the named owner decide?
- What business outcome appeared later?
- How should that result change the next decision?
Acceptance before expansion
Agree the evidence that earns the next commitment.
Predictive decision programs need historical evidence and a scoped evaluation. Operational workflows need suitable execution, permission, and recovery tests. Agree what acceptance requires before production; a product walkthrough does not require a historical dataset.
Test completed cases against observed outcomes and preserve limitations.
Compare the candidate system with the current process without changing live decisions.
Go live for one approved decision with named human authority and ongoing outcome measurement.
Product walkthrough · your first use case