Who you hire.
Identify the candidates most likely to reach the role's defined performance outcome.
Proven in productionThe People Decision Engine
Better outcomes start with who.
Every system records who was chosen. Nodes connects that decision to what happened next, then helps your team identify which current candidates or applicants are most likely to achieve the result you care about.
Every recommendation shows the evidence, drafts the next step, and waits for a named person to approve it.
Past data only. No live decisions.
Decision programs
Four decisions. One engine.
The outcome changes. The loop does not. Each decision type is trained, tested, and governed separately. Nothing transfers into production by assumption.
Who you insure.
Help underwriters evaluate applicants against the carrier's approved risk and loss criteria.
Ready for historical validationWho you lend to.
Help credit teams evaluate applications against approved repayment outcomes.
Ready for historical validationWho you admit.
Help admissions teams identify applicants most likely to achieve the institution's defined student-success outcome.
Ready for historical validationThe problem
Every system records who you chose. Almost none learn from what happened next.
The decision and the result live apart.
Your hiring system stores the applicant. Your policy system stores the application. Your lending system stores the credit decision. Your student system stores the admission.
Performance, claims, repayment, and student outcomes live somewhere else.
When those systems never connect, old assumptions keep making the next decision.
Nodes connects each decision to the result that followed and tests which permitted signals consistently held up.
The line Nodes never crosses
Some checks aren't filters. They're the law. A license, a background check, a credential: Nodes never re-ranks, relaxes, or overrides those. It only helps you choose among people who have already passed every one.
How it works
You define the outcome. Nodes shows what predicts it.
Define the decision and outcome.
Your team defines the decision, measurable outcome, permitted evidence, constraints, and person responsible for the final decision.
Replay the history.
Nodes tests historical cases against the outcomes that followed. Held-out validation compares Nodes with the current process. Nothing runs live.
Recommend with evidence.
Each current case arrives with a decision-specific recommendation, the evidence behind it, the limits of that evidence, and any required policy checks.
Decide and act.
A named person approves, changes, delays, or declines. Approved steps run inside the systems your team already uses.
Measure and recalibrate.
When the result becomes known, it becomes evidence for the next calibration.
Evidence available. Human decision. Measured outcome.
First production proof
The first proof came from hiring.
These results came from one top-10 U.S. carrier deployment. They are not universal results.
Hires Nodes scored 72 or above were 2.47× more likely to reach the company's top-performer milestone than hires it scored below 72.
Time to hire dropped from 127 days to 38.
The company documented $1.58 million in first-year savings.
Days to first production milestone.
Fewer manual screens.
Interviews per hire.
Absolutely love what you guys are doing. Sometimes I sit down and start thinking of so many more things we can do with this.
At that carrier, an experience rule would have excluded 2,863 hires who later generated $17.7M in observed annual premium credit. Forty of fifty eventual top-producer award winners would have been screened out. More than 3,000 screening keywords were tested against 10,765 hires and their actual production.
0
The number that matters most here is zero: the count of live decisions Nodes touched before it proved itself on the company's own past data.
That's the deal for you too. We test Nodes on your history first. If it doesn't beat your current way, we stop, before anything ever goes live.
Today, the proof is in insurance. The deal is the same in your industry: it proves itself on your history first, or we stop.
Measure and improve
When the result becomes known, it becomes evidence for the next calibration.
Every approved action creates a record. When the result is known, it becomes evidence for the next calibration.
Nodes learns from outcomes, not by watching people.
New evidence may confirm a signal, weaken it, or require recalibration. Nothing transfers into production by assumption.
The decision history and company-specific learning carry forward when the underlying model changes.
Ownership
Your competitors can buy the same model. They cannot buy your history.
Nodes runs inside your cloud or on-premises. Nothing consequential happens without named human approval.
Your data, customer-specific learned weights, Decision Traces, and outcome history remain inside your environment.
When the underlying foundation model changes, your organization does not start from zero. The decision history and company-specific learning carry forward.
Built from your outcomes. Governed by your people. Run in your cloud.
Start
Prove it on your history before it touches a live decision.
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01
Decision Replay
Test one past decision type against a defined outcome. No live candidates, applicants, loans, policies, or admissions decisions.
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02
Shadow Mode
Run Nodes beside the current process. Compare recommendations case by case while nothing changes operationally.
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03
Controlled Deployment
Go live for one approved decision type. A named person approves every consequential action while Nodes measures value, adoption, and impact.
Replay a past decision
Bring the person who owns the outcome and the person who owns the data. At every stage, your team can proceed, recalibrate, pause, or stop.