Who you hire.
Identify candidates most likely to reach the role's defined performance outcome.
Proven in productionTHE PEOPLE DECISION ENGINE
Reduce the cost of the wrong yes.
Reclaim the upside hidden behind the wrong no.
Every people decision carries two costly risks: the wrong yes and the wrong no. Nodes is your internal People Decision Engine, built to reduce both.
It learns from the people your organization previously hired, insured, funded, or admitted and the outcomes that followed. Before the next decision, Nodes shows what is most likely to happen, the evidence behind it, what you risk by saying yes, and what you stand to lose by saying no.
You define the outcome. Nodes shows the evidence. Your team makes the final call.
Every measured outcome compounds a decision advantage built from your own history, one competitors cannot buy.
Past data only. No live decisions.
First production proof: enterprise talent at one top-10 U.S. insurance carrier.
Decision programs
Four decisions. One engine.
The outcome changes. The method does not. Every decision type is built, tested, and governed separately before it goes live.
These are examples, not limits. Nodes starts wherever a people decision repeats, produces a measurable outcome, and leaves enough history to test.
Who you insure.
Help underwriters evaluate applicants against the carrier's approved risk and loss outcomes.
Ready for historical validationWho you fund.
Help credit teams evaluate applications against the institution's approved repayment outcomes.
Ready for historical validationWho you admit.
Help admissions teams identify current applicants most likely to achieve the institution's defined student-success outcome.
Ready for historical validationThe problem
Most systems record the decision. They do not learn from the outcome.
The decision and the outcome live apart.
Your hiring system stores who was hired. Your policy system stores who was insured. Your loan system stores who was funded. Your student system stores who was admitted.
Performance, claims, repayment, and student outcomes appear later, usually in different systems.
When those records never reconnect, the same assumptions keep driving the next decision.
Nodes closes that loop.
Every people decision has two expensive mistakes.
The wrong yes. Your organization selects someone, but the outcome it wanted does not follow.
The wrong no. Your process screens out someone who would have produced a stronger outcome.
Nodes shows where both costs appear in your own history, before the next decision is made.
How it works
You define the outcome. Nodes shows what the evidence supports.
Define
Choose one decision, one measurable outcome, the evidence Nodes may use, the evidence it may not use, the policy boundaries, and the person responsible for the final call.
Replay
Nodes tests past cases against the outcomes that followed. Nothing runs live, and no current applicant is affected.
Recommend
Each current case arrives with a decision-specific recommendation, the evidence behind it, what is missing, and where confidence is limited.
Where the data supports it, Nodes also shows the likely value of acting now and the cost of waiting.
Decide and act
A named person approves, changes, delays, or declines the recommendation.
Approved work is carried out inside the systems the team already uses.
Measure and improve
When the outcome becomes known, it becomes evidence for the next calibration.
Every measured outcome strengthens the next recommendation.
First production proof
The first proof came from enterprise talent.
At one top-10 U.S. insurance carrier, Nodes connected four years of hiring records to actual post-hire production.
Hires scoring 72 or above reached the defined top-performer milestone 2.47 times as often as hires scoring below 72.
Observed time to hire before and after deployment.
Eventual top-producer award winners would have been excluded by the existing filters.
to first production milestone
fewer manual screens
fewer 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.
An experience rule would have excluded 2,863 hires who later generated $17.7M in observed annual premium credit. Supporting results included 109 to 62 days to first production milestone, 40% fewer manual screens, 2 fewer interviews per hire, and $1.58M in year-one savings documented by the customer's finance team.
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These results come from one deployment. Every new company and decision type must establish its own evidence through historical validation.
Historical replay uses past data only. No live decision is touched before the signal is proven.
If Nodes does not beat the agreed bar, the parties recalibrate or stop.
Human control
A person is not a score.
Nodes evaluates one person for one decision, one context, and one defined outcome.
It does not create a universal score that follows someone across roles, companies, employment, lending, insurance, or admissions.
A named person reviews the evidence and makes the final call.
No automated adverse decision
No universal person score
No cross-domain score reuse
No automatic hiring, lending, insurance, or admissions decision
No consequential action without named human approval
Ownership
The model is replaceable. Your decision history is not.
Generic models are available to everyone. Your outcomes, Decision Blueprints, Decision Traces, calibration history, and approved policies are not.
Nodes runs inside your cloud or on-premises. Your customer-owned data and learned assets remain inside your environment.
When the underlying model changes, your organization does not start from zero.
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 historical decision type against one defined outcome. Past data only. No live applicants or operational changes.
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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
At every stage: proceed, recalibrate, pause, or stop.