Hiring
Nodes evaluated 900,000+ people using location-specific top-performer profiles across 200+ locations nationwide. The carrier hired 10,000+ insurance agents from that pool.
Insurance · live since January 2025
Three workflows at one Fortune 500 insurance carrier.
Nodes supports insurance-agent hiring, licensing progress and educational support at the same Fortune 500 insurance carrier.
Licensing
Nodes identified a gap between sign-up and licensing completion, then implemented approved reminders based on each person's latest activity and remaining progress.
Educational support
Nodes identified a support gap and built and deployed an educational assistant that understands each person's context and works with them.
The historical hiring evidence below has its own populations, dates and definitions. The 10,000+ hires are distinct from the 10,765-person research cohort; the historical hiring metrics do not measure licensing or educational support outcomes.
Historical application evidence · insurance · live since January 2025
Historical hiring evidence
This case documents Nodes’ historical candidate-evaluation application at one Fortune 500 insurance carrier. It reports $1.58M in Q1 2025 net savings, validated by the customer’s CFO, alongside the deployment records and separate retrospective research below.
Application-specific evidence · Customer records and retrospective research · These findings do not establish results for every Nodes workflow.
Requisition to hire · 2025127 → 38 daysObserved
Candidates scored since Jan 2025900,000+Observed
Deployment footprint215+ locationsObserved
Research cohort · 2022 to 202510,765 agentsObserved
The case
The filter had never been tested against production.
The carrier operated a high-volume hiring funnel across 215+ locations. The study tested familiar screening signals by joining historical hiring records to later production outcomes.
It examined whether the evidence used to assess candidates was associated with the defined production milestone, while preserving the limits of a retrospective analysis.
The familiar signals did not hold up.
Of 8,181 parsed skills, 3,597 had enough data to test. After Bonferroni correction, zero predicted the first production milestone and 30 were anti-predictive.
Connected evidence supported further testing.
The keyword analysis reported AUC 0.558. In a separate small sample of n=229, behavioral assessment alone produced cross-validated AUC 0.647 and fused ATS, assessment, and behavioral evidence produced AUC 0.735. These analyses are bounded by their samples and methods.
The production milestone arrived 47 days earlier.
The reference cohorts moved from 109 days to 62 days to the first production milestone. The live requisition-to-hire loop moved from 127 days to 38 days.
One rule would have rejected 2,863 producing agents.
The historical replay identified producing agents the industry-experience filter would have removed. Their annual production represented $17.7M at risk in the retrospective counterfactual. This does not represent incremental revenue caused by Nodes or realized savings. Inspect the modeled claim.
AUC describes ranking performance in the stated sample. The time and value findings come from one carrier. They do not establish universal accuracy, causality, or future lift.
Observed production record
The deployment moved from contract to production in 34 days.
Legal approval took 17 days after six AI hiring vendors had been rejected over 18 months on architecture. These are results from one deployment, not a timeline promise for another environment.
17 days · legal approval · one customer record
34 days · contract to production · one deployment
Live since January 2025
Expanded two quarters ahead of schedule
Application and research boundary
What the historical hiring evidence establishes.
The production application evaluates resume and assessment evidence against configured hiring criteria. Named people retain hiring authority. Quarterly production reports and retrospective research provide outcome evidence; their existence does not establish that the application automatically learns from them.
01 · Ingest
Read approved records.
Production evidence stayed inside the approved customer VPC boundary.
02 · Evaluate
Review candidate evidence.
The application supports candidate evaluation against configured criteria. A named person makes the hiring decision.
03 · Analyze
Study the historical outcome.
The retrospective research links records to the customer's defined production milestone, with its methods and limits published separately.
04 · Distinguish
Keep the application scope clear.
These records establish results for this application and period. Evaluate the current platform against the workflow and outcomes your company needs.
Broader platform · evaluation standard
Ask to see the unfamiliar job, the exception, and the next case.
A separate capability demonstration should show relevant context and gaps, reused or newly tested capabilities, a human-refined plan, permitted execution, and recovery from an exception. Then test whether a later result changes the next applicable recommendation.
Measure engineering effort, customer intervention, verified effects, and learning against a prior or no-memory baseline. The product example illustrates current capabilities in a fictional service workflow. Its records and outputs are illustrative, rather than additional customer evidence.
Evidence record
Observed, validated, and modeled claims stay distinct.
Each number retains its population, period, method, and limitations. The full evidence register remains available as a separate inspection surface.
| ID | Claim | Value | Population | Method | Status |
|---|---|---|---|---|---|
| C01 | Candidates scored | 900,000+ | One Fortune 500 carrier | Live production record since January 2025 | Observed |
| C05 | Research cohort | 10,765 agents | Agents at one carrier | Retrospective observational study, 2022 to 2025 | Observed |
| C20 | Q1 2025 net savings | $1.58M | One live deployment | CFO-validated customer record | Observed |
| C21 | Annual production at risk | $17.7M | 2,863 producing agents | Retrospective counterfactual | Modeled |
| C25 | Time to first production milestone | 109 to 62 days | Reference carrier cohorts | Historical median comparison | Observed |
| C26 | Requisition to hire | 127 to 38 days | One live deployment | Customer production record, 2025 | Observed |
| C41 | Predictive keywords after correction | 0 of 3,597 | Testable keyword set | Bonferroni-corrected testing | Validated |
Methodology: Decision Traces, arXiv:2604.19819. Complete claim records and supporting artifacts are available through the evidence register and deployment data room.
Find your starting point
What should Nodes take on in your business?
Explore one workflow in a walkthrough, with no dataset required. Discuss a suitable implementation scope and how to measure it afterward. Historical Decision Replay remains a separate, optional evaluation.