Rules don't learn. The process you built — the forms, the flows, the logic — was optimized for compliance, not outcomes. We built the infrastructure that changes that.
Decisions are made every minute in your business. Most are losing you money. The leverage is in the decisions. Examine them, and you change the outcomes.
Every closed investigation generates fresh signal — confirmed fraud, false positive, what the adjuster actually found. None of it makes it back to the triage rule. So 40% of worked cases close clean. Real fraud passes through. The queue gets refilled by the same rule the next morning. The loop reads every closed investigation and reweights what gets worked next.
Referral source, claim profile, claimant and provider history, third-party fraud scores, and supervisor notes on what flagged real fraud last quarter.
The agent takes in the structured and unstructured signal the decision needs — inside your systems, against your data. Reads what the rule never could.
An accept-or-reject recommendation, a confidence score, the supporting reasoning, and the single signal that tipped the call.
It scores, routes, flags, drafts, or recommends — only inside the action space you've set. Routes high-confidence flags to SIU, holds the marginal ones for adjuster judgment, clears the rest.
Investigation outcomes in thirty to ninety days, recovery dollars within six months, every supervisor override with the reason, and what the adjuster found on the ground.
Every output is scored against the target in production — not at end of quarter. False-positive rate measured against every closed file, every week.
Referral profiles reweighted by outcome and adjuster finding, provider and claimant flags updated as recovery data lands, and confidence thresholds tuned with each closed investigation.
What the loop learns feeds the next cycle. The agent is sharper on the next decision than it was on the last. The 40% false-positive rate compresses cycle by cycle.
Today lead routing ranges from capacity-based at the simple end to rules-based and scored. None of it learns from outcomes. Some agents underperform. Some cross-specialty placements close at 50%. Aggregator sources retain at half their published rate eight months in. Static models never see this. Our solution learns from every closed outcome — quoted, bound, retained, lost. Next week’s decision runs on every closed cycle, not last year’s rules, or who is most available.
Lead profile, source history, real-time behavior signals, agent close rates on similar leads, and producer notes on what actually bound.
The agent takes in the structured and unstructured signal the decision needs — inside your systems, against your data. Reads intent, not activity.
A qualification tier, a recommended destination, a confidence score, and the single signal that tipped the call.
It scores, routes, flags, drafts, or recommends — only inside the action space you've set. Doesn't override SDR judgment — gives them the why.
Quote outcomes within days, bind outcomes within weeks, retention outcomes at twelve months, and every router override with the reason behind it.
Every output is scored against the target in production — not at end of quarter. Outcome attribution, not activity attribution.
Close rates reweighted by profile and specialty, source quality updated as retention data lands, and qualification thresholds adjusted with each closed cohort.
What the loop learns feeds the next cycle. The agent is sharper on the next decision than it was on the last. The score earns trust by being right more often than the gut.
Cross-sell runs on a spectrum — segmentation rules at the simple end, propensity scores and journey orchestration at the sophisticated end. None of it learns from outcomes. Some segments convert at three times what the model predicts. Some products get suppressed for fatigue rules tuned for a different cohort. Lifecycle triggers fire at a moment that mattered last year. The loop reads every offer outcome — opened, declined, ignored, complained — and reweights propensity, margin, and suppression every cycle. Next quarter’s offer runs on the last ninety days, not the 2022 rule deck.
Customer profile, transaction patterns, life-event triggers, conversion rates on similar segments, and banker notes on what actually opened the account.
The agent takes in the structured and unstructured signal the decision needs — inside your systems, against your data. Reads life events and intent — not last year’s segment.
A ranked product, a delivery channel, a confidence score, and the single signal that tipped the offer.
It scores, routes, flags, drafts, or recommends — only inside the action space you've set. Banker holds final sign-off — the agent recommends, never sends.
Engagement within days, funded accounts within weeks, RoRWA and retention at six months, and every banker override of the recommended offer with the reason behind it.
Every output is scored against the target in production — not at end of quarter. The model sees its own mistakes — funded vs. ignored, by segment.
Propensity reweighted by segment and product, margin priors updated as funded data lands, and suppression thresholds adjusted with every closed cohort.
What the loop learns feeds the next cycle. The agent is sharper on the next decision than it was on the last. The 2022 rule deck stops running 2026 offers.
Lumen. An AI decision intelligence platform for financial services.
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