What real-time qualification actually does to a pipeline.

Across SMB lending, mortgage, and high-ticket sales organizations that added real-time pre-qualification between form submit and CRM push, average unqualified call volume dropped 62% within 45 days.

Methodology:

Pre-post comparison of dialer disposition data from customer CRMs (HubSpot, GoHighLevel, Salesforce). Unqualified is defined as leads dispositioned Not Interested, Bad Fit, Do Not Contact, or No Contact after 3+ attempts within 14 days.

Sample:

38 customer instances across SMB lending, mortgage, coaching, home services, and med spa.

Period:

January 2026 – June 2026

SMB lenders and ISOs who route all inbound leads through OmniaIQ before the first dial saw their lead-to-funded rate move from an industry-typical 3.4% to 11.8% on average.

Methodology:

Cohort analysis of funded loan counts against original lead source volume, before OmniaIQ (T-90 to T-0) vs after OmniaIQ (T+30 to T+120).

Sample:

14 ISOs and direct funders in term loan, MCA, LOC, and equipment finance.

Period:

November 2025 – May 2026

Retail mortgage teams that adopted program-matched pre-qualification (FHA/VA/Conv/Jumbo/Non-QM) saw pull-through rates lift 18 percentage points on average within a full loan cycle.

Methodology:

Pipeline analysis in Encompass and Byte — applications-to-funded, matched against pre-qualification verdict at intake.

Sample:

9 retail mortgage lenders, average pipeline size 240 apps/mo.

Period:

October 2025 – April 2026

Blended cost per funded loan (marketing + LO/closer labor + bureau spend) fell 44% on average when unqualified leads were filtered before the first human touch.

Methodology:

Fully-loaded CPFL calculation: (marketing spend + closer hours × loaded rate + bureau + tech) / funded loans, T-60 vs T+60.

Sample:

21 customer instances.

Period:

December 2025 – June 2026

When pre-qualification fires between form submit and CRM assignment, qualified leads reach a human in under four minutes on average — a 68x compression versus the pre-OmniaIQ baseline.

Methodology:

Timestamp analysis: form submit → first outbound call/text logged in CRM. Only qualified leads counted.

Sample:

26 customer instances.

Period:

February 2026 – June 2026

Nearly a quarter of leads that fail primary pre-qualification are successfully monetized via a next-best-action offer (Buy Now Pay Later, secured cards, credit repair, alt lending) surfaced in the OmniaIQ DNQ file.

Methodology:

Attribution from DNQ file delivery to downstream offer acceptance within 30 days.

Sample:

11 agencies covering coaching, home services, and med spa verticals.

Period:

March 2026 – June 2026

Mortgage LOs working exclusively OmniaIQ-qualified pipelines close 2.6x the funded units per month versus LOs on the same team working mixed pipelines.

Methodology:

Same-team, same-comp-plan matched-pair analysis over two full loan cycles.

Sample:

6 lenders, 74 LOs.

Period:

January 2026 – May 2026

OmniaIQ's routing engine returns a full tri-bureau soft pull + program match + verdict in 1.7s median, 3.4s p95, measured end-to-end at the customer webhook.

Methodology:

Production telemetry, all API calls, 30-day rolling window.

Sample:

All production traffic.

Period:

June 2026 rolling 30 days