Platform · Call Monitoring
Every call scored against your SOPs and the regs. Nothing sampled.
Sei encodes your rulebook — including the edge cases that live in your reviewers’ heads — and scores every interaction against it, with the violating moment cited to its timestamp.
Every interaction, end to end
4 beats- 01Ingest
Calls, emails, chats and external review channels land in one record per interaction, transcribed and attributed turn by turn.
- 02Score
Each interaction is scored against your SOPs, your QA rubric and the regulations that apply to it — across more than thirty dimensions
- 03Surface
Violations, coaching opportunities and complaint trends reach your team with a severity score and the moment in the conversation that produced them.
- 04
The outcome
1,000+
calls audited a month, none sampled — Better Mortgage
Read the Better Mortgage study
- Purpose of call stated00:41 · SOP 4.1
- Rate discussed before disclosure02:14 · TILA
- No steering language06:03 · Fair Housing
- Fees restated accurately08:52 · RESPA
What you staff
2 of 16 desksCall Monitoring is built for regulated finance, with guardrails and domain-specific evals. Every change is tested against simulations and measured against those evals before it reaches a live file.
Proof
Better Mortgage audits LO calls with a compliance agent built with mortgage regulations.
- calls audited per month
- 1,000+
- analyst productivity
- 4×
- Sampling & Manual review rate
- 0%
Call auditing went from 4 a day, against a backlog of more than 1,000, to a full month of calls scored in under an hour — with no sampling.
Read the Better Mortgage studyCall Monitoring on a scored interaction
A scored conversation with the findings beside it — what was flagged, under which rule, and where.
The channels it scores, and what it scores them on
One scorecard per interaction, wherever the interaction happened and granular dashboards & reports
- Channels it scores06
- Inbound and outbound calls
- Chat
- CFPB Consumer Response complaints
- Trustpilot and the BBB
- App Store and Play Store reviews
- What each interaction is scored on05
- Regulatory exposure — TILA, RESPA, TRID, UDAAP, FDCPA, TCPA, ECOA and Fair Housing
- Process adherence to your SOPs
- Disclosure and script compliance
- Customer experience and sentiment across the call
- Complaint identification, disposition and resolution
- What comes out04
- A scorecard per agent, with coaching recommendations
- The violating moment cited to its timestamp in the transcript
- Complaint categories and trends across channels
- Severity-scored findings pushed to your team
Triple Check
Three checks between the rulebook and the score
Three checks stand between a regulation and a decision. Each one can stop the work, and each one is logged. The Rulebook is yours, and every decision lands in The Evidence Trail, cited to its source. See how it works.
- Every interaction scored, not a 3% spot-check
- Your QA rubric encoded, including the edge cases that only live in reviewers’ heads
- The violating moment cited to its timestamp, so a reviewer goes straight to it
- Complaints tracked across the CFPB portal, Trustpilot, the BBB and the app stores
1,000+calls audited a month, none sampled
When it is unsure
A scored interaction that sits near a rubric boundary is flagged for a human reviewer rather than silently passed or failed.
Integrations
Scores the conversation wherever it happened
Calls from your telephony platform, email and chat from your support stack, and complaints from the public channels — pulled into one scored record per interaction.
FAQs
What does 100% coverage actually mean here?
Every call, email and chat, not a sample of them. Our customers have gone from auditing a few loan-officer calls a day to running every call through Sei — with no sampling and no manual review queue.
How is this different from the QA scoring in our contact-center platform?
The rubric is a mortgage rulebook — TILA, RESPA, TRID, UDAAP, FDCPA and your own SOPs, including the edge cases your reviewers carry in their heads — rather than a generic quality score. And every finding cites the moment in the conversation that produced it, so a reviewer verifies it real-time instead of relistening to the call.
What happens to a score that sits near a boundary?
It is flagged for a human reviewer rather than quietly passed or failed, with the moment cited so the reviewer can go straight to it.
Where do the complaints come from?
The CFPB Consumer Response portal, Trustpilot, the BBB and the app stores, alongside the complaints raised in your own calls, emails and chats. They are categorised and trended together, so a trending issue surfaces as a pattern.
The rules a conversation is scored against
UDAAP for AI Agents in Consumer Finance: What "Materially Interferes" Actually Looks Like in a Chat Transcript, and the Consumer-Experience Test the CFPB Applies
UDAAP is the rule every consumer-facing AI system in banking is ultimately measured against, and it is also the rule with the least specific text. The CFPB's Circular 2023-03 on chatbots, the 2022 exam manual update that was later rescinded, and the enforcement pattern under 12 USC 5531 and 5536 set the practical standard the agent has to clear. What we score against on every conversation, and why the consumer-experience test is the one that matters more than the internal QA test.
13 min readThe CFPB Consumer Response Portal With AI Complaint Handling: The 15-Day and 60-Day Response Windows, the Portal Tag Discipline, and the Public-Database Read the Bank Cannot Ignore
Every complaint routed through the CFPB Consumer Response portal is a supervised, time-boxed compliance event with a 15-day acknowledgment, a 60-day substantive response, a specific issue-and-sub-issue taxonomy that becomes the public database, and a consumer-dispute flag the Bureau tracks. The rule reads simple and the operations misfire often. Where the AI agent tightens the intake, the response drafting, and the root-cause loop, and the audit file the Bureau tests against in an examination.
13 min readAI Call Monitoring for Regulated Finance: A Practical Guide
How AI call monitoring moves beyond speech analytics to policy-aware, end-to-end monitoring with real-time nudges, complaint detection, and audit-ready evidence.
1 min read
You Ain't Seen Nothin' Yet
Book a Demo
Pack up some of your complex historical files — any loan type, any investor. We run them through intake, income and condition clearing, and in 30 minutes you see every condition we created and cleared efficiently for your own team, and why.
- Any loan type, any agency guideline or custom investor overlays.
- Every finding cited to the guideline or document it came from
Case study highlight
Loan review fell from 4 hours 50 minutes to 47 minutes per file, and 78% of items now clear at high confidence — so QC touches only the exceptions.
Pre-Close QC Agent · Better Mortgage— read the case study