FinHub Engage · AI
Manual QA reaches a fraction of your calls. AI transcription and analysis reaches all of them, surfacing the compliance breaches, mis-selling and coaching moments the sample was always going to miss.
Everything outside the sample is unreviewed by definition
The Challenge
Sampling Was Never a Quality Strategy, It Was a Capacity Limit
A QA team can listen to a small percentage of calls. Everything outside that sample is unreviewed, and the calls that go wrong are not conveniently inside it.
Compliance breaches on recovery calls surface when a customer complains or a regulator asks, which is the most expensive moment to find out.
Coaching built on a handful of sampled calls generalises. Agents get feedback that does not match the conversations they actually had.
Capabilities
What Full Coverage Actually Changes
Not a better sample, the removal of sampling as a constraint.
Compliance flags on every call
Recovery and collections calls carry regulatory exposure in the language used. Every call is checked for tone, threatening language and whether consent was properly taken, not the two in a hundred a reviewer had time for.
- Tone and threat-language detection across the full call book
- Consent capture verified on the call, not assumed
- Breaches surfaced within the review cycle rather than at audit
Mis-selling caught before it escalates
Mis-stated interest rates, invented features and price misquotes are usually discovered when a customer complains. Analysing the whole book finds the pattern while it is still a coaching problem rather than a remediation project.
- Price and feature misstatements detected in the conversation
- Recurring complaints grouped by branch, product and agent
- Long-pending cases surfaced from what customers actually said
Coaching tied to the exact moment
A monthly review built from a handful of sampled calls is a generalisation. Timestamped moments from real conversations give a team lead something specific to work with.
- Coaching anchored to the call and the timestamp
- Agent and team trends measured on complete data
- QA capacity redirected from sampling to acting on findings
How it runs
From Recording to Something You Can Act On
A serverless pipeline that deploys into a cloud account you control, so recordings do not leave your environment.
Ingest recordings
Call recordings land in object storage from your existing telephony. No change to how your contact centre records.
Architecture
Serverless on AWS
The same architecture running at NBFC scale today. No servers to manage, and recordings stay inside a cloud account you control.
Call recordings land in Amazon S3 from your existing telephony. A Lambda trigger starts the pipeline: Amazon Transcribe handles Hindi, Hinglish and English speech-to-text with speaker separation, Amazon Comprehend extracts sentiment and key phrases, and Amazon Bedrock generates the LLM-driven insights and summaries.
Results land in DynamoDB for fast lookups and Athena for SQL over the full transcript corpus, surfaced in QuickSight dashboards. The moment a compliance flag or priority signal surfaces, Amazon SNS pushes it to Slack, email, SMS or your ticketing system.
Connects to the systems you already run
- Telephony
- Cloud PBX, IVR and contact-centre platforms
- CRM
- Outbound, inbound and recovery CRMs, via API or webhook
- Storage
- Amazon S3 or SFTP for batch import
- BI
- QuickSight, Athena, or export to your own warehouse
- Alerts
- Slack, email or SMS
Security & compliance
Where the Certifications Stand
Stated as it is, including the three still in progress, a security review will ask, and the honest answer is the useful one.
| Certification | Status |
|---|---|
| PCI Compliant | Live |
| HIPAA-eligible | Live |
| AWS SDP | Live |
| ISO 27001 | In progress |
| SOC 2 | In progress |
| DPDP-ready | In progress |
FAQ
Post-Call Analytics, Common Questions
Coverage, languages, deployment and what the compliance flags actually catch.
Post-call analytics transcribes and analyses recorded customer calls after they end, attaching structured signals, sentiment, compliance flags, outcome reasons and summaries, to each one. It differs from manual QA in coverage: a QA team can review a small sample of calls, while an automated pipeline processes every call in the book.
Explore More
The Rest of FinHub Engage
Run It Against Your Own Recordings
A shadow pilot on your calls, with your QA team comparing the AI's flags against their own sample.