73%
Containment Rate
44s
Avg Handle Time Reduction
2x
Spanish Language Capacity
99.9%
Uptime SLA Maintained
A top US Bank runs millions of customer calls annually. English and Spanish. High expectations. Regulatory scrutiny at every layer. Their contact center was not keeping pace. Vivify 247 rebuilt it from the ground up on AWS, with AI doing the heavy lifting and their team doing what humans do best.
The Situation
Millions of inbound calls. An IVR that frustrated callers into hanging up. A bilingual customer base that was being underserved on every Spanish call. Average handle times that were eating into agent capacity. And a compliance team that needed every interaction logged, auditable, and immutable.
The bank knew voice AI was the answer. What they needed was someone who could architect it without creating a security liability, without violating their data governance posture, and without a long runway to prove it worked.
That is where Vivify 247 came in.
The Architecture
We built this system entirely on AWS infrastructure with Salesforce Financial Services Cloud as the CRM backbone. Every component earns its place. The architecture is designed so that the bank owns every layer and no data ever touches a third-party inference endpoint without tokenization upstream.
The nucleus is AWS Bedrock running Meta Llama 3.3, fine-tuned on the bank’s own vocabulary, product catalog, and bilingual customer interaction history. The model does not guess. It has been trained on the exact language this bank’s customers use, the exact compliance constraints the contact center operates under, and the exact escalation signals that matter.
Every signal routes here.
Every output fires from here.
PII never enters the model.
No raw PII transmitted
Every call logged · PrivateLink isolated
Interactive architecture diagram. Follow the animated signal through the system.
How the Signal Flows
A caller dials in. Amazon Connect receives the call and routes the real-time audio stream to Amazon Transcribe. Transcribe converts speech to text in under 300 milliseconds. The text hits AWS Bedrock. Bedrock runs intent classification, sentiment scoring, and response generation simultaneously. ElevenLabs synthesizes the response in the caller's preferred language using a custom voice model trained specifically for this bank.
If the model detects distress, an escalation trigger fires. The call routes to the right human agent with a pre-loaded context card pulled from Salesforce FSC. The agent walks in briefed, not blind.
The Secure Data Perimeter sits completely outside the AI inference path. Consumer PII, including Social Security numbers, dates of birth, and account credentials, lives in an isolated vault managed by AWS Macie, Amazon S3 Object Lock, and customer-managed KMS keys. The AI never touches raw PII. It receives tokens. Every token exchange is logged by CloudTrail and retained per the bank's regulatory schedule.
The Results
Within 90 days of go-live, the numbers were unambiguous.
The Full Technology Stack
| Layer | Technology | Role |
|---|---|---|
| AI Nucleus | AWS Bedrock + Meta Llama 3.3 (fine-tuned) | Core inference engine. Intent, sentiment, response generation |
| Telephony | Amazon Connect | Contact routing, real-time streaming, call management |
| Speech-to-Text | Amazon Transcribe | Real-time bilingual transcription with custom vocabulary |
| Text-to-Speech | ElevenLabs API | Custom voice synthesis, English and Spanish models |
| NLU | Amazon Comprehend | Intent classification, sentiment scoring, escalation detection |
| Orchestration | AWS Lambda | Serverless routing between every system layer |
| CRM | Salesforce Financial Services Cloud | Client records, case auto-creation, advisor context |
| Data Security | AWS Macie + S3 Object Lock + KMS CMK | Secure Data Perimeter, tokenization, WORM retention |
| Network | AWS PrivateLink + VPC | Zero egress. All traffic stays inside the bank's private cloud |
| Compliance | AWS CloudTrail + CloudWatch | Immutable audit logs, real-time ops dashboard |
What This Means for Regulated Enterprises
Most voice AI deployments in financial services fail at one of three points. The model hallucinates. The data perimeter is porous. The compliance team cannot audit the inference path. This system was designed to solve all three problems before deployment, not after.
Bedrock Guardrails enforce the model's behavior at the compliance layer. PrivateLink ensures no inference traffic ever leaves the private network. CloudTrail captures every token exchange between the AI and the Secure Data Perimeter and retains those logs on S3 Object Lock, which is tamper-proof by design.
The result is a system that a CTO can put in front of a compliance officer, a regulator, or an audit committee and walk through with confidence. Every decision point is traceable. Every data access is logged. The model is constrained. The vault is isolated.