SiriusOne:

AI-Powered Customer Support Automation

Multi-channel AI system combining voice AI, text automation, and a real-time agent copilot — reducing handling time by 52% and scaling support capacity without scaling headcount.

Client & Project Overview

A regional financial services company was processing over 14,000 customer support interactions per month across voice and text channels. Response times were averaging 8–12 minutes per call. First-contact resolution sat at 61%. Agent turnover was high — onboarding new support staff took 6–8 weeks before they reached acceptable performance levels.

The client needed a way to handle growing support volume, reduce average handling time, and improve accuracy — without proportionally increasing their support team.

SiriusOne designed and deployed a multi-channel AI agent system combining voice AI, text automation, and a real-time agent copilot, grounded on approved knowledge-base content and support documentation — reducing average handling time by 52%, increasing first-contact resolution to 89%, and enabling the existing team to process 3.4x more interactions per agent per day.

Business Problem

The traditional support model was showing structural limits. Every spike in customer volume required hiring, training, and ramping new agents — a process that took months and introduced quality variance.

Agents spent 40–60% of each call searching internal knowledge bases, switching between systems, and manually logging interaction data. Experienced agents were bottlenecked answering the same high-frequency questions. Complex calls were escalated unnecessarily because junior agents lacked confidence and context.

The client faced three compounding problems: volume they couldn't absorb, quality they couldn't maintain, and costs they couldn't justify at the growth rate they were targeting.

Automation was the obvious answer. But previous chatbot implementations had failed — rule-based systems couldn't handle natural language variation, and customers were escalating to human agents anyway, making the automation layer pure overhead.

The client needed a production-ready AI workflow with reliable handoff, approved knowledge grounding, and operational safeguards — not another demo or proof of concept.

SiriusOne helped us cut average handling time in half and give every agent an AI copilot grounded on approved support content — while supporting our compliance, PII, and audit requirements.

Head of Customer Operations

Financial Services Client

Tech Stack

Cloud: AWS · S3 · Lambda · API Gateway · CloudWatch · IAM · KMS · CloudTrail

AI/ML: OpenAI GPT-4o · realtime voice models · speech-to-text · RAG-based knowledge retrieval · intent classification · entity extraction · evaluation pipeline

Voice: Twilio Voice · Media Streams/WebSockets · WebRTC · real-time transcription and handoff pipeline

Backend: Python · FastAPI · PostgreSQL · Redis

Retrieval & Knowledge: embeddings · vector search · knowledge-base connectors · document indexing

Integration: REST APIs · CRM webhooks · support-ticketing/knowledge-base connectors

Security & Compliance: encryption in transit and at rest · PII masking/redaction · RBAC · audit logging · retention policies

Deployment: Docker · GitHub Actions · container-based CI/CD · monitoring and alerting

#ArtificialIntelligence

#MachineLearning

#AIchatbot

#VoiceAI

#CustomerSupport

Project Timeline

A phased delivery model allowed the client to go live with voice AI in week 8 while text and copilot layers were completed in parallel — minimising time to value without compromising quality.

Duration

16 weeks

Effort

~680 hours

Discovery & Business Analysis

2 weeks

Support workflow mapping, call recording analysis, knowledge base audit, integration requirements, compliance review

Data & Infrastructure Preparation

2 weeks

Data pipeline setup, CRM integration, knowledge base structuring, voice infrastructure provisioning

AI Model Development & Training

4 weeks

LLM fine-tuning on client knowledge base, voice model configuration, intent classification, entity extraction

System Integration

4 weeks

Voice AI deployment, text agent integration, copilot UI development, CRM connectors, real-time transcription pipeline

Testing & Optimization

3 weeks

Accuracy validation, load testing, edge case handling, compliance review, agent UAT

Deployment & Training

1 week

Production launch, agent onboarding, documentation, monitoring setup, operational handover

Team involved

AI/ML Engineers

Fine-tuned LLMs, built intent classification models, developed real-time transcription and voice response pipeline.

Backend Engineers

Built API layer, CRM integrations, data pipelines, and knowledge base connectors.

Cloud Architect

Designed scalable AWS infrastructure, managed deployment pipeline and observability stack.

UX Designer

Designed agent copilot interface — built for speed, clarity, and zero training curve.

Project Manager

Coordinated phased delivery, client UAT, compliance sign-off, and production rollout.

Compliance Specialist

Ensured PII handling, call recording policy, and data retention alignment throughout delivery.

Solution Overview

SiriusOne designed and deployed a three-layer AI system — voice agent, text agent, and human agent copilot — operating as a unified customer support platform. Each layer handles a distinct interaction type while sharing the same knowledge base, context engine, and CRM integration.

Key features of the solution include:

Voice AI Agent

An AI voice agent handles approved high-frequency request types within defined confidence thresholds — including general service inquiries, branch information, and FAQ resolution. The agent uses real-time speech recognition, intent detection, and natural language generation to conduct full conversations without human involvement. Calls outside confidence thresholds are transferred to human agents with full context preserved.

AI Text Agent & Chatbot

A text-based AI chatbot handles web, mobile, and messaging channel interactions. Powered by a RAG-grounded assistant using approved knowledge-base content, product documentation, and historical support patterns — the agent resolves 78% of text interactions without human escalation.

Real-Time Agent Copilot

For interactions requiring a human agent, an AI copilot operates alongside — listening in real time, surfacing relevant knowledge-base articles, suggesting response options, and automatically logging interaction data to the CRM. Agents stop switching between systems. They focus on the customer. The copilot handles the rest.

Seamless Escalation & Handoff

When the voice or text agent reaches its confidence boundary, the interaction transfers to a human agent with full conversation history, detected intent, and recommended next steps — delivered to the copilot interface before the agent says their first word.

Enhanced Security & Compliance

All voice interactions are processed with PII masking in the transcription pipeline. Call recordings follow the client's data retention policy. Audit logs capture key AI actions, handoffs, and agent decisions for compliance review. Encryption in transit and at rest across the support workflow.

Results

52% Reduction in Average Handling Time

Voice AI and the agent copilot eliminated the search-and-switch behaviour that consumed nearly half of every interaction. Agents resolve faster because the right information arrives before they need to ask for it.

Enhanced First-Contact Resolution

First-contact resolution improved from 61% to 89% — driven by the AI text agent handling routine queries completely, and the copilot giving human agents immediate access to accurate answers on complex calls.

3.4x Agent Throughput

The same team now processes 3.4 times more interactions per agent per day. Volume spikes are absorbed by the AI layer — not by emergency hiring.

Improved Accuracy & Compliance

Automated CRM logging removed manual data entry errors. PII masking, audit logging, and retention controls were configured around the client's financial-services compliance requirements from day one.

Similar

implemented cases:

View all cases

Similar

implemented cases:

AI-Powered Customer Support Automation

SiriusOne designed and deployed a multi-channel AI agent system combining voice AI, text automation, and a real-time agent copilot — reducing average handling time by 52%, increasing first-contact resolution to 89%, and enabling the existing team to process 3.4x more interactions per agent per day.

Tech Stack: Cloud: AWS, AI/ML: GPT-4o · RAG · Voice AI · intent classification, Voice: Twilio · WebRTC, Backend: Python · FastAPI · PostgreSQL · Redis, Security: PII masking · RBAC · audit logging