AI-assisted Agent Workflows

Overview

What we deliver

We embed AI assistants into agent desktops to surface answers, draft notes, and guide conversations in real time across voice and digital channels.

We design and deploy AI-assisted workflows that help contact center agents resolve queries faster and with greater consistency. Our team integrates large language models, knowledge bases, and CRM data into the agent desktop, so the right answer, script, or next-best action appears at the moment of need. We build retrieval pipelines, prompt libraries, and guardrails that fit your products, policies, and compliance posture. We also automate after-call work such as call summaries, disposition coding, and CRM updates, freeing agents to focus on the customer. Our delivery approach pairs solution architects with operations leads, so the AI layer ships alongside trained agents, updated SOPs, and measurement dashboards. We tune models against your transcripts, monitor drift, and iterate weekly so quality, handle time, and CSAT keep improving across voice, chat, and email queues.

Fit Check

Built for teams like yours

Who it's for

BPO operators scaling AI pilots
In-house contact centers
SaaS support teams
Retail and e-commerce service desks
Telecom and utility care teams

Pain points we solve

Long handle times and repeat contacts
Inconsistent answers across agents
Heavy after-call work and wrap time
Slow onboarding for new hires
Limited visibility into agent assist value

What's included

Capabilities

Everything we cover in this engagement.

Agent copilot deployment
Real-time next-best-action prompts
Knowledge base retrieval and grounding
Automated call summaries and notes
CRM and ticketing integration
Prompt and guardrail engineering
Sentiment and intent detection
Adoption coaching for agents and supervisors

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discovery

We audit current workflows, tooling, and transcripts to find AI value pockets.

02

Design

We map use cases, data sources, and guardrails into a target agent experience.

03

Build

We integrate copilots with CRM, knowledge, and telephony in a sandbox.

04

Pilot

We run a controlled pilot, measure impact, and refine prompts and content.

05

Scale

We roll out to all queues with training, monitoring, and weekly tuning.

What you get

Deliverables & outcomes

What you get

Agent copilot integrated with desktop
Curated knowledge and prompt library
Auto-summary and disposition module
Adoption playbook and training kit
AI performance dashboard
Weekly tuning and release notes

Outcomes you can expect

Lower average handle time
Higher first contact resolution
Reduced after-call work
Faster new-hire ramp
More consistent quality across agents

Timeline

8 to 14 weeks to first scaled rollout

Engagement

Monthly retainer, Project, Sprint

Tools we use

OpenAI, Azure OpenAI, Salesforce, Zendesk, Genesys, Five9

KPIs we track

AHT, FCR, ACW time, CSAT, copilot adoption rate

Client stories

What clients say

"

We had been prototyping an AI quoting agent for nine months and could not get it past demo quality. They came in, scoped a real eval set, swapped our retrieval layer, and added guardrails for the edge cases that kept burning us. Went live in seven weeks. It now handles 41 percent of inbound quote requests without a human touching them.

Kyle A.

"

Holiday season was about to break us. We needed 22 agents in six weeks and our internal hiring pipeline could not move that fast. They staffed it, trained on our tone guide, and ran nesting alongside our senior reps. CSAT actually went up by three points during peak. First Q4 in four years my support lead took her vacation.

Tom H.

Proof