Agentic AI for Customer Support

Cut ticket resolution time with AI that knows every ticket your team ever solved

Deploy AI support systems grounded in your historical tickets — automated first responses, RAG-powered resolution suggestions with root cause and diagnostic steps, and in-console AI chat for engineers. Built on proven deployments at Cleo for EDI support.

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Key Benefits

65% reduction in engineer research time per ticket

40% faster first response to customers

Consistent resolution quality across engineers and shifts

100% human-in-the-loop — AI drafts, engineers approve

Knowledge base that compounds with every resolved ticket

Core Technologies

AWS LambdaAWS Bedrock (Claude)SQSDynamoDBLangChainOpenAI EmbeddingsPineconeZendesk API & App Framework

Deep Dive: AI Support Automation

Your support organization's most valuable asset is buried in closed tickets: thousands of solved problems, tested resolutions, and institutional knowledge that new engineers take years to absorb. We build agentic AI systems that turn that history into a living knowledge layer — so every engineer responds like your most experienced one.

Our approach combines two proven patterns. First, an Intelligent First Response Agent: an event-driven pipeline that analyses each incoming ticket, enriches it with relevant handling notes and internal knowledge, applies multi-step AI reasoning, and posts a draft response for engineer review — cutting first-response drafting time dramatically while keeping a human in the loop on every reply.

Second, RAG-powered past-ticket intelligence embedded directly in your help desk (Zendesk, ServiceNow, Jira Service Management, Freshdesk). When an engineer opens a ticket, the system retrieves the most relevant past tickets, cites them as evidence, and synthesises a recommendation covering root cause, diagnostic steps, and resolution actions — plus an AI chat interface for follow-up questions grounded in your ticket history.

We deployed both systems in production for Cleo's EDI support organization: a serverless AWS pipeline (Lambda, SQS, Bedrock/Claude) for automated first responses, and a Zendesk app with a continuously-learning knowledge base built from historical tickets — reducing engineer research time by 65% and first response time by 40%.

Key Features & Capabilities

Everything included in our AI Support Automation service offering.

Automated First Response Drafting

Event-driven AI pipeline that analyses incoming tickets and posts well-structured draft responses as internal notes for engineer review — human-in-the-loop by design.

Past-Ticket RAG Intelligence

Semantic search over your entire ticket history with LLM synthesis: root cause analysis, diagnostic steps, and resolution actions, each citing the specific past tickets they came from.

Native Help Desk Apps

Widgets and apps inside Zendesk, ServiceNow, Jira SM, and Freshdesk — AI assistance where engineers already work, no tab-switching or copy-pasting.

AI Chat Grounded in Ticket History

Engineers ask follow-up questions in natural language and get answers backed by past resolutions — like Slack-messaging your most senior engineer, 24/7.

Continuous Learning Loop

Newly resolved tickets are automatically ingested into the knowledge base, so the system gets smarter with every ticket your team closes.

Serverless, Auto-Scaling Architecture

Event-driven pipelines on AWS (Lambda, SQS, DynamoDB, Bedrock) that scale with ticket volume and add zero server management overhead.

Real-World Applications

Use Cases

How organizations across industries are leveraging AI Support Automation.

Enterprise Software

EDI Support First Response

Cleo's EDI support team uses our event-driven AI pipeline to auto-draft first responses enriched with internal handling notes — every draft reviewed by an engineer before sending.

Customer Support

Zendesk Past-Ticket Intelligence

A Zendesk app that analyses open tickets against historical resolutions, presenting root cause, diagnostics, and resolution steps with cited evidence — cutting research time 65%.

IT Operations

L1 IT Ticket Auto-Resolution

RAG over runbooks and past incidents to auto-resolve common L1 issues (password resets, VPN, access requests) and hand off complex cases with full context.

Any Support Organization

New Engineer Onboarding

New support hires become productive in days instead of months — the AI surfaces institutional knowledge that previously lived only in senior engineers' memories.

What You Get

Deliverables & Outcomes

A complete engagement includes all of the following — no hidden extras, no scope surprises. Our ISO 9001:2015 certified process ensures every deliverable meets documented quality standards.

Historical ticket ingestion pipeline

Vector knowledge base with continuous learning loop

First-response agent (event-driven AWS pipeline)

Help desk app/widget (Zendesk, ServiceNow, etc.)

RAG synthesis engine with citation support

AI chat interface for engineers

Analytics dashboard (adoption, accuracy, time saved)

Runbooks, documentation, and team training

Technology Stack

Tools & Technologies

Best-in-class tools selected for your specific requirements — balancing performance, cost, and long-term maintainability.

AWS LambdaAWS Bedrock (Claude)SQSDynamoDBLangChainOpenAI EmbeddingsPineconeZendesk API & App FrameworkServiceNow APIFastAPIPostgreSQLPython

Frequently Asked Questions

Common questions we hear about ai support automation engagements.

How does AI ticket resolution work with our existing help desk?

We build native apps and widgets for Zendesk, ServiceNow, Jira Service Management, and Freshdesk. The AI appears directly inside the ticket view — when an engineer opens a ticket, it automatically shows relevant past tickets, a synthesised recommendation (root cause, diagnostic steps, resolution actions), and a chat interface. No workflow changes or tab-switching required.

Will the AI send responses to our customers automatically?

Only if you want it to. Our default deployment is human-in-the-loop: the AI posts draft responses as internal notes, and an engineer reviews, edits, and sends. Teams typically move to selective automation for well-understood ticket categories once they trust the system's accuracy.

How many historical tickets do we need for this to work well?

Meaningful results typically start around 5,000–10,000 resolved tickets with resolution notes. More history helps, but quality matters more than volume — tickets with clear resolution documentation produce the best knowledge base. We assess your data in the discovery phase and tell you honestly what accuracy to expect.

How long does a deployment take?

A production pilot — ticket ingestion, knowledge base, and help desk widget for one product line or team — typically takes 6–10 weeks. Full rollout with the automated first-response pipeline usually lands in the 3–4 month range depending on integrations.

Can this run on our own infrastructure for data privacy?

Yes. We deploy on your AWS/Azure account by default, and can run fully on-premise with self-hosted open-source LLMs if your tickets contain sensitive data that cannot leave your environment — the same pattern we use for government clients.

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Let's discuss your specific requirements and design a solution that delivers real business outcomes -- not just impressive demos.

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