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AI and Automation

AI Knowledge Base for Support Teams

An AI-powered knowledge base that helps support agents find accurate answers in seconds.

Overview

What we deliver

We build AI knowledge bases that index your support content and surface trusted answers for agents and customers in real time.

We build AI knowledge bases that turn scattered support documentation, ticket history, and product manuals into a single source of truth your agents and customers can query in natural language. Our team ingests your existing content, structures it for retrieval, and connects it to a chat interface or your help desk so answers appear inline. We handle embeddings, vector storage, retrieval logic, and citation rendering so every response links back to the source. We also set up feedback loops, content gap reports, and freshness checks so the knowledge base stays accurate as your product changes. The result is faster ticket resolution, fewer escalations, and consistent answers across every channel your support team works in.

Fit Check

Built for teams like yours

Who it's for

  • SaaS support teams
  • E-commerce help desks
  • Fintech customer service
  • B2B technical support
  • Subscription product companies

Pain points we solve

  • Slow first-response times
  • Inconsistent answers across agents
  • Outdated knowledge articles
  • High onboarding time for new agents
  • Repetitive tier-one tickets
What's included

Capabilities

Everything we cover in this engagement.

  • Content ingestion and chunking
  • Vector index setup
  • Retrieval-augmented generation
  • Citation and source linking
  • Help desk integration
  • Feedback capture
  • Content gap reporting
  • Freshness and quality monitoring
How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discovery

We audit your support content, tools, and ticket categories.

02

Ingestion

We collect, clean, and structure documentation for retrieval.

03

Build

We configure the retrieval stack and agent interface.

04

Pilot

We run a controlled rollout with a subset of agents.

05

Launch and tune

We expand access and improve based on real usage.

What you get

Deliverables & outcomes

What you get

  • Indexed knowledge base
  • Agent-facing chat interface
  • Customer-facing widget option
  • Citation system
  • Admin dashboard
  • Documentation and training

Outcomes you can expect

  • Faster ticket resolution
  • Higher first-contact resolution
  • Consistent answer quality
  • Lower agent ramp time
  • Reduced ticket volume
Timeline

6 to 10 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

OpenAI, Pinecone, LangChain, Zendesk, Intercom

KPIs we track

Average handle time, First-contact resolution, Deflection rate, Agent satisfaction, Answer accuracy

Client stories

What clients say

"

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.
"

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.
FAQ

Frequently asked questions

Quick answers to the questions we hear most.

Can the knowledge base pull from multiple sources?
Yes, we connect to help center articles, internal wikis, PDFs, and ticket history in one index.
How do you keep answers accurate?
Every response cites its source, and we run scheduled checks for stale or conflicting content.
Will it work with our existing help desk?
We integrate with Zendesk, Intercom, Freshdesk, and most major platforms through their APIs.
Can customers use it directly?
Yes, we can deploy an agent-only version, a customer-facing widget, or both.
What happens when the AI does not know an answer?
It routes the question to a human agent and flags the gap for content authors to address.

Ready to give your support team an AI knowledge base?

We will scope your content, design the retrieval stack, and ship a working pilot.