Customer Science

Product · Knowledge improvement

Knowledge Quest

The intelligence layer between your interactions and your knowledge base. Knowledge Quest reads every call, chat, email and ticket, finds the gaps costing you the most, and closes them with evidence rather than guesswork.

Book a demoVisit knowledgequest.ai ↗
Knowledge Quest's assistant, Kai, answering a question about billing problems with source-backed evidence.
Kai, the Knowledge Quest copilot, answering with the evidence behind it.

The problem

Every service team has two systems that do not talk to each other

On one side is the interaction platform: the contact centre or the service desk, where every call, chat and ticket lands. On the other is the knowledge base the team is supposed to answer from.

Nobody can see the gap between them. You do not know which articles help, you discover problems after they have become complaints, and emerging issues hide in the data for weeks before anyone spots them. Dashboards show you what happened. They cannot tell you why.

Knowledge Quest sits in that gap. It reads the interactions, measures them against the knowledge, and tells you exactly which gaps are costing you the most and what to write to close them.

It is also the capability AI now depends on. CRM and omnichannel platforms have had most of the attention and investment for years. Knowledge is being recognised as the next key platform, because AI needs a source of information to answer from. It does not need a single source, and it can use material you already have, such as training documents and web pages, which makes the quality of that material matter more than ever.

Who it is for

Four kinds of team, one closed loop

BPOs

For operators running contact centres at scale for many clients, from three programs to three hundred, with a recovered-cost view per client.

For BPOs ↗

MSPs

For managed service desks on fixed-fee contracts, where every avoidable ticket erodes margin and every deflected one protects it.

For MSPs ↗

How it works

One closed loop, always on

It is not a chatbot. Deterministic scoring and calibrated probability models do the ranking, with language models used only where they earn their place, so every result can be explained.

1. Capture

Every interaction, transcript and ticket flows in from the platforms you already run. No re-platforming, no agent behaviour change.

2. Analyse

It extracts intent, classifies topics and measures sentiment the same way on every channel.

3. Discover

Emerging topics and outliers surface while you can still do something about them, rather than in next month’s report.

4. Prioritise

Gaps are ranked by the cost of leaving them open, so the team works on what matters first.

5. Resolve

Kai, the copilot, drafts knowledge articles grounded in customer interactions. For existing articles, it shows the original alongside suggested changes, so your team can review what needs updating and the evidence behind it.

6. Measure and learn

Actual impact is tracked and the model recalibrates per knowledge area every week. Sharper every cycle.

Why it matters

The cost of the gap, in other people's numbers

of contact centres reach world-class first contact resolution of 80 per cent or more (COPC)
5%
support-cost reduction at advanced knowledge-centred service maturity (Consortium for Service Innovation)
25–50%
the cost of a level-three ticket against level one, $104 versus $22; every gap-driven escalation pays it (MetricNet, 2024)
5×
the cost of an assisted contact against self-service, $13.50 versus $1.84 (Gartner, 2024)
7×

What you get

Three wins, every week

A ranked list of gaps

The knowledge gaps that cost you the most this week, with the interactions that prove it and the article that would close each one.

Answers with provenance

Team leads and agents ask Kai and get an answer with its sources, so trust is earned on every reply rather than assumed.

A brief for every level

Real-time for team leads, weekly for managers, monthly for the executive who has to sign off on the knowledge investment.

Discover

Contact volume by topic and hour, before anyone has filed a report

The heat map is one of the views Kai draws on. Every cell is real interactions, and the hot ones are where the knowledge gaps are costing you today.

A Knowledge Quest heat map of contact volume by topic and hour of day, with a spike highlighted.
Kai explaining a contact volume heat map, with the spike it found and the article that would flatten it.

Built by practitioners

Built by a consultancy that did this job by hand for decades, and got tired of it

Knowledge Quest was not built by engineers with a deck. It was built by Customer Science, a CX and service management practice that spent years finding knowledge gaps manually for contact centres and service desks across Australia. The product is that method, run continuously.

It connects to Genesys, Amazon Connect, NICE CXone, Talkdesk, Twilio, Vonage, ServiceNow, Jira Service Management, Freshservice, Zendesk, Salesforce, Zoho Desk and LivePro, and the same practitioners who built it are available to run the first cycles with your team.

Ways to start

Start with a sample, or go straight to a pilot

Gap-to-Draft

A dedicated Knowledge Quest subscription, started with a hands-on workshop.

Assisted Migration Accelerator

A pilot implementation of Knowledge Quest in your own environment.

Insights

Related insights

All Knowledge Quest insights ↗

Your team

Who you would be working with

Meet the whole team ↗

See your own gaps. A demo runs on a sample of your interactions, not ours.

Or start with a pilot on one service desk, or ask for a sample weekly brief.

Book a demo at knowledgequest.ai