Customer Experience AI

Better CX AI depends on getting retrieval right.

A relevant answer isn't enough. It has to be right for this customer, in this situation, at this moment.

For teams building or running AI-powered customer experience.

  • Correct for this customer
  • Applicable in this situation
  • Responsive in the moment
Why retrieval matters

When retrieval fails, the customer feels it.

  • Stale knowledge still answers. Old policy versions are rarely retired, so the new version and the old one compete for the same question.

  • The wrong kind of search. A product catalog pushed through a system built for policy documents loses exact attributes such as SKU, size, and availability.

  • Silence feels broken. On a voice call, a slow retrieval step is a dead line, not a typing indicator.

Business impact

Retrieval sits upstream of every CX metric. When it fails, work flows back to customers, service teams, and engineering: containment can fall, escalations and repeat contacts can rise, handle time can stretch, and someone has to diagnose and repair the answer by hand.

Even a frontier AI model cannot compensate for missing documents, outdated policies, or information that was never eligible for retrieval.

Four CX workloads. Four retrieval problems.

Agent Assist

Find relevant knowledge during a live interaction, using conversation context, current procedures, and source authority. Agents quietly work around weak results, which can hide retrieval problems.

Customer-Facing AI

Retrieve policies and information that actually apply to the customer. Handle versions, permissions, eligibility, and escalation correctly. With no human to catch a mistake, retrieval errors reach the customer.

Voice AI

Select the right evidence quickly enough for a natural conversation, balancing retrieval quality and end-to-end latency.

Product & Technical Support

Combine semantic understanding with exact attributes, identifiers, compatibility rules, and availability.

A shared retrieval foundation can support all four—but relevance must be engineered for each workload, and on multi-client platforms, for each client's knowledge base.

CX retrieval in practice

Use case

AI Concierge Agents for Vacation Rentals

How customer-facing agents need applicable context, policy authority, and escalation controls—not merely relevant answers.
Explore use case →
Use case

Enterprise RAG for Workplace Knowledge

A production pattern for permissioned, versioned, and changing knowledge spread across documents, tickets, messages, and systems.
Explore use case →
CX retrieval evaluation

Know why your CX AI succeeds—or fails.

When a customer gets the wrong answer, can your team tell which layer failed: the knowledge source, indexing, retrieval, ranking, or generation? Tie retrieval tests to the outcomes you already track, so improvements show up in system quality and in customer experience.

Before and after launch

Pre-launch checks build confidence, but content and customers keep changing. Keep measuring retrieval quality once the system is live.

Per workload and per client

Use realistic scenarios for each workload and, on multi-client platforms, each knowledge base, instead of one global score.

Which layer failed

Trace each wrong answer to its source, indexing, retrieval, ranking, or generation, and turn it into a prioritized, fixable finding.
Explore Retrieval Evaluation & Operations
How we help

From diagnosis to production improvement.

We work with your existing stack and find where the constraint actually sits before recommending architectural change. That keeps engineering effort focused and avoids unnecessary replatforming.

Evaluate & Diagnose

A structured first step: test representative customer interactions, starting with your top contact drivers, against your retrieval stack and return a baseline with prioritized, fixable findings.
Start with a Search Stack Audit →

Design & Build

Engineer retrieval for new CX AI capabilities, with success criteria defined from the beginning.

Optimize & Scale

Improve relevance, freshness, latency, and serving efficiency as workloads evolve.
Further reading

See how retrieval requirements change when AI moves from agent assist to customer-facing chat and voice—where applicability, authority, and latency become part of answer quality.

Read “The Unexpected Ingredient in Every Good CX Tool: Information Retrieval”
Start here

Bring specialist retrieval engineering to your CX product.

Whether you're building a new AI capability or improving an existing platform, we can help establish what good retrieval looks like—and engineer the system to meet that standard. A Search Stack Audit is the fastest first step.

Start with a Search Stack Audit