Agent Optimizer

Stop configuring AI agents by instinct. Start configuring them by evidence.

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The real cost of AI agents that don’t work

Your business is investing heavily in AI customer service. The financial case is compelling: lower cost per interaction, faster resolution, 24/7 availability.

But here’s what’s actually happening on the ground.

Agent language is unknown

The way in which your Agents talk has a big influence on their effectiveness.

Resolution rates, satisfaction scores, and other key metrics are driven by how the agent talks and there is aften no visibility or prediction on what they will sat

Which slows down AI Agent rollout

When trust is low, the rate of rollout and the financial upside it brings is stalled.

The organisation wants to push on with the adoption of AI Agents but the uncertainty and risk inhibits it

The fix isn’t slower rollout

It’s deterministic visibility into agent language, so confidence and speed stop being a trade-off

AI agent optimization

Agent Optimizer is not a chatbot builder. It’s a dedicated system for measuring, optimizing and continuously improving AI agents using what your customers’ own language tells you actually works.

The method is:

Deterministic

Auditable
Repeatable

It doesn’t rely on AI black boxes. It relies on your own data, the years of customer interactions already sitting in your systems. That data contains the signal; we make it visible, turn it into evidence and configure your agents from that evidence.

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What does this look like in practice?

Here’s a real conversation between a customer and an AI agent, with what our analysis reveals and what you can fix.

Optimization insights

1. Missing personalization. Bot never used customer name. In successful interactions, agents do.

2. Language mismatch. Customer said “primary,” bot said “default.” Mirror language to prevent confusion.

3. Inappropriate escalation. Bot pushed payment when customer just changed card. Hard-code this scenario out.

4. Help desk dumping. FAQ redirect caused departure. Need smarter escalation logic

Original conversation

Bot: Hello, how can I help you today?

Customer: I need to check my primary payment method.

Bot: Let me pull up your default payment method...

Bot: I've updated that for you now.

Customer: Thank you.

Bot: I notice you missed a payment. Would you like to update your default card now?

Customer: My card is fine. I just changed it. I don't need help.

Bot: Check our FAQ on payment troubleshooting.

[Customer leaves conversation]

How Agent Optimizer works: three phases

We deploy the right capability at the right moment in your optimization cycle, enabling you to measure, fix and sustain improvement in your AI agents.

1. Surface the signals

Before we can fix anything, we need to see what’s actually happening in your conversations.

Agent Optimizer gathers your historical customer data and runs deterministic analytics that compare what succeeds against what fails, revealing exactly where your agent breaks down.

Success vs. failure

We compare conversations that resolve against ones that don’t, at the level of tone, vocabulary, topic handling and timing.

Signal, not speculation

Deterministic analysis identifies what’s actually driving frustration and confusion. Not a guess at what might be.

Every data source

NPS surveys, CSAT responses, chat transcripts, call recordings, reviews, social mentions. If your customers talked, we analyze it.

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Configure your agents

Once our deterministic engine has identified what drives success, the question becomes: how do we build that into your agent?

Agent Optimizer translates those signals into precise configuration instructions deployed across four mechanisms: RAG rules, prompt engineering, hard-coded behaviors and escalation logic.

Data-led configuration

Every change flows from what your customers’ language tells us works, not generic best practice.

Brand specific

Configured to your customers, your tone, your product. Generic prompts produce generic results.

Non disruptive

Works with any chatbot platform. No rip-and-replace, no platform migration.

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Measure and iterate

The same deterministic analytics that identified the initial problems continue monitoring agent performance post deployment.

We track escalation rates, satisfaction signals, confusion patterns and language drift. Every month or quarter, we repeat the analysis and compound your gains.

Continuous improvement

Not a one-time project. Recurring optimization that compounds month over month.

Measurable outcomes

Track deflection, satisfaction and retention impact. Every iteration proves value.

Low risk

Changes are incremental, grounded in evidence and reversible if needed.

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Why Relative Insight?

Most AI vendors configure behavior from the outside in: brand guidelines, generic frameworks, assumptions about good CX. It produces average results.

Agent Optimizer configures from the inside out: using what your customers’ language tells us actually drives satisfaction. Customer language is specific to your sector, brand and customer base. Evidence-based configuration produces yours. Watch our Agent Optimizer webinar to see it in action.