Agentic Customer Intelligence

From Data to Decisions -Volto Agents in Your Pipeline

Volto is a new data layer between raw customer data and operative decision systems. Specialized multi-agent systems from research, through qualitative data analysis, to resonance simulation - individually integrable or as a full stack.

The Platform

What Volto is not

No synthetic data tool, or LLM-generated personas
No classical survey tool
No static BI dashboard

What Volto is

Primary Research at Scale	Panel-based micro-surveys with n=500-4,000, speech-based
Qualitative at Scale	Open answers, thousands of cross-connections, no click data
Real-Time CI Layer	No one-off report - continuous updates
API-first	Segment data flows directly into CRM, marketing automation, internal agents
Multi-Agent internally	Volto itself uses proprietary multi-agent systems for segmentation & simulation

Three core capabilities

Innovative Primary Research

Speech-based micro-surveys capture multidimensional customer data. Not what customers click - what they think, feel, plan.

Dynamic Segmentation

AI-based clustering models weighted customer segments based on qualitative criteria and behavioural-psychological profiles. Segments update automatically when attitudes shift.

Integrated Customer Intelligence

Volto is the new data layer for interpreting, predicting and simulating consumer behaviour. Usable as a platform or fully integrable into existing operative systems.

The Agent Pipeline

From Research to Optimization — One Connected Pipeline.

A multi-agent system with swarm intelligence — an adaptive system that fits your workflow. Each module is usable individually or as part of the full pipeline. No more silos, no more weeks of waiting. From market research through to campaign optimization in one connected flow.

Briefing & Mapping

Initial Research & Intent Mapping

Segments from CRM tags are backward-looking — VOLTO recognises patterns that remain invisible manually.

CRM Tag AnalysisLookalike PatternsKeyword ClusterIntent MapCross Connections

Segmentation & Targeting

Look-a-like Modelling & Segment Detail

Trigger + Lookalike combined: Who is currently in a purchase moment AND has similar profiles to existing high-value customers?

Focus SegmentsSegment DetailsTrigger CorrelationResonance Score

Campaign Design

Hypotheses & Messaging per Segment

Instead of one message for all: VOLTO generates a differentiated hypothesis per segment.

Hypothesis per ClusterMessaging MatrixHeadline VariantsFrame Options

Hypothesis Pre-Testing

VOLTO Simulation Engine

Scenario simulation before budget deployment. 18 hypotheses reduced to Top-5 for real budget.

Resonance PredictionScenario SimulationTop-5 Selection-70% Test Budget

The Agent Pipeline

How the agents interact

Market / Panel

Research Agent

Authentic signals, NLP analysis

Audience Agent

Living target group profiles

Resonance Agent

Resonance scores, framing

Scenario Agent

Simulations, forecasts

Activation Agent

CRM / MA / CDP / Agents

Feedback Loop

Campaign KPIs back to Resonance

Business Cases

Where Agents Create Value

Volto Agents plug into existing workflows across industries and deliver measurable outcomes. Here are the seven core integration patterns.

3.1

Marketing Automation

Resonance-driven Targeting

B2C: Energy, Telco, Financial Services, FMCG

The system learns from clicks - not from attitudes. Why customers respond (or not) remains invisible. Messages are rarely truly relevant.

Standard Flow (without Volto)

1 - Planning & Briefing

Marketing team defines campaign goal. Copywriter creates message based on experience. Target group defined via demographic CRM filters.

2 - CRM Segmentation

Static lists created (e.g. "Customers without interaction >90 days"). No attitude data - only historical behavioural data.

3 - MA Tool Setup

Campaign set up in Braze / Klaviyo / HubSpot. Uniform message or maximum 2-3 A/B variants. Time or event-based triggers.

4 - Send & Measure

KPIs: Open Rate, CTR, Conversion Rate - all retrospective. Optimisation: manual A/B test, next campaign runs with slightly adjusted message.

5 - Reporting

"Variant B has 12% higher CTR" - but no explanation why. Next campaign starts again with assumptions.

Data limitations in standard flow

Source	Data Type	Limitation
CRM	Demographic, Transactional	Only history, no attitudes
Web Analytics	Clickstream, Sessions	Superficial, no "why"
Email Tool	Opens, Clicks, Bounces	Retrospective, no context
Ad Platform	Impressions, CTR, ROAS	Algorithm black box
Surveys (rare)	NPS, CSAT	Too rare, too small, too slow

Standard Flow (without Volto) → With Volto

Standard Flow (without Volto)

Campaign Brief

CRM Segmentation (demographic)

Message (assumption)

MA Tool Setup

Send

KPI Measurement

Next Campaign (assumptions)

With Volto

Campaign Brief

[Research Agent]Research Agent

What concerns the target group? Which topics resonate?

[Audience Agent]Audience Agent

Qualitative segments instead of demographic lists

[Resonance Agent]Resonance Agent

Message validated against signals, resonance score per segment

MA Tool Setup (with Volto context)

Send

KPI + Resonance loop

[Activation Agent]Activation Agent

Insights back into CRM, segment updates in MA tool

Agent contributions

Research Agent

Before campaign planning

Current topics, language and attitudes of target group (n=500-4,000)

Marketing team no longer plans in a vacuum. Brief based on real market situation - not last quarter.

Audience Agent

Replaces or supplements CRM segment list

Qualitative segments with behavioural profiles

Targeting based on what customers really drive - not just what they bought. Higher accuracy, less wastage.

Resonance Agent

Between message draft and MA tool setup

Resonance score per segment, framing recommendations

A/B tests are reactive. Resonance Agent optimises proactively before sending. No wastage from wrong message.

Activation Agent

After sending, as feedback loop

Explanation of KPIs at segment level, updated segment data back into CRM

System learns not just from clicks, but understands why customers responded.

Agent KPI summary

Agent	Integration point	Value
Research Agent	Campaign planning	Real market situation instead of assumptions
Audience Agent	Target group targeting	Attitude-based instead of demographic
Resonance Agent	Pre-launch	Optimised message before sending
Scenario Agent	Strategy decisions	Quantified forecasts instead of hypotheses
Activation Agent	Post-launch	Closed loop, learning effect in system

Output:

3.2

Churn Prevention

Early Warning System

B2C: Energy, Telco, SaaS, Insurance

Churn is detected when it happens - not before it develops. Traditional scoring models use historical data, no leading attitude data.

With Volto

Research Agent

Continuous panel surveys

Audience Agent

Identifies churn-intent segments

Scenario Agent

Models churn probability over time

Activation Agent

CRM alert / customer success tool

Output: Churn alerts months before departure, segment-specific interventions

3.3

Product Development

Feature Resonance Testing

SaaS, FinTech, Consumer Apps, Energy (Tariffs)

Features are built based on internal assumptions or small user interviews. Whether they really resonate - only known after launch.

With Volto

Resonance Agent

Matches feature concept against segment signals

Scenario Agent

Simulates adoption probability per segment

Product team (prioritisation with confidence score)

Output: Resonance score per feature per segment, prioritised roadmap recommendation

3.4

Pricing & Tariff Optimisation

Price Elasticity per Segment

Energy, Telco, Insurance, Mobility

Pricing based on cost calculation and competitive benchmarks - not on actual price tolerance behaviour of individual segments.

With Volto

Research Agent

Captures willingness to pay & price sensitivity qualitatively

Audience Agent

Clusters by price behavioural patterns

Scenario Agent

Simulates adoption/churn at different price points

Pricing team / revenue management tool

Output: Price elasticity profiles per segment, optimal price point, churn risk per price level

3.5

Agentic Commerce

Autonomous Customer Interaction

E-Commerce, Energy (Tariffs), FinTech

AI agents interacting with customers (chat, email, recommendations) only know the customer from CRM data. No access to qualitative attitude data.

With Volto

Audience Agent

Assigns customer to current segment in real-time

Commerce/Support Agent (LLM) receives segment context as system prompt

Personalised response / recommendation / offer

Output: Segment context as live enrichment for every customer contact

3.6

Content & SEO

Topic Resonance

B2C general, Publishers, Brands

Content strategy driven by SEO tools and internal assumptions - not by what target groups really care about.

With Volto

Research Agent

Captures current topics & language of target group

Resonance Agent

Evaluates content ideas against segment signals

Content team / LLM content agent -> resonance-optimised content

Output: Topic priorities per segment, resonant framing proposals, language templates

3.7

Market Positioning

New Business Models

All industries for new products, business models or market entries

Time: 3-6 months until a founded positioning. Validation too small (n=10-30). Personas outdated immediately. No simulation.

Standard Flow (without Volto)

1 - Market Analysis (4-12 weeks)

Desk research, 10-30 qualitative interviews, optional quantitative study. Output: market sizing, competitive landscape, initial target group hypotheses.

2 - Positioning Workshop

Internal team develops positioning. Static personas modelled (demographic, psychographic assumptions).

3 - Concept Validation (2-6 weeks)

Concept tests with small focus groups (n=10-30). Qualitative feedback on positioning drafts.

4 - Go-to-Market Planning

Messaging framework finalised. Channel strategy based on target group assumptions.

5 - Launch & Learning Phase

Soft-launch in individual segments. Adjustments reactively after first data.

Data limitations in standard flow

Source	Data Type	Limitation
Secondary sources (reports)	Aggregated, retrospective	Too old, too generic
Focus groups / interviews	Qualitative, small (n=10-30)	Not representative, selective
One-off market study	Quantitative, one-time	No update, expensive, slow
Competitive monitoring	Public data	No insight into customer perspective
Internal experience	Implicit knowledge	Confirmation bias, outdated

Standard Flow (without Volto) → With Volto

Standard Flow (without Volto)

Desk Research + Hypotheses

Focus Groups (n=10-30, slow, expensive)

Static Personas (outdated immediately)

Positioning Draft (assumptions)

Concept Validation (n=10-30)

GTM Plan (assumptions)

With Volto

Desk Research + Hypotheses

[Research Agent]Research Agent

n=500-4,000 in ~2 hours, authentic open answers

[Audience Agent]Audience Agent

Who would use the business model and for what reasons?

[Resonance Agent]Resonance Agent

Tests positioning variants against real customer signals

[Scenario Agent]Scenario Agent

Which segments will adopt? Market size, price willingness, switching probability

GTM Plan (data-based)

[Activation Agent]Activation Agent

Segment profiles in CRM, lookalike audiences for paid, messaging templates

Agent KPI summary

Agent	Standard equivalent	Volto advantage
Research Agent	Focus groups (n=10-30, 4-8 weeks)	n=500-4,000 in ~2 hours, open answers
Audience Agent	Persona workshops (internal, static)	Attitude-based, dynamic, representative
Resonance Agent	Concept test (reactive, after draft)	Proactive, quantified, multiple variants in parallel
Scenario Agent	No standard equivalent	Market scenarios before launch, confidence scores
Activation Agent	Export to Excel / PowerPoint	Live feed into CRM, Paid, MA, Feedback Loop

Instead of 3-6 months - founded positioning decision in 1-2 weeks. Instead of hypotheses - real customer signals at every decision.

Output:

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