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WizAI · Agentic management

Your AI colleague to drive customer satisfaction forward

WizAI and its AI agents analyse your data, surface the priorities and prepare recommendations without waiting for your questions. Your teams decide.

What it changes for our customers
Observed on the first networks equipped
WizAI
Customer feedback put to work
×4
with the same team
Collection ×2.5 more verbatims collected
Analysis 3 hrs saved every week
Review management ×4 reviews handled per hour
Summaries & actions 9 in 10 sites with an action plan under way
See the agents in a demo

They trust Wizville

Speedy Castorama Etam Truffaut Diptyque O2 Picard Intersport
Conversational agent

WizAI takes the initiative, your teams decide

WizAI works out which analyses are worth running, runs them and prepares recommendations, even when nobody is looking at the platform. Its memory keeps the context that matters for your brand. Its custom skills apply your business rules, your priority criteria and your reporting formats. Then ask your questions to dig further into the results.

  • Prioritised recommendations
  • Active memory
  • Custom skills
Your priorities are ready. Let's explore them together. 1,248 reviews analysed
1 Strengthen in-store advice priority detected

Customers report a lack of advice. WizAI has grouped the feedback and prepared recommendations for each level of the network.

2 Prepare review handling suggestions ready

WizAI prepares reply suggestions matched to your brand's tone. Your teams approve them before publication.

What do customers say about advice? Which stores need support? Prepare a regional summary
Ask WizAI a question…
From listening to action

AI agents at every step, from collection to action plans

The AI agents enrich and analyse your customer feedback, bring the priorities to the surface and prepare action plans suited to head office, to the regions and to each site. Your teams decide and carry the plans out to improve the customer experience.

01
Collection

Get useful feedback, not just scores.

Open question · initial verbatim didn't like it at all
AI follow-upWhat exactly did you not like?
MD
Marie D.“Twenty minutes waiting at the checkout, nobody on the shop floor.”
Enriched verbatimCheckout waitShop floor service
In pictures
AI verbatim follow-up

A score with no explanation? The agent asks the follow-up question that gets the customer to be specific, and turns a “didn’t like it” into an actionable pain point.

Also in Collection : Product reviews · AI voice collection

See the AI follow-up on your surveys
02
Analysis

Read your results and the market's, without building a single pivot table.

Lyon Part-Dieu area · topic “Wait”
Your store3.9
Competitor A4.3
Competitor B3.5
Gap −0.4 vs A
In pictures
Local competition

Around each store, how you are perceived compared with competitors in the area, based on public reviews.

Also in Analysis : Autonomous semantic taxonomy · Semantic classification of verbatims · NPS and satisfaction analysis · Network performance · National competition · Sales and satisfaction correlation

See the analysis of your reviews and your market
03
Review management

Handle the right review, at the right time, with the right reply.

★★☆☆☆Marie D. · “Wait too long on Saturday”
Hello Marie, thank you for your feedback. Twenty minutes' wait is too long: we are opening an extra checkout on Saturdays from next week…
Review and publishRewrite
In pictures
Suggested reply

A reply ready to proofread, in your brand’s tone, based on what the customer wrote.

Also in Review management : Customer satisfaction summary · Handling prioritisation

See a reply drafted for one of your reviews
04
Summaries

Go from a wall of data to three useful lines.

Week 36 · South-East network
Checkout wait12 stores
Product availability4 stores
Fitting room cleanliness2 stores
In pictures
Priority actions summary

Findings translated into actions: what to handle first, at which level of the network, and why.

Also in Summaries : Multi-source summaries · Clustering by priority · Chart and table summary

See your week in summary
05
Actions

Go from findings to what needs doing, at every level of the network.

Topic “Checkout wait”
Head office
Review the Saturday staffing schedule across 12 stores
Region
Prioritise hiring extra checkout staff in Lyon
Store
Open checkout 4 from 2 p.m. on Saturdays
In pictures
Action recommendations by level

At head office, in the regions or in store, everyone receives the actions within their reach, phrased for their level of decision.

Also in Actions : Sector business policy

See the actions suggested for your network
See the agents on my data
The agents work on your Wizville data and on the public reviews of your market. The agents propose, your teams decide.

Your brand's context guides the recommendations

Specialised agents, an active memory and custom skills, so the platform works the way you do.

An active memory
WizAI keeps the context that matters, so nothing starts from scratch at each exchange.
Custom skills
Your business rules, your priority criteria and your reporting formats guide the recommendations.
Specialised agents
Each agent handles one precise task: enriching a comment, analysing, summarising or recommending.
MCP

Your Wizville data in ChatGPT, Claude, Gemini and Copilot

Your customer satisfaction data becomes a source your AI assistant queries directly, with no export and no connector to build. It sits alongside your sales, your footfall and your staffing schedules, and everything your AI knows how to do, it can now do on that data: what used to take a BI project now fits into a single question.

The signed-in user’s scope and access rights are respected. No personal data is shared without an explicit request.

MCP

What an agent never does on its own

Agents speed up the work, they do not replace it. Three principles that apply across the platform.

Nothing goes out without you
A suggested reply remains a draft until the person replying approves it.
Your data stays yours
The agents work on your account's data and on the public reviews of your market, within each user's scope.
Enabled one by one
Each agent can be enabled, disabled and configured according to your needs and the organisation of your network.
The agents propose, your teams approve: that is the rule across the whole platform.

What our customers say

ChatGPT Image 23 juil. 2026, 18_41_05 (2)

“Wizville isn't just a tool. There is a whole team behind it, and support from customer satisfaction experts, which allows us to achieve excellent results.”

Delphine Audibert
Chief Digital Officer, Jacadi
What they say
FAQ

Frequently asked questions

An assistant specialised in a single task: asking the right follow-up question, analysing a score, preparing a reply, writing a summary, recommending an action. It works inside the platform, on your data, and hands its output to your teams.

A chatbot waits to be spoken to. An agent has a precise mission and runs within your workflow: when a review comes in, when a period closes, when a committee meeting needs preparing.

On the data in your Wizville account (surveys, reviews, verbatims, scopes) and, for competitor analyses, on the public reviews of your market. Each user stays within the scope assigned to them.

No. The agent prepares a proposed reply; the person in charge reviews it, adjusts it if needed, then publishes.

Yes. Agents are enabled family by family and agent by agent, according to your needs and your configuration. Your Wizville contact helps you choose the ones that matter for your teams.

Yes, that is their home ground: analyses by site and by region, review prioritisation store by store, grouping of sites that share the same problem.

It depends on your plan. Ask your Wizville contact: they will tell you, family by family, what is already included and how to activate the rest.

Through your Wizville contact, or by writing to support. Agents are activated family by family, according to how your network is organised.

Want to see the agents at work on your reviews?

30 minutes with a Wizville expert to see the agents running on your data, your reviews and your network.

Request a demo