Engage · Listen · Act — as one system
Most companies collect data in one tool, understand it in another and act on it in a third — losing meaning at every handoff. Mobile Institute is the single environment where a question reaches a person, an answer becomes a model, and a model becomes an action.
The problem
The survey lives in a survey tool. The reviews live on the storefront. The purchase history lives in the ERP. The dashboard lives in a BI licence nobody logs into. And the one thing you actually wanted — do something about it, today, automatically — lives nowhere at all.
Every integration you bolt on is another place where context dies.
The answer
One engine. Every capability speaks the same language, shares the same access control, writes to the same activity log, and can trigger every other one.
Declarative and behavioural, in every channel your customers actually use.
Conversational surveys that feel like a chat, not a form. Embeddable widgets and post-purchase feedback. E-commerce and POS integrations. Web signals, scrapers, in-store vision and beacons. If your customers leave a trace, we can read it.
Raw input lands in canonical models we designed over years of market research.
Respondents, purchases, products, services, venues, segments, demographics. Enrichment, validation and computed attributes run the moment data arrives. Queries, boards, KPIs and AI agents on top of your own knowledge bases. Not a chart of what happened — an answer to what to do.
This is where most platforms stop and we start.
Flows chain any action into any process — automated, or with a human approval step where judgement belongs. Mailings that react to the buying journey. Alerts to Slack, Teams or email the moment a metric moves. Recommendations, loyalty, coupons, communities, support conversations. Your data doesn't end in a report. It starts a process.
AI inside, not beside
Most "AI features" are a chatbot stapled to a database. Ours sits inside the engine: it sees the data, respects the permissions, and can invoke any action the platform has — with an audit entry for every step.
Chats over your own datasets and knowledge bases. "Why did NPS drop in Kraków last month?" returns an answer with the events behind it — not a chart to interpret.
Agents code open-ended answers, enrich records, draft reports, triage tickets and run flows. Every action they take goes through the same permissions and lands in the same log as a human's.
Surveys that adapt to the answer, probe when it is vague and stop when it is enough. Higher completion, richer verbatims, coded on arrival.
Every capability is exposed to external AI agents through MCP. Your assistant, your copilot, your stack — talking to live data instead of exports.
Pillar one · ice
Understanding data. Our own data models and methodologies, worked out over years of studying markets: how to ask so an answer means something, how to describe a respondent, a purchase and a segment so that five sources speak about one reality — and how to tell a finding from noise.
Pillar two · fire
AI, scale, tools, platform. An engine that collects from every channel, processes at write time, serves analytics live and turns a conclusion into automatic action — for a hundred customers at once.
Apart, each of these is a commodity: a research agency without technology delivers PDFs once a quarter, and a platform without methodology is an empty tool where the customer has to know what to ask and how to count it. Together they make something neither side builds alone — which is why our domain models live inside the platform, not in consultants' heads.
Research · methodology · models
Designs the questions, the samples and the canonical models. Knows when a number is a finding and when it is noise — and writes that knowledge into the platform so it stops being tribal.
Platform · AI · scale
Builds the engine: components, actions, flows, agents. Fifteen years on one system, rebuilt so that every next solution is a configuration, not a project.
Why it's different
Spaces, Resources and Capabilities mean every solution we ship is a configuration of the same engine — not a separate product with its own database, its own login and its own gaps.
Blueprints and configurators, not a rewrite.
Granular permissions and roles across every resource.
Every action — a UI click, an API call, an agent — lands in one activity log.
UI, API, scheduler, flow, or an external AI agent over MCP. Same action, same rules.
Tasks, Events, Intents and Plugins extend the engine instead of copying it.
Five layers
Products on top are configurations of what lies underneath. Open any layer to see what's inside.
What we build on it
NPS, CES, CSI and NBS tracked live, with alerts the moment a location, product or segment starts sliding — and the flow that routes it to whoever can fix it.
Wave studies, segmentation, personas, concept testing and pricing — designed, fielded, cleaned, analysed and published without leaving the system.
For e-commerce, retail and manufacturers. Ratings, stamps, coupons and consumer communities that turn buyers into a panel you own.
Industry-specific platforms that get the whole engine — data models, automation, billing, access control — and add only what is genuinely unique to them.
Built for the people who actually use it
Design a study, field it, clean it and report it in one place. Open-ended coding, weighting and canonical models included.
See the metric move and act on it the same hour, in the same tool, without filing a ticket.
Experiments, feature toggles, KPIs from any source, and automation that turns a signal into a task with an owner.
An API-first platform where every capability is an action. Extend it with plugins, drive it from your stack, or hand it to your own AI agents over MCP.
Granular access control down to the resource. A full audit trail on every action. Consent, retention and deletion handled as first-class parts of the data model — not as an afterthought bolted on before an audit.