How Beecrowdy Works

Data-Driven Decisions. Zero Barriers.

Beecrowdy combines Swiss location intelligence, curated analytical workflows, and AI-assisted interpretation to help teams move from isolated data to shared decisions.

Why we built Beecrowdy

Started early. Guided by the same goal ever since.

At Hontolab, we started working on this problem early. Long before generative AI became mainstream, we were already building advanced analytics, simulations, and data-science systems for real decisions.

Again and again, we saw the same gap: a lot of effort went into good analysis, but too little of it became action. Results arrived too late, stayed too complex, were built once and then left to go stale, or got stuck between analysts and decision-makers. Deciding without data is risky, and misreading it can be even more dangerous.

Less friction

What Zero Barriers means in practice.

Less friction between data and decisions. Less misinterpretation. More direct answers with context and explanation.

What that means in practice

Less waiting for analysis

Less dependency on scarce specialists

More usable answers instead of raw output alone

More shared decisions across the team

Six transformations

Beecrowdy is not one feature. It is a shift in how work gets done.

These six transformations reinforce each other. Better data matters little if nobody can understand it. Better reports matter little if they never reach daily decision-making.

beecrowdyLocation Intelligence · SwitzerlandSix transformations. One platform.01fromSearchtoAnswersAsk, don't search02fromData silosto connectedknowledgeData that talks to data03from staticDashboardsto livingReportsReports that explain themselves04from theAnalyst queueto the wholeTeamOne workspace per job05from theDesktoeverywhereAnswers come to you06fromTool sprawlto onePlatformEverything on one foundationFrom isolated data to shared decisionsPowered by HontoFlow · hontolab.com

Six transformations, six interdependent shifts — on one shared foundation.

How people use it

One report, many perspectives.

Beecrowdy helps people find information fast, but it does not stop at chat. Analysis can turn into living reports that remain shareable, interactive, and open to follow-up questions.

Chat

Find answers quickly, clarify a point, start a comparison, or get the next useful angle on the data.

Living reports

Reports can be shared and stay interactive like a dashboard. Different roles can ask follow-up questions from their own perspective without losing the shared analytical context.

Like a 360 camera, Beecrowdy lets you choose your perspective freely without losing the shared picture.

Data & analytics

Location intelligence needs more than one dataset.

Beecrowdy does not work on an isolated layer of numbers. Context data, Switzerland-wide datasets, strategic sources, and local information can be brought together so analysis is not only fast, but grounded.

Data layers

Layer 01

Built-in context data

Weather, holidays, seasonality, and similar drivers are part of the foundation because they shape real usage and visitor patterns.

Layer 02

Structured Switzerland-wide datasets

Well-structured open datasets can become directly usable inside Beecrowdy. In tourism, HESTA data is one example: it is directly accessible together with built-in analysis and benchmark views.

Layer 03

Strategic datasets as needed

Strategic datasets such as Swisscom Mobility can be added according to local needs and use case. They bring not only data access, but often ready-to-use analysis, benchmarks, and forecasts.

Layer 04

Local and customer data

Local operational data, internal reports, knowledge sources, and connected services can also be brought in so analysis reflects local reality instead of generic context alone.

Analytical layers

Analytics that come with the data

Datasets are more useful when they include built-in analysis and benchmark views, not just access to raw tables. Beecrowdy is designed to make those analytical layers immediately usable instead of starting from raw data every time.

Precomputed analytics

Sophisticated data science such as forecasting, benchmarking, and heavier models is often too slow or too costly to recompute inside a live assistant loop. Preparing it ahead of time keeps answers fast, reliable, and economical. That matters especially for visitor or guest forecasts that take weather, holidays, and seasonality into account.

HontoFlow

Beecrowdy is built on HontoFlow.

Beecrowdy is built on the HontoFlow AI Harness Engine, developed by Hontolab. On top of this foundation, Beecrowdy adds Swiss location intelligence with specialized datasets, tools, and applications for location-based analysis and real-world insight.

What is an AI Harness Engine?
An AI Harness Engine brings together models, data, tools, and workflows so they become a practical system for answers, reports, and follow-up analysis. In Beecrowdy, that is the role HontoFlow plays.
How is that different from an LLM?
An LLM is a language model. It can read and generate text, but by itself it is only one component. HontoFlow is the system around and above those models: it coordinates how models, data, tools, and workflows work together inside Beecrowdy.
Is Beecrowdy based on third-party orchestration technology?
No. Beecrowdy is built on the HontoFlow AI Harness Engine, developed fully in-house by Hontolab. That keeps the orchestration under our own control instead of placing a third-party layer between the system and your data. As a result, we can say clearly which components are involved, where data goes, and when external providers are actually used. Only selected LLM calls rely on external model providers.
What does Beecrowdy add on top of HontoFlow?
Beecrowdy adds Swiss location intelligence on top of HontoFlow: specialized datasets, spatial context, analytical workflows, and applications for real-world decisions.
Is Beecrowdy just RAG?
No. RAG is a technique for giving a model relevant information from documents at answer time. Beecrowdy can use that approach, including with private knowledge sources, but it is broader than that. It also works with structured datasets, analytics, benchmarks, connected services, and prepared models.
Why are some analytics prepared in advance instead of computed live?
Because not every useful analysis belongs inside a live assistant loop. Forecasts, benchmarks, and heavier models are often better prepared ahead of time so answers stay faster, cheaper, and more reliable.
Can local data and organizational knowledge be connected?
Yes. Local data, internal knowledge sources, and connected services can become part of the analytical context, so reports reflect the local reality instead of generic background alone.

Best understood live

With a real place, a real question, and real data.

In a short demo, we open a real report and show how Beecrowdy works for your use case.

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