Your data and AI team, at a fraction of the cost.
A data and AI team costs six figures a year before it ships anything. On WebRobot, AI agents build and run your data pipelines, your agents, the apps on top of them and your own models — inside your own cloud or ours. Plans from €9 a month.
The console in 90 seconds: describe the data, build agents, query datasets, get the report. See the product →
Try it
Build it in a minute
Play with the three studios, right here in your browser: turn an agent into an app, design a pipeline from a page to a dataset, compose a team of agents — a chain or a swarm. Add boxes, click them, see what the studio would save.
Validated on EuroHPC, for research and innovation. Within our R&I allocation, our runtimes — distributed Spark ETL with its browser fleet, the encrypted agent runtime, Ray, private LLM serving on A100 GPUs and multi-node fine-tuning — have run on the Vega supercomputer in Slovenia. Commercial workloads run on your own cloud or on ours, never on EuroHPC; a wizard for researchers with their own EuroHPC allocation is in progress.
- ● London — WebRobot Ltd
- ● Cyprus
- ● Italy
- ● EuroHPC · Vega, Slovenia
Where the saving comes from
Three costs you cut drastically
The work stays: AI agents do most of it, the operations are automated, and your people spend their time where it matters.
Drastically less DevOps
Clusters stood up and torn down, browser fleets and Spark executors created for a run and destroyed when it ends, GPUs, a private network per tenant, schedules, retries, logs — automated. The machines run in your own cloud, billed by the minute they work.
Drastically less pipeline code
Our AI data engineer builds the pipeline from a description — databases, files, APIs, documents, IoT, the web — and runs it on distributed Spark. You review the result, not the code.
Drastically less agent engineering
Agents and teams of agents in the Agent Studio, the apps on top of them in the App Studio — with tools, schedules, human approval and full logs. What usually takes an ML team months is a canvas and a conversation.
Use cases
What people build with it
Six of the shapes this takes — most of them never touch the web. Each one links to the detail.
An assistant on your company knowledge
Contracts, manuals, tickets and reports answered by an agent that cites its sources — and never leaves your cloud.
documents → index → agent with tools → answers with sources
See howYour own sector model
A small model fine-tuned on your data and served privately, on your own GPUs or on ours.
internal data → clean dataset → full / LoRA fine-tuning → private endpoint
See howArchives made searchable
Scanned registers and handwritten documents transcribed, linked and searchable — with a human reviewing what the model is unsure of.
scans → transcription → entities → human review → archive search
See howA trading backend
Strategies written with an agent, backtested on market data from the exchange APIs and taken live behind an approval gate.
market data → strategy agent → backtest → approval → live
See howYour own AI app, sold to your customers
Turn an agent into an app on its own address: your customers sign in and pay, you get paid, we keep a platform fee.
agent → app on your-name.webrobot.eu → customers pay → payout to you
See howCompetitor prices
Know every day who sells your product and at what price, matched by EAN across shops.
shop pages → daily extraction → matched catalogue → alert on undercut
See howPricing model
Your bill does not grow with your results
Almost every data platform meters what you extract: per record, per request, per page, per credit. It sounds fair until it works — the more the pipeline finds, the more you owe. Success is what makes the bill explode, and the only lever you are left with is collect less: exactly the opposite of why you built the thing.
So growth moves the part you buy wholesale — compute, at cloud prices, which fall every year — and not a per-record margin someone adds on top. And because the heavy VMs are ephemeral, that part is charged by the minute a job actually runs, not by the month.
What each plan costs How this compares to per-record platforms
Metered by volume
The meter runs on rows, requests or credits. Double the results and you double the invoice. Nobody can tell you next month's number, because it depends on how much the web gives you.
Metered here
A fixed orchestration fee, and the machines are yours — billed by your own provider, at their price, for the minutes they ran. Ten thousand rows or ten million, our share of the bill is the same number.
For builders
DevOps automation an AI agent can use
What we sell is not compute — in every plan the machines are yours. It is the DevOps around them, automated: clusters stood up and torn down, browser fleets and Spark executors created for a run and destroyed when it ends, a private network per tenant, schedules, retries, logs. A new generation of cloud in the BYOC paradigm, where the provider owns no hardware.
And it is meant to be driven by software rather than by a console, so the operator can be another AI agent. That is the point of the MCP surface below: a next-generation agentic product can treat this as its infrastructure instead of building acquisition, processing and an agent runtime first.
WebRobot is built to be built on. The same stack behind this platform — web acquisition, distributed processing and an agent runtime — runs the products of the PioneersGalaxy studio, and is available to teams building next-generation agentic platforms that need those three things without building them first.
Reachable as a backend: REST API, CLI, plugin SDKs and an MCP server that exposes the whole API as tools, forwarding the caller's own credential — so your agents drive the platform with exactly the permissions their user has, and nothing more.
Commercially this is what BYOC is: the stack installs in your own cloud account or on your servers, your product keeps its data and its cloud bill, and you licence the software per node — no compute resold in between.
Talent programme. Recent graduates with an entrepreneurial mindset: inherit our infrastructure and build your own AI products on it, in revenue sharing.
Apply →