A pipeline that builds itself around your goal.
Cost and expected coverage before execution; strategy changes until the criteria are met — or an honest account of why they can't be.
See the cost before it is spent.
Phiuto weighs acquisition strategies across public, customer-connected and commercial sources — scored on coverage, quality, freshness, cost, speed, access and licensing. It picks the best strategy for your objective, not the most complete one available: a fast directional answer should not be sold a €300 path.
If a goal needs paid access, a credential or a licence decision, Phiuto asks rather than assuming.
Goal-driven, not workflow-driven.
The pipeline is defined in advance. It stops when the steps are done — whether or not you got what you needed.
The pipeline is constructed around the objective. It stops when the objective is satisfied — or is shown to be unattainable, and says so.
Traditional automation executes a workflow. Phiuto pursues an objective.
A completed goal is not one API call.
Between your objective and a verified answer sits a coordinated system of specialised components — each one a real engineering problem. The agents are not the product; the system that drives them toward the objective, and can prove how it got there, is.
The models are rented. The source intelligence, the entity graph and the evaluation data are ours — and they compound with every run.
The world's data, as a territory our agents navigate.
Every source we've ever touched — registries, APIs, filings, sites — is a node in a graph, connected by what links to what. Agents traverse it in parallel: reaching a node probes it, a hit turns it red and opens its neighbours, a dead end is recorded so no agent wastes time there again. The graph is live below.
Every traversal leaves the graph smarter: which sources answer, what they cost, where they fail. That map is the asset no competitor can copy by calling the same APIs.
If you want the detail.
The core is above. The rest is here — positioning, trust, pricing — for whoever is looking for it.
What Phiuto is not+
Not a database. We don't aim to own data — we aim to know how to reach it, and to be the layer that reaches it well.
Not a scraper. Scraping is one acquisition method among many, and usually not the first we reach for.
Not a workflow builder. No canvas of nodes to wire up. If you have to design the pipeline, we've failed at the core premise.
Not a general-purpose agent. Phiuto does one thing: acquire external data against an objective. It does not write your emails.
Not a model company. The model is a component. The intelligence is in source knowledge, strategy selection and evaluation.
Not a replacement for the analyst. Phiuto assembles and verifies the information; the human decides.
Trust, provenance and licensing+
For every material result, Phiuto can expose its source, retrieval time, transformation history, verification, confidence — and its limitations: what remains uncertain or unobtainable.
Licence terms are enforced in code: retention, redistribution and rate limits are attributes of each source, checked at acquisition and at delivery. Budgets are hard ceilings — no goal spends past its authorised limit without a checkpoint.
The goal isn't to pretend data is perfect. The goal is to make data — and its uncertainty — transparent. In procurement and financial contexts, an unsourced number is unusable regardless of whether it's correct.
How pricing works+
Priced per completed goal where possible — it matches the product's own unit of execution, and it's the only pricing you can evaluate before spending. Plan Mode shows estimated cost and expected coverage before execution.
Four layers, sequenced: SaaS for teams through a focused workflow · usage per completed goal · a developer API for applications and AI agents · enterprise infrastructure for high-volume acquisition. Land with workflows; expand into infrastructure.
Questions we get+
Isn't this just scraping?
Scraping is one method and usually not the first. The product is strategy selection and completion measurement.
How do you know the data is right?
Confidence is computed from independent corroboration, not asserted, with provenance on every material field. We'd rather return 82% with a gap report than 100% that's quietly wrong.
Won't the AI labs just do this?
Models improve reasoning, not access. Licences, credentials, source behaviour, entity resolution and verification are accumulated operational assets.
What happens when a goal can't be completed?
Phiuto says so: what it could not obtain, why, and what it would cost to close the gap. A system that can only succeed will fabricate.