Floowed vs Hyperscience: Lending Decisioning Platform vs Enterprise IDP (2026)

Floowed vs Hyperscience: Lending Decisioning Platform vs Enterprise IDP (2026)

If you are a credit or risk team evaluating Floowed against Hyperscience, you are usually comparing two different categories of software, not two products in the same one. Hyperscience is enterprise intelligent document processing (IDP) with custom-trained machine learning models, deployed at the largest insurance carriers, banks, and government agencies. Floowed is a lending decisioning platform built as two products on one platform: document intelligence that reads and analyses any loan document at any quality into clean, decision-ready data, and a Decisioning Engine that runs your credit policy on that data, every application, every time. It is built for lenders that want to ship a credit policy this quarter and version it next quarter.

Both platforms touch documents. Only one of them turns those documents into a credit decision your team can author, version, and audit without writing code. This guide walks through the practical differences in the Floowed vs Hyperscience comparison: what each platform is built for, how long it takes to deploy, how pricing works, and which type of lender each one fits.

Floowed vs Hyperscience at a glance

Enterprise IDP vs lending decisioning: the category gap

Hyperscience sits in the same category as Rossum, ABBYY, and Nanonets: extract structured data from documents at high accuracy, then hand that data off to whatever downstream system makes the actual decision. The hard problem they solve is extraction at scale on heterogeneous document types, often with custom-trained models tuned to the buyer's exact forms. That work is real and valuable. It is also only the first leg of a credit workflow.

Floowed is in a different category. We call it lending decisioning, and the canonical reference points are platforms like Taktile, Provenir, GDS Link, Scienaptic, Lentra, FICO Platform, Experian PowerCurve, and CRIF Strategy One. A decisioning platform owns the path from a borrower submission to an approve, decline, refer, or price decision. Document intake, extraction, and analysis are part of that path. The product is two things working together: document intelligence that reads and analyses the paperwork, and the policy that decides who gets credit and on what terms, expressed as rules, scorecards, knockouts, segmentations, and routing logic.

For a deeper walkthrough of why these are different categories, see credit decisioning vs credit scoring and what is a credit decisioning platform. Buying an IDP when you actually need decisioning is one of the most common, and most expensive, missteps in lending tech.

Document intelligence: reads and analyses, not just extraction

The biggest misread in a Floowed vs Hyperscience comparison is treating Floowed's document layer as OCR. It is not. Floowed reads and analyses the paperwork other IDPs choke on: handwritten passbooks, photographed and scanned bank statements, skewed mobile captures, and the messy real-world loan documents that US-built IDPs like Ocrolus, Rossum, and Hyperscience, all optimised for pristine documents, struggle with. Floowed does not stop at pulling text off the page. It analyses: income normalization, cash-flow and bank-statement analysis (average daily balance, DSCR), fraud and tampering signals, and cross-document validation, turning any-quality input into clean, decision-ready data.

That analysis includes an evidence cross-check that pure extraction tools miss. Floowed cross-checks what a document claims against the evidence in the image itself: an ID against the selfie, a utility bill against a meter photo, a vehicle title against the chassis photo on a secured loan, an invoice against a delivery photo. Claimed values that do not match the image surface as a fraud signal rather than passing through as clean data. For lenders, that is a fraud surface an extraction-only IDP never sees.

Deployment time: months vs weeks

Hyperscience deployments are enterprise programmes. The typical sequence is a discovery phase, model training on the customer's specific document types, IT integration with core banking or claims systems, workflow configuration, user acceptance testing, and a phased rollout. Insurance carriers and government agencies often take six to nine months from contract to first production document. That timeline is appropriate for the buyer profile: a $100M+ organisation processing millions of documents a year, where one percentage point of straight-through processing is worth seven figures.

Floowed deployments are measured in weeks of proper configuration to full production rollout, not the quarters an enterprise IDP forces on you. The reason is structural. We do not custom-train extraction models per customer; the document intelligence reads and analyses bank statements, payslips, IDs, financial statements, and loan packets out of the box, including poor-quality scans, handwritten inputs, and mobile captures. Credit and risk teams use the Decisioning Engine to author policies in plain English, not in BPMN diagrams or JSON config files. Integrations to LMS, credit bureaus, KYC vendors, and banking data providers are pre-built (40+ at last count). What used to be a six-month systems integration project is a configuration exercise.

For lenders running RFPs, the practical question is: how many quarters until this is making real decisions on real applications? With Hyperscience, that answer is usually two or three. With Floowed, it is one.

What does Hyperscience actually cost?

Hyperscience pricing is not published. Buyers go through a sales cycle, get a custom quote, and, by industry reputation, typically land in a six-figure annual contract for a meaningful deployment. The largest enterprises are reported to pay seven figures. Public reviews have cited per-page costs up to around $1.50, well above cloud-native alternatives, though actual rates vary by contract. Implementation services are usually billed separately, on top of the licence. There is nothing inherently wrong with that model for the largest accounts, but it leaves most lenders unable to benchmark, model unit economics, or pilot without a long procurement process.

Floowed pricing is consumption-based on credits, sized to your operation. There is no long sales cycle: one short call determines the right package and cost, and the all-in number lands at a fraction of typical enterprise platform cost. There are no hidden professional services line items for standard rollouts. A credit team can get a real number fast, run the math against their loan book, and decide in a meeting whether it is worth a demo.

A fast, credits-based quote is also a forcing function on the vendor. If we cannot deliver value at the package a lender's volume calls for, we should not be charging it. That constraint shapes the product: the Decisioning Engine has to be usable without a services engagement, integrations have to be self-serve, and onboarding has to be weeks of proper configuration, not the quarters an enterprise IDP rollout forces on you.

Services-led vs self-serve

Hyperscience operates a services-led model. The platform is powerful, but the buyer is expected to engage Hyperscience's professional services team, or a systems integrator, to roll it out. That is consistent with the customer base: large institutions with their own IT and procurement processes that expect a vendor with a delivery arm.

Floowed is self-serve by design. The Decisioning Engine is the policy authoring surface, and it is intended to be used by credit and risk teams, not by engineers or implementation consultants. Credit officers operate it day to day; risk teams own policy authoring at the program level. Versioning, simulation, and audit logging are built in so that a team can change a knockout rule on Tuesday, simulate it against last quarter's applications, and ship it to production on Wednesday with a full audit trail.

If your organisation prefers to outsource implementation to a vendor's services team, Hyperscience's model fits. If your credit and risk teams want to own the policy and ship changes without raising tickets, Floowed's model fits. See the plain-English credit policy builder guide for what good policy authoring looks like in practice.

Score orchestration: where Floowed plugs in

One of the most common questions on a Floowed evaluation is whether we replace the customer's existing scoring stack. We do not, and that is intentional. Floowed is score-agnostic: bring any score or your own model and it is absorbed unchanged. We orchestrate, we do not compete with scoring vendors. The Decisioning Engine orchestrates whatever scoring inputs make sense for the lender: traditional bureau scores like FICO and local equivalents, alternative-data scores from Zest AI, CredoLab, or Trusting Social, and in-house champion-challenger models. Those scores become inputs to the policy, not the policy itself.

Hyperscience does not occupy this layer at all. It extracts data and hands it off. Whatever scoring or decisioning happens downstream is the responsibility of another system, often a legacy LOS, a homegrown rules engine, or a separate decisioning platform. Lenders who buy Hyperscience for IDP frequently end up also buying a decisioning layer to sit on top of it. For a comparison of the leading decisioning engines, see credit decision engine comparison 2026.

The IDP-only category is real and useful, just bounded. For a side-by-side view of Floowed against the closest IDP-only platforms, see Floowed vs Rossum and Floowed vs Docsumo.

Where each platform fits

Hyperscience fits when

• You are a $100M+ revenue enterprise (insurance carrier, top-tier bank, government agency) processing millions of documents a year.
• You have a services budget and a multi-quarter implementation timeline.
• Your downstream decisioning is already handled by a separate system (LOS, claims platform, internal rules engine), and you primarily need world-class extraction.
• Custom-trained models on your specific forms are a strategic requirement.
• Procurement expects opaque, contract-negotiated pricing as part of the standard process.

Floowed fits when

• You are a lender (bank, fintech, neobank, NBFC, multifinance, BNPL, microfinance, or specialty lender) that wants decisioning out of the box.
• The credit and risk teams want to author and version policies directly, without code or vendor services on the critical path.
• Your documents are real-world: handwritten, scanned, photographed, mixed quality, and you need them read and analysed reliably.
• Time to first production decision matters: weeks, not quarters.
• You want score-agnostic orchestration so you are not locked to one scoring vendor.
• A fast, consumption-based quote and predictable unit economics matter to your CFO.
• Your roadmap includes connecting to LMS, bureaus, KYC, and banking data without building each integration from scratch.

For a structural look at where decisioning sits relative to your existing systems, see loan origination software vs decisioning platform. Decisioning is a layer, not a replacement for everything. To go deeper on the engine itself, see what is loan decisioning.

Floowed in production

This is live, not theoretical. Floowed runs in production at Alon Capital. Founder Rene de Jesus puts it plainly: "Floowed reads the documents, runs our credit policy, and surfaces a decision in minutes." That is the two-product platform in one sentence: document intelligence reads and analyses the paperwork, and the Decisioning Engine runs the policy on the result.

External references

• Gartner Peer Insights: Intelligent Document Processing for context on the IDP category Hyperscience competes in.
• Forrester Wave: Digital Decisioning Platforms for the analyst view on the decisioning category Floowed competes in.
• BCBS 350 on prudent credit risk management practices, the regulatory backdrop for auditable decisioning.
• CFPB supervisory guidance on adverse action and explainability in credit decisions.

Frequently asked questions

Are Floowed and Hyperscience competitors?

Only at the document layer. Hyperscience is an enterprise IDP focused on extracting structured data from documents using custom-trained ML models. Floowed is a lending decisioning platform: documents are an input, read and analysed by Floowed's document intelligence, but the product also includes the Decisioning Engine that turns that data into a credit decision. Most lenders evaluating both eventually realise they are different categories, not interchangeable products.

Does Floowed replace Hyperscience for document extraction?

For most lending document types (bank statements, payslips, IDs, financial statements, loan packets, KYC documents), Floowed's built-in document intelligence reads and analyses out of the box, including poor-quality scans, handwritten passbooks, and mobile captures, and it cross-checks claimed values against the evidence in the image to catch tampering. For very high volumes of highly specialised forms with custom-trained models, Hyperscience is more specialised. The deeper question is what you want to do with the extracted data: if the answer involves a credit policy, Floowed covers both legs.

How long does Floowed take to deploy compared to Hyperscience?

Floowed is typically days to a few weeks for a first production decision flow, with full rollout in weeks. Hyperscience deployments at enterprises typically run two to three quarters, including model training, IT integration, and phased rollout. The difference comes from Floowed's Decisioning Engine, pre-built integrations, and out-of-the-box document intelligence.

How much does Hyperscience cost?

Hyperscience does not publish pricing. By industry reputation, meaningful deployments land in six-figure annual contracts, with the largest enterprises reported in seven figures, and implementation services are typically billed on top. Public reviews cite per-page costs well above cloud-native alternatives. Quotes are custom, so confirm current numbers with Hyperscience directly.

What is Floowed's pricing compared to Hyperscience?

Floowed pricing is consumption-based on credits and sized to your operation. There is no drawn-out sales cycle: one short call determines the right package and cost. Hyperscience pricing is not published; enterprise contracts typically run into six figures annually, with implementation services often billed separately.

Does Floowed support our existing credit scoring models?

Yes. Floowed is score-agnostic: bring any score or your own model and it is absorbed unchanged. The Decisioning Engine orchestrates FICO, local bureau scores, alternative-data scores from vendors like Zest AI, CredoLab, and Trusting Social, and in-house models. Scores are inputs to the policy, not the policy itself, so you keep the scoring stack you have invested in. We orchestrate, we do not compete with scoring vendors.

Who owns policy changes inside Floowed?

The credit and risk teams. The Decisioning Engine is designed for credit officers and risk leads to author, simulate, version, and ship policy changes without engineering or vendor services on the critical path. Every change is versioned and audit-logged, so risk and compliance get a full trail without extra configuration.

When should we choose Hyperscience over Floowed?

Choose Hyperscience if you are a large regulated enterprise (top-tier bank, major insurer, government agency), have a services budget and multi-quarter implementation tolerance, and primarily need best-in-class extraction with custom-trained models, with downstream decisioning handled by another system. For lenders that need decisioning out of the box, with document intelligence that reads and analyses real-world paperwork, a fast consumption-based quote, and a policy layer owned by credit and risk teams, Floowed is the better fit.

Book a demo

If you are a lender comparing Floowed and Hyperscience, the fastest way to know which one fits is a demo on your actual documents and your actual policy. We will load a sample of bank statements, payslips, or loan packets, read and analyse them, build a working version of your credit policy on the Decisioning Engine, and show end-to-end documents to data to decision. Start free, or book a demo and we will tailor the session to your portfolio.