Floowed vs Nanonets: Running the Whole Case vs Document AI Agents

For teams whose work doesn't end when a document is read, Floowed is the stronger choice than Nanonets, because it runs the whole case: it gathers what the case needs from your systems and external sources, reads the documents that arrive, chases whoever still owes something and decides against your rules on a deterministic policy engine, then writes the result back.

If you are evaluating Nanonets, you are usually trying to solve one of two problems: turn messy documents into structured data, or get a whole operation (supplier onboarding, claims intake, invoice approval, a credit application) from the first request to a finished outcome. Nanonets handles the first problem well across almost any document type, and now builds AI agents that act on the result for back-office processes like accounts payable and claims. Floowed is built for the second. It is the operations platform: it runs each case from start to finish across every system and person involved, and documents are one of three input lanes next to your own systems and external sources.

This comparison is for operations leaders, the people who own the process rather than the system it runs in. We will be fair to Nanonets, which is one of the strongest general-purpose document AI products on the market. We will also be specific about where its scope ends and why that matters when the hard part of your process isn't reading the document, it's everything that happens around it.

Floowed vs Nanonets at a Glance

Document Agents vs Running the Case

The most important thing to understand before comparing features is that Nanonets and Floowed start from different places.

Nanonets started as a horizontal IDP platform and now positions itself as AI agents for enterprise data processing. The strategy is breadth: any document, any industry, any use case. Drop in your SOPs and policy documents, Nanonets parses the rules into a context graph and builds agents that follow them, on top of its own OCR-3 extraction model. It works for AP automation, for ID verification, for medical claims, for shipping paperwork, for HR onboarding. If your problem is "we receive a lot of varied documents and want to stop typing them in," Nanonets is a strong fit.

Floowed starts from the case. Underneath is an AI-native, multi-agent runtime that works each case as a long-running asynchronous process, from intake to outcome: it gathers, interprets, chases and decides, looping the middle three until the case is ready to decide, then acts by writing the result back to every system that needs it. A supplier onboarding case, for example, spans the ERP, the inbox, a company registry, the documents the supplier sends and the people who still have to answer. The document is one input. The work is keeping the case moving until it is done.

Automation runs a step. Floowed runs the case. That is the divide this comparison keeps coming back to.

Document AI Quality: Where Nanonets is Strong

Nanonets has been doing document AI longer than most. Their core extraction engine is mature. A few things they do well:

• General OCR strength. Across printed text, mixed layouts, multilingual content, and reasonable-quality scans, Nanonets performs at the top of the horizontal IDP pack alongside Rossum, Docsumo, and ABBYY, and it says its own OCR-3 model tops the IDP Leaderboard.
• Self-service training. An ops team can upload sample documents, label fields, and have a custom extractor running the same day. The training UX is one of the cleanest in the category.
• Pre-trained models. Out-of-the-box coverage for invoices, receipts, IDs, passports, and a long list of common business documents. For standard formats, you get value with no training at all.
• Workflow connectors. Solid integrations with QuickBooks, NetSuite, Sage, SAP, Google Drive, and the usual Zapier-adjacent ecosystem.
• Developer-friendly API. If you have engineering capacity, the API is well documented and predictable.

If you only need extraction in isolation, Nanonets earns its shortlist place. Most operations need more than extraction, and that is where the next sections go.

Reading Documents Inside the Case

Floowed reads documents too, and it reads the ones horizontal tools struggle with: phone-camera photos, photocopies of photocopies, scans, handwriting. There is no per-type training behind it: the runtime routes each document between open-weight and frontier models on cost, latency and data residency, where Nanonets builds around its own model.

The bigger difference is what happens next. Floowed doesn't treat a document as the end of the job. It cross-checks what a document claims against the rest of the case (the bank letter against the account in the ERP, the certificate against the registry, the invoice against the purchase order) and looks for tampering signals such as edited values, mismatched fonts and altered metadata. When something is missing, expired or doesn't match, the case doesn't stall in a review queue: Floowed chases whoever can fix it.

On a clean printed invoice, Nanonets and Floowed will both do fine. The comparison is decided by what the case needs after the characters are on the screen.

What Happens After Extraction: The Real Divide

This is the section that matters most, because it is where the comparison stops being about document AI at all.

Nanonets now goes past structured data: its agents follow rules parsed from your SOPs, with confidence scores deciding when to act and when to ask a human, and every human correction teaching the agent to handle more on its own. For accounts payable, orders or claims, that is a strong design for the step it covers: post the invoice, three-way match it, pay it.

Real operations rarely stay inside one step. A document arrives wrong. Two systems disagree. Someone doesn't answer. Something looks off. Floowed keeps the case open through all of it:

• Gather. Pull what the case needs from your systems and external sources over API, MCP and 400+ integrations, and take in the documents people send.
• Interpret. Read what arrived and check it against the other sources.
• Chase. Ask whoever still owes something: a person, a system or an agent. Wait, and resume when the answer comes.
• Decide. Apply your rules in a deterministic policy engine, versioned, so the same inputs on the same version give the same outcome. You can replay a rule change on past cases before you ship it.
• Act. Write the result and the reason back to every system that needs it.

Determinism is scoped to the rules. Reading a document is model work and can vary; the decision applied to the result does not. That is why every case keeps a full record of what was read, who was asked, which rule and version decided it and what was written where.

Agents do the tasks. Floowed runs the process. An agent your team already uses can be one of the executors inside a Floowed operation.

What Does Nanonets Actually Cost?

Nanonets publishes its pricing, which is rarer than it should be in this category. It starts free with $50 in credits, then $100 a month for 100 credits, with usage metered in prepaid credits that each workflow step consumes (extraction steps cost more than classification or detection steps), volume discounts reported up to 40%, and free starter credits to test with. That works well for predictable, moderate volumes and lets small teams start cheap. It works less well when every case carries several documents, because every document runs its own set of metered blocks, and every re-run bills again.

Floowed publishes its pricing too. We charge credits for the work done on each case, not seats and not models, and a credit is a cent at every size. You can start free with $80 of credits, no card and no sales call, and paid plans start from $100 a month at the same rate, with every feature and integration included. Custom set-ups are quoted; the detail is on pricing. The difference is what the credits buy: the whole case from intake to outcome, chasing included, not just the extraction.

Two practical tips when modeling cost:

• Count documents and blocks, not cases. A case with several attachments can easily run to a dozen documents, each billed per block. Metered pricing compounds fast.
• Include everything around extraction. If you are comparing Nanonets to Floowed honestly, also price in whatever you would put behind Nanonets to chase missing items, apply your rules and keep an audit trail per case. Sometimes that is another tool, sometimes engineering hours, often it is people doing it by hand.

Integrations

Nanonets covers the general business surface, and covers it well: ERP and accounting (SAP, Oracle, NetSuite, QuickBooks, Xero, Sage), storage (Drive, Dropbox, SharePoint), CRM (Salesforce, HubSpot), procurement and logistics tools, plus an MCP server, APIs and Zapier for the long tail. If your destination system is a mainstream business tool, Nanonets reaches it.

Floowed has 400+ integrations across every industry we serve, with API and MCP in both directions. The point is not to replace your systems of record. They stay where they are; Floowed reads from them, writes the result back to them and runs the work between them.

In Lending

Nanonets' financial-services agents add KYC, loan application intake and credit file assembly. For the credit decision itself, the bureau reports, scorecards and a versioned credit policy still need a home: your loan origination system, a separate decisioning platform, or custom code.

On Floowed, the same case runs to a credit decision. Floowed reads bank statements, payslips, tax documents, KYC, financials and collateral in whatever state they arrive, including handwritten passbooks and photographed statements, with cash-flow analysis such as average daily balance and DSCR built in. The policy engine runs the credit policy your credit and risk teams own as explicit conditions ("if monthly cash inflow is below 1.5x the requested installment, refer to manual review") and orchestrates your scores, whether FICO, an internal model or alternative-data engines. Bring any score: we orchestrate, we don't compete. On lending, Floowed connects to 40+ integrations across the lending stack (loan management and origination systems, credit bureaus, KYC and banking APIs). Bringing your own credit model covers how scores plug in, and the lending page covers the rest.

What Nanonets Is Built For

Nanonets is built for teams where:

• You need document AI across many document types and many business processes.
• The process is accounts payable, reconciliation, order management, supplier onboarding or claims, the use cases it leads with, and the hard part is the document step.
• Volumes are moderate and the per-block meter stays small.
• You're happy for Nanonets' forward-deployed engineers to stand the agents up over a few weeks.

Across that surface it is a serious product. The ceiling shows when the case needs more than the document: another system to check, a person to chase, a rule to apply with a record of why. The Floowed vs Ocrolus comparison covers another strong document vendor on the same dimensions.

Where Floowed Wins

Floowed is the recommendation when:

• You want the whole case run in one place, from first request through chasing to decision and write-back, rather than stitching extraction, chasing, rules and audit together yourself.
• Your cases span several systems, documents and people, and the work that eats the team's time is chasing and reconciling, not typing.
• You need to read the documents other tools choke on (scans, photos, handwriting) and cross-check them against your systems and external sources.
• The people who own the process, not engineers, should own and version the rules, and choose how much Floowed decides on its own: every case, only the routine ones, or none.
• You need a full record per case: what was read, who was asked, which rule and version decided it, and what was written where.
• You want the model choice left open. Floowed is model-agnostic, and we charge for the work done on a case, not for a seat or a model, so every model improvement makes that work cheaper.

The center of gravity is different. Nanonets is the right tool when "documents" are the noun. Floowed is the right tool when the case is the noun: it runs the work across systems and people to an outcome, and reads the documents underneath along the way.

External References

• Gartner's Intelligent Document Processing market for the horizontal IDP landscape Nanonets sits within.

FAQ: Floowed vs Nanonets

What is the main difference between Floowed and Nanonets?

Nanonets turns documents into structured data and builds agents that act on it for back-office steps. Floowed runs the whole case: it gathers from your systems and external sources, reads the documents, chases whoever still owes something, decides against your rules on a deterministic policy engine and writes the result back.

Can Floowed replace Nanonets?

For document work that sits inside a larger process, yes: Floowed reads the documents as part of running the case. Operations such as supplier onboarding, invoice approval, employee offboarding, claims intake or a credit application run on the same platform and the same policy engine.

Does Floowed use AI agents?

Yes. Floowed runs on an AI-native, multi-agent runtime, model-agnostic, routing each task between open-weight and frontier models on cost, latency and data residency. Agents do the tasks. Floowed runs the process, and your rules make the decision in a deterministic policy engine, so every outcome traces back to a rule version you approved.

How much does Nanonets cost?

Nanonets publishes pricing: a free start with $50 in credits, then $100 a month for 100 credits, metered in prepaid credits per workflow step, with volume discounts. On cases with many documents the per-step meter compounds quickly. Floowed charges credits for the work done on each case, a cent a credit at every size, and starts free with $80 of credits, no card and no sales call. Paid plans start from $100 a month, and custom set-ups are quoted.

Which is faster to deploy?

Nanonets is fast if you only need extraction: a custom model can be live in hours, and for full agent deployments Nanonets quotes weeks, with its forward-deployed engineers. Floowed is live in minutes: name the process in Slack, Microsoft Teams or the Floowed Dashboard and Floowed builds the operation. The right comparison is "Floowed running the whole case" vs "Nanonets plus whatever handles chasing, rules and audit."

What about audit?

Nanonets records every agent run, approval and data access, and streams the logs to your SIEM. Floowed records the case itself: what was read, who was asked, which rule and version decided it and what was written where. When someone asks why a case was decided the way it was, that is the record that answers.

Does Floowed work for lenders?

Yes. On a credit application the same case runs to a credit decision, with the policy engine running your credit policy and orchestrating your scores. See Floowed for lending.

Bottom Line

Nanonets is a strong horizontal document AI product, now with agents on top. If your problem is "structured data from any document, fast," or an AP step run by agents, it deserves to be on your list. Floowed is the operations platform: it runs the whole case across your systems, external sources, documents and people, chases what is missing, and a deterministic policy engine applies your rules to the result. If your problem is the work between the documents, the comparison stops being about document AI.

If you want to see Floowed run one of your own cases, talk to us. Prefer to try it yourself first? Start free with $80 of credits.