Nanonets vs Parashift (2026)

The integration catalog spans nine categories and dozens of systems, including ERP and finance (SAP, Oracle, NetSuite, Dynamics 365, Workday, QuickBooks, Xero, Sage, Zoho, Odoo), procurement and contracts (Ariba, Coupa, Jaggaer, DocuSign, Ironclad, Icertis), order and revenue (Salesforce, HubSpot, Shopify, Stripe, PayPal, Adyen), supply chain (FedEx, UPS, DHL, Maersk, Flexport, project44), healthcare RCM (Epic, Oracle Health, Availity, Waystar, Optum), storage, support and data platforms.

Agents act as well as read. They post decisions into systems of record, create sales orders, block a supplier whose bank details do not match the W-9, and adjudicate and pay claims.

SourceNanonets, nanonets.comread 2026-09-07

More than thirty prebuilt connectors reach ERP, DMS, CRM and workflow systems, including SAP, Salesforce, SharePoint, M-Files, JobRouter, d.velop, Lobster, BSI and Octo by TCG Process, and they span RPA, iPaaS, ERP, DMS, CRM, email inboxes and the customer's own applications with no middleware needed. Invoices are matched two and three way against purchase orders and order confirmations and released straight for posting in the customer's ERP, and cases export automatically to the target system at the end of the pipeline.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Agents run multi step workflows without a person in the loop by default. An agent reads incoming documents, applies the rules held in the context graph, decides what to do, escalates when uncertain and writes structured results into the systems of record, and agents are composed from a shared set of building blocks. Several agents take part in a single run with distinct scopes, such as an AP clerk agent, a regional manager agent and a business analyst agent in one freight audit run, and the claims example runs parse, rule evaluation, adjudication and payment in sequence.

SourceNanonets, nanonets.com and docs.nanonets.comread 2026-09-07

Documents move through a multistep pipeline from intake to a completed case.

Intake runs by email, scan, upload or API, multipage batches are split into individual documents automatically, each is classified against a catalog of 300 or more types, fields are extracted with a confidence score each, and case management follows with validation status, an audit trail and automated export to the target system.

Agents orchestrate parts of the pipeline within guardrails and escalate when a threshold is not met, and the two and three way matching module reconciles an invoice against purchase orders and order confirmations before releasing it for posting.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Inbound email, including a named order intake mailbox, brings work to the agents, along with EDI transmissions, scans and faxes, PDF uploads, bank feeds and connected storage in Google Drive, SharePoint, OneDrive, Dropbox and Box, and reviewers are engaged over Slack, Teams or email with notifications on exceptions. Incoming documents and messages form an inbound queue, and feeds from ERP, payment and logistics platforms start work on events.

SourceNanonets, nanonets.com and nanonets.com/agentsread 2026-09-07

Documents arrive by email, scan, upload and API, forming an inbound queue across four channels. An inbox is monitored, boundaries within it are detected automatically, and each document is routed to the matching workflow on arrival. Downstream, confidence thresholds and validation rules fire escalation events that route a case for review. No broader event trigger system is described.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Trail, Nanonets' context graph, is built from the customer's own knowledge. Policy documents, SOPs and runbooks are imported and parsed into a machine readable graph where every constraint is a node and every dependency an edge, holding explicit, conditional, negative, precedence and implicit rules, each traceable to the source page it came from.

It also absorbs tribal knowledge from Slack, Zoom, Teams and email as exception nodes and logs past decisions as precedents that later cases are matched against, with a case match score. The graph persists, is shared across every agent that needs it, picks the right rule for each situation, cites its sources and records which rule drove which decision.

SourceNanonets, nanonets.com and docs.nanonets.comread 2026-09-07

A class catalog of 300 or more document types, each with its own extraction schema, is maintained for the customer, models are retrained directly from the customer's corrections in live operation through OneTouchLearning, and matching runs against the customer's own purchase orders and order confirmations, all of which persist and shape later runs. No index, graph or embeddings layer holds the customer's policies or reference material for agents to query, and the extraction schemas are configuration for reading documents, not a knowledge layer.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Two forms of persistence go beyond session state. Precedent memory logs every past decision with its full context and cites it as precedent, matching new cases against it with a similarity score, so a decision made once shapes later ones.

Learned instruction state updates the agent's rulebook with each human correction, shown as a day 1 against day 30 rulebook where an exception the agent once escalated now clears.

Neither is presented as a memory layer with a stated scope and lifetime, nothing says where it lives or how it can be deleted for one customer, and the rules themselves live in the context graph.

SourceNanonets, nanonets.comread 2026-09-07

Parashift keeps learning, not memory. OneTouchLearning retrains models from corrections during live operation, and DocumentSwarmLearning pools model learning across customer environments, so the result lives in model weights. No memory with a stated scope and lifetime, no session or case state carried between runs, and no way to delete such memory for one customer is described.

SourceParashift, parashift.ai/en/platform and parashift.ai/en/about-parashiftread 2026-09-07

Edge cases route to Slack, Teams or email for review, and confidence scores decide when an agent acts on its own and when it asks a person, as in an example where an agent pauses at 61 percent confidence to ask a named reviewer which product code to use.

Escalation thresholds come from the customer's own policy and are encoded in the context graph, with the claims example routing by value to one or two reviewers with SLAs, and proposed self corrections go to a person to approve, review or reject before a rule changes. Every extracted rule is approved, edited or rejected by a person before agents run on it.

SourceNanonets, nanonets.comread 2026-09-07

Review is built into the platform in two parts. With human in the loop, cases below a set confidence threshold or showing anomalies go automatically to an embedded validation interface where a person makes the final call, with no frontend development needed, and the interface can be embedded in third party applications under an SLA.

With agent in the loop, agents run parts of processing only within predefined guardrails, and confidence thresholds, validation rules and approval workflows decide when an agent may continue on its own and when a case is escalated. Every extraction carries a confidence score against a threshold the customer sets, and the same guardrails apply whichever model is running.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Nanonets holds SOC 2 Type II certification, ISO 27001 and GDPR, signs a HIPAA business associate agreement on enterprise plans and is independently audited every year. Enterprise controls cover SAML, OIDC and SCIM user provisioning with Okta, Azure AD and Google Workspace, full RBAC and fine grained access control across organizations, workspaces and agents, IP allow listing, AES-256 at rest and TLS 1.3 in transit with customer managed keys, and per team budgets and API quotas. The agent run view shows scoped, read only and configure level access for individual agents, backed by audit logs.

SourceNanonets, nanonets.comread 2026-09-07

Parashift holds ISO 27001, SOC 2, BSI C5 and PCI DSS and publishes a Compliance Trustcenter at trust.parashift.io. Customer control comes from multitenancy, with centrally defined configurations that individual tenants inherit, dedicated tenants, end to end encryption in transfer and storage, and confidence thresholds and approval workflows the customer sets per agent. No single sign on, SAML, SCIM or named role model is shown, so access control rests on tenancy and configuration, not an identity integration.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Every agent run is traceable, auditable and explainable, and a run shows a named run identifier, the invoice routed, the purchase order and load it matched, which rules fired and which did not, and which other agents took part with their access scope. Every agent run, approval and data access is recorded and streams to the customer's own SIEM by webhook, every agent decision traces to the document it read and the rule it applied, and each extracted field links back to its source for side by side citation. That record, kept by Nanonets itself, shows why an agent acted, not only that it did.

SourceNanonets, nanonets.com and docs.nanonets.comread 2026-09-07

A complete audit trail records model decisions, confidence scores and human interventions across the whole pipeline, with traceability of every automated decision built in. Parashift presents end to end governance and an audit trail per confidence score and decision as what sets it apart from a classic IDP platform. The pipeline ends in a complete auditable case with validation status and audit trail, and every field carries its own confidence score whichever model captured it, so the record shows why a decision was made and by which model.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Customers can deploy privately in a VPC, in a single tenant cloud or on premises, on their own infrastructure and network policies, and data residency pins processing to US, EU or APAC regions, so customer data never leaves the customer's boundary. Bring your own key covers customer managed encryption keys.

SourceNanonets, nanonets.comread 2026-09-07

Customers choose where their data is processed among three compliance zones, Switzerland in Swiss data centers, the EU in EU data centers, and Germany in a BSI C5 operating environment in German data centers. Document data never leaves the selected zone, and the platform can run so data is processed only in the chosen country. Separately, the Dokumentli model and its Node can run fully in the customer's own data center, including air gapped deployment, which covers the model, not the whole platform.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Customers pick and run pre built agents, including invoice and purchase order matching, bank statement and expense matching and contract analysis, and running one means picking a workflow and dropping in a document. Pre built agents are offered as the alternative to pointing a customer's own agents at the platform, and solution pages ship named agents for accounts payable, order management, logistics and healthcare. Each does its own job when selected.

SourceNanonets, nanonets.com/agents and nanonets.comread 2026-09-07

A catalog of 300 or more document types ships out of the box, each with its own extraction schema, and customers adopt them, customize existing templates or build from scratch. The platform also ships named modules, the Matching Hub for purchase to pay two and three way matching, the embedded validation interface, automation agents and the Flow integration component. Each class and module does its own job when selected, and the rest stay whole when one is left out.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Nanonets runs its own proprietary extraction model, Nanonets OCR-3, and publishes open source models on Hugging Face, and no customer or admin selection of the model behind its agents is described. The Claude, ChatGPT, Copilot, Gemini, DeepSeek, Mistral and Hugging Face logos shown under connect your agent platform are outside assistants that call Nanonets through its MCP server or API, not a choice of the model behind its agents.

SourceNanonets, nanonets.comread 2026-09-07

Customers choose the model, and Parashift presents this as its main differentiator. Under a bring your own model principle, the platform connects OpenAI API compatible models, including GPT, Claude, Gemini and Mistral, or Parashift's own Dokumentli Vision Language Model, and any LLM a customer wants can be connected.

The model can be swapped by use case, price or model generation without rebuilding the guardrail and automation logic, and a specialized and a general purpose model can run together on the platform, with guardrails, audit trail and human in the loop applying whichever model runs. Parashift contrasts this open approach with the hard wired engine of a classic IDP platform. Its own proprietary model approaches are technologies it uses, not choices the customer makes.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Customers can point the agents their teams already run at Nanonets through an MCP server, native retrievers, or a plain REST or GraphQL API, with Claude, ChatGPT, Copilot, Bedrock, Vertex AI, SAP Joule, Salesforce Agentforce, LangChain and LlamaIndex named as callers. The public documentation at docs.nanonets.com has an llms.txt index of every page, endpoints in OpenAPI form and markdown for any page, and open source models are published on Hugging Face.

SourceNanonets, nanonets.com and docs.nanonets.comread 2026-09-07

Parashift publishes an open API through which every platform function is available, API first and platform independent, with documentation at docs.parashift.io. The validation interface can be embedded as a standalone component in a third party application, and software vendors can embed Document AI in their own products under OEM licensing.

SourceParashift, parashift.ai/en/platform and docs.parashift.ioread 2026-09-07

After deployment, an optimization loop works on the agents themselves.

Nanonets finds conflicts and undetected issues in the encoded rules, diagnoses them and proposes a fix for approval, for example noticing that Net-60 terms were granted twenty times by hand this quarter and recommending the rule change, and every human correction on an exception updates the instructions so autonomy rises with runs.

No harness, scored test cases or gate before deployment is described, and no change is compared against a control, so the loop has no measured result. Auto-labeled samples and model retraining improve extraction accuracy but do not evaluate the agent.

SourceNanonets, nanonets.comread 2026-09-07

Every extraction passes a live quality gate. Each field carries a confidence score against a threshold the customer sets, results above it continue automatically and everything below goes for review, whichever model captured the data, and corrections retrain the model in live operation through OneTouchLearning. The gate checks each output, not a change under test. Customers can swap models, but there is no way to compare one model against another on their own documents, and no evaluation set, scored test cases, release path or holdout is described.

SourceParashift, parashift.ai/en/platformread 2026-09-07

Agents reach outside systems through native integrations, retrievers and REST or GraphQL calls, and Nanonets delivers clean data to systems of record, so actions run through programmatic connections, not an interface an agent operates. Documents arriving as email attachments, EDI, scans, faxes and uploads are parsed, which is ingestion, not driving a screen. No browser, desktop or remote computer control is described.

SourceNanonets, nanonets.comread 2026-09-07

The platform reaches outside systems through an open API and more than thirty prebuilt connectors and takes documents in by email, scan, upload and API, all programmatic routes. Separating, classifying and extracting pages means reading files at build time and run time, not driving a screen, and no browser, desktop or remote computer control is described.

SourceParashift, parashift.ai/en/platformread 2026-09-07