Extend vs Instabase (2026)

The External Data Validation Step calls a customer's own system mid run to check an extracted value, an outbound call inside a running process and the clearest form of tool use here. Webhooks push run outcomes into the customer's systems, with configuration options and an event catalog. A GitHub App integration is available for Workflows. UI Components let Extend's review interface be embedded in a customer's own product. The platform is reachable by REST API, SDKs and a CLI, so a customer wires it wherever needed.

There is no catalog of prebuilt business connectors, such as Salesforce, Workday, NetSuite or SharePoint. Extend is document processing infrastructure a customer's engineers integrate, not a platform pre wired to their systems, and the named counterparties are file types, not applications. The agent acts on documents and hands structured data back to the caller. The Edit API fills forms and generates documents, so it too acts on a document, not a system.

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AI Hub writes into outside systems as well as reading from them. Data connections link document repositories as sources of file input or destinations for processed output, and deploying an app connects it to upstream document sources and downstream business systems. Organizations centrally manage secrets for integrations.

Named targets include external storage on Amazon S3, Azure Blob Storage and Google Cloud Storage at Enterprise lite, with extended data source support above it. Customers can also write Python in custom function steps to add logic and integrations, with access to an LLM client, so they are not limited to a fixed connector list. Correspondence routing and the AI Hub API and SDK handle the inbound direction.

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Workflow step types include Parse Step, Validation Step, Router Step, Conditional Steps, External Data Validation Step and Conditional Extraction Step, which make this orchestration, not a pipeline. A router and conditional steps are branching primitives, so the run takes different paths depending on what the previous step found. Formulas add computation between steps. External Data Validation reaches outside mid run.

A workflow pauses to check an extracted value against the customer's own system of record before continuing, holding state across a call the platform does not control. Durability and versioning are built in. Workflow Versioning and per run inspection through Reviewing Workflow Run are available, and Async Processing and Batch Processing are separate, so a run survives beyond a request, at volume. Multifile Extraction in Workflows handles one logical document arriving as several files.

The five APIs compose instead of stacking. Parse, Extract, Split, Classify and Edit work alone or together, and Extend contrasts this with rivals where multi-step workflows require custom engineering because capabilities are bundled into a single endpoint. The participants are distinct. They are the pipeline, the Review Agent correcting low confidence extractions, the human reviewer at the confidence gate, and the customer's external system answering a validation call.

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Multi step pipelines are the core of the product. In the flow editor, customers chain classification, extraction, validation and refinement steps into a single pipeline, with native LLM powered classification and extraction and custom Python function steps that carry their own logic, integrations and LLM client.

Flows publish as advanced apps that run on demand or deploy for automated processing, and the no code app editor runs on the same engine while exposing less of the pipeline. A business logic layer validates data across documents and applies multi-step business rules, with cross-document validations and data quality checks, so state carries between steps. Deployments bind the pipeline to upstream sources, downstream systems, review queues and retention. No BPMN or named engine is published.

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Webhooks cover events, with configuration, an event catalog and best practices. Delivery is configurable, so a customer's systems are notified when a run completes or fails instead of polling. Async and batch processing are how long running document work is invoked at volume. The caller submits and is called back. A GitHub App integration under Workflows is a second invocation path, with a commit or pull request event in the customer's repository driving a document pipeline. Invocation surfaces are several but developer facing.

They are the REST API, the CLI, the MCP server, the Studio dashboard at dashboard.extend.ai, the GitHub App, and embeddable UI Components that put Extend's review interface inside a customer's own product, so a reviewer working in the customer's application reaches Extend without knowing it. There is no schedule, such as cron, a recurring run or a scheduled batch. The customer's systems invoke Extend, which does not wake itself, a coherent design for infrastructure. There is also no Slack, Teams or email channel, because the customer is an engineering team.

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Runs start without a person invoking them. Deploying an app connects it to upstream document sources and downstream business systems, with scheduled processing, human review workflows and data retention policies, and apps can run on demand or be configured for automated processing through deployments.

AI Hub can also automatically classify, index and route incoming correspondence, including emails, attachments, chats and letters, working out intent, splitting multi-document packets into individual files and sending each piece to the right workflow. Data connections attach repositories as standing sources, so a deployment watches a location instead of waiting for an upload.

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The product sits upstream of retrieval. Extend calls itself document processing infrastructure for AI agents, and its job is to convert unstructured documents into structured, LLM-ready markdown. It produces the grounding material that someone else's retrieval system consumes, and manufacturing the corpus is not the same as being grounded in one. Extend's work is per document. The five capabilities (Parsing, Extraction, Splitting, Classification, Editing) operate on the file in front of them. There is no index, semantic search surface, vector store, accumulated corpus a customer queries, or citation back to source.

Multifile Extraction handles one logical document arriving as several files, a batching concern, not a corpus. What persists is configuration, not knowledge. Extraction schemas, workflow versions, evaluation sets and processors are versioned, but they describe how to read a document, not what the organization knows. Evaluation sets are labeled ground truth for scoring, and Memory sits under Optimization. None of these retrieval pieces appears among Extend's named capabilities.

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Results are grounded per run over a document batch, with source linked references and cross document validation, but no maintained retrieval structure over the customer's corpus is described. Projects define document classes and fields, apps package them, flows chain classification, extraction, validation and refinement steps into a pipeline, and documents uploaded to projects stay in their workspaces.

Data connections attach repositories as sources of file input for a run, not as a queryable layer that persists between runs. AI Hub Analyze, which offered chat agents backed by a customer knowledge base, is no longer live, its page now redirects to the Automate page and it no longer appears in the main navigation.

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A memory feature sits under Optimization beside Composer, the background agent that refines extraction schemas across runs. Placed with the features for improving over time, it is retained state, not a session buffer. Extend does not spell out its scope, lifetime or access rules. The system appears to write the state from its own runs, since Composer is a background agent that experiments with prompt and schema variants and refines them.

User roles and permissions and per deployment isolation exist, but the scope of memory itself is not given. There is no lifetime or purge path for memory. Data handling rules cover storage, retention and deletion in general. Extend does not say whether the API, SDKs, CLI or MCP server expose the memory object. An agent written store that carries across runs goes beyond a database doing the job of memory.

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AI Hub has no memory layer, with no per user or per tenant memory scope, published lifetime, expiry or purge path, or read or write surface, and nothing carries state across turns or sessions. Workspaces persist documents, projects and deployments as the application's data model, so deleting them would delete the business record.

Data retention policies on deployments govern how long documents are kept, which is records management, not agent memory. The Hub keeps reusable apps, ground truth datasets persist for testing, and cross document validation carries state within a run. AI Hub processes document batches through a pipeline and returns results, so no continuing agent remembers anything between runs.

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The review gate spans three surfaces. Confidence Scores are the signal the gate fires on. A Review Agent uses vision language models to review and correct low confidence extractions. Reviewing Workflow Run is the human facing surface where a run is inspected before its output is accepted. Extend also offers a human in the loop review UI alongside confidence scoring and LLM-as-a-judge scoring. The gate is on the agent's own output.

An extraction below the confidence threshold does not flow downstream. It is routed to a person who inspects and corrects it. For a product whose value is that extracted data is trustworthy enough to write into a finance or healthcare system, the hold before acceptance is the product. Two validation steps, Validation Step and External Data Validation Step, are workflow primitives that extend the gate to anywhere in a pipeline.

The second validates an extraction against the customer's own system of record before accepting it, an automated check that can block a run, placed where the customer chooses. Confidence threshold routing fires on a measured signal, not a rule someone remembered to set.

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A person can be looped in to approve or correct any low confidence result, using built-in case management that compares extracted data with the full context of the document packet.

Both apps and advanced apps support human review workflows that let reviewers move between review tasks, human review queues are scoped to workspaces alongside deployments and data sources, and deployments ship with human review support. The Enterprise tier adds SLA based reviews and sophisticated human in the loop requirements. Governed release workflows add a second gate, promoting changes with built-in approvals and change tracking.

Sourceinstabase.com/product/ai-hub/automate and docs.instabase.com/overview/getting-startedread 2026-09-12

Extend's SOC 2 claim is hedged. Extend maintains controls aligned with SOC 2 Type II requirements, with reports and security documentation in its trust center. Aligned with is weaker than certified. The other two claims are unhedged, and one is contractual. Extend is HIPAA compliant and can execute Business Associate Agreements (BAAs) across all three deployments. A signable BAA is a legal instrument accepting liability as a business associate, and HIPAA has no certificate to hold.

Extend also states flatly that it is GDPR compliant, with a complaint procedure that sets acknowledgment within 3 business days and a response within 30. There are user roles and permissions, API authentication, and data handling rules covering storage, retention and deletion. Per region deployment isolation adds a data boundary control. The SOC 2 wording is the weak link. The stronger assurance comes from HIPAA with executable BAAs and GDPR compliance, backed by a trust center with reports.

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Enterprise SSO is the authentication path, and role-based controls with dedicated workspaces give each user the right data and permissions.

Group and role based access management, organization admins who assign roles to control access, multi factor authentication in account settings, centrally managed secrets for integrations, service accounts for programmatic calls and a roles reference cover access, with encryption in transit and at rest.

Instabase says its security program is designed to comply with SOC 2 Type II, GDPR, HIPAA and CCPA, which does not claim the attestations are held, and no certificate, auditor or report period is published, though a trust page at instabase.com/trust is linked.

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Reviewing Workflow Run gives a record at run level. Inspecting an individual run of a pipeline shows what the agent did, which output quality reporting alone does not. Workflow Versioning sits beside it, so a run can be read against the configuration that produced it, which is what makes a run record usable months later. Evaluation adds Publishing Processors, a promotion step, so what changed and when is itself recorded.

The quality layer adds field level and document level accuracy reports, confidence scores per extraction, and regression detection across evaluation sets. Extend runs a public status page, and webhook events notify a customer's own systems of run outcomes instead of polling. Extend does not say what a run record contains, such as step level timing, inputs, model version, retention or export.

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Every result can be traced and verified with full explainability, showing exactly where each answer came from and tracking every change with detailed record-keeping, and outputs carry audit trails and source-linked references.

The log covers what AI Hub's own processing did, which answer came from which page of which document and what changed since, not the audit trails of the customer's downstream systems. Governed release workflows add change tracking across environments, and deployment metrics give an operational view above individual results. No retention period, export format or SIEM integration is described.

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Deployments run in three named regions, us1, us2 and eu1, with deployments covered in the API reference, so the region is a parameter a customer sets. The EU region is specified to the datacenter. eu1 stores and processes customer document data within the EU, with primary infrastructure in Frankfurt on AWS eu-central-1. Naming the cloud region, not just the country, is what a procurement team bound by GDPR asks for. Extend is candid about the limit.

Operational telemetry that does not include document content, such as error and performance monitoring, may be processed by vendors outside the EU. Separating document data from telemetry and saying which leaves the region marks a real boundary. Extend also refers to deployment models beyond the three regions, and HIPAA compliant processing is available across all of them. Extend does not say whether those models include a self hosted or private cloud tier.

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Customers can run Instabase within their own virtual private cloud and keep full control, which is deployment into the customer's own infrastructure, not VPC peering into Instabase's cloud. Single tenancy is a defined deployment option, and the flow editor is limited to Enterprise, single-tenant only. External storage on Amazon S3, Azure Blob Storage and Google Cloud Storage lets processed documents rest in buckets the customer owns, and data connections serve as both sources and destinations. No region list or on premises option is published.

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Generation is the opposite of a catalog, and it is Extend's deliberate position. Composer takes a few of the customer's own sample documents, generates the extraction schema and refines it in background optimization loops. The unit a customer ends up with is built for their documents, not selected from a library, and Extend sells that as the advantage on the messy documents the product exists for. There is no browsable set of units to adopt.

The five APIs are product surfaces. Workflow steps are primitives a customer assembles, processors under Evaluation are the customer's own configurations being promoted, and UI Components are embeddable interface elements. None is a working unit a customer selects and runs on day one. No industry or document type packs appear, the most conspicuous absence given the product.

Extend serves finance, healthcare and logistics and names Brex, Square, Checkr and Flatiron Health as customers. An invoice, claims or bank statement pack would be the obvious thing to ship, and none is offered. Supported File Types is a compatibility list. There is no schema gallery, document type template library or shareable processor, and the dashboard at dashboard.extend.ai sits behind login.

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AI Hub ships pre-built apps for common use cases such as ID verification and invoice processing, which customers can use as-is or customize by modifying the underlying project. Identity documents, invoices and annual reports are named among many more. The Hub gives central access to document processing apps that Instabase built, the customer customized or the organization shared, and supports reuse of assets across AI Hub.

New users can try a pre-built app with included sample files, so adopting one takes a single step. Each app is a whole working unit, so an ID verification app and an invoice processing app do different jobs and neither depends on the other. Customers can also build their own apps in the app editor and publish them to the shared Hub.

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Extend routes across more than one model. It runs an agentic OCR pipeline with intelligent routing through vision AI and custom-trained VLMs, an ensemble of frontier models combined with proprietary context engineering, and a Review Agent that uses vision language models to check low confidence extractions. The platform runs several model classes and chooses between them per document. Customer control exists over mode and version, not provider. Multiple performance modes, tuned for low latency, bulk cost efficiency or maximum accuracy, are toggled per job.

Model versioning is a top level part of the product, so customers manage model change. Pinning a version is real control over which model runs. Control of the model itself stays with Extend. A customer can pin Extend's version and pick a speed, cost and accuracy profile, but cannot nominate a provider, bring their own model or route a step to a named external LLM. The models are Extend's own, including custom trained VLMs, and accuracy claims in the mid to high nineties rest on Extend controlling the ensemble.

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Customers can pick a model only in a mode Instabase labels legacy, and only between two of its own tiers.

In legacy mode, automation projects offer standard and advanced models, and a customer can switch to the advanced model for individual fields to balance speed, accuracy and cost, with a capability table comparing the two on search, calculations, table extraction, visualizations and visual reasoning.

The current agent mode, AI Hub's LLM-based engine used in both experiences, has no model selection. Standard and advanced are Instabase's own labels, and no third party provider is named, so customers cannot tell whose model runs or bring their own.

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Extend offers four developer tools, SDKs, a CLI, an MCP Server and UI Components. An MCP server, SDKs, a CLI and a REST API together make an unusually wide external surface. The API has full reference documentation, covering authentication, deployments for region selection, and webhooks with configuration, events and best practices, in a versioned documentation build stamped v2026-02-09. Rate limits, async processing and batch processing are each covered.

The documentation is built for machine readers as well, with an index for AI agents at /llms.txt, a page level index at any URL with /llms.txt appended, and a Markdown version of any page with .md appended. A GitHub App integration works with Workflows, and UI Components let a customer embed Extend's review interface in its own product, an extension surface few vendors offer. The MCP server exposes Extend to a customer's assistant, while the External Data Validation step reaches the other way, calling a customer's own systems mid workflow. Extend names the SDK, CLI and MCP server but does not list their tools one by one.

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The AI Hub API and SDK let customers build AI Hub into their own workflows and tooling, running apps and deployments, including advanced apps created from flows, and retrieving results. The API and SDK have their own section in the public documentation at docs.instabase.com, beside App editor, Flow editor and Admin, with a getting started path, and a root level llms.txt index for AI agents offers .md versions of each page. Outside callers drive AI Hub this way, while the upstream and downstream connections run in the other direction.

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Evaluation is part of the product, not a claim. It covers processors, publishing processors, creating and running evaluation sets, and calculating array accuracy. Evaluation sets are the core artifact. A customer assembles documents with known correct answers, runs a configuration against them and reads a score. This puts a change under test with a readable, comparable result. Publishing processors is the gate that separates this from a metrics dashboard.

A processor is a configuration and publishing is a promotion step, so a change is measured before it reaches production instead of monitored after. Workflow Versioning gives the rollback side, and a test environment gives a separate place to run against.

The results include field level and document level accuracy reports, regression detection across runs, confidence scores per extraction, and guidance on calculating array accuracy, how to score an extraction that returns a list, which is the hard case in document extraction. Extend adds LLM-as-a-judge scoring alongside automated accuracy reports. The optimization loop is controlled.

Composer, a background agent, experiments with prompt and schema variants and refines them, measured against the evaluation sets instead of left to drift. A self improving loop with scoring the customer can read is different from absorbed learning.

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Releases pass through accuracy benchmarks before going live. Customers run accuracy tests and get detailed comparisons so only trusted configurations reach production, and they manage and update ground truth datasets as real-world samples change. The tests read what the agent produced, not just whether a change installed.

Governed release workflows let teams configure, test and promote changes across logically separated environments with built-in approvals and change tracking, with workspaces set aside for development and production. For model changes, AI releases can be versioned and locked for defined periods, new models tested in controlled environments and then rolled out on the customer's own schedule.

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Every path in is an API call, an SDK, the CLI, the MCP server, a webhook or an upload, and every path out is structured data, markdown or a filled document returned to the caller. There is no interface for an agent to drive because the input is a file, so how often it breaks when interface elements change is not a question that applies. There is no hosted or local browser, desktop session, remote computer control, RPA, recorder or extension. The Dev Tools section's UI Components are interface elements Extend ships for a customer to embed, the reverse relationship. Two adjacent items fall short of computer use.

The Editing capability, with Detect Form, detects fields in a document and fills them. This is form field detection inside a file, not an agent operating a form in a browser. Vision language models and an agentic OCR pipeline read the pixels of a scanned page, which is ingestion, not computer use. A model that can read a degraded scan is not a model that can click a button. Extend is infrastructure other products build on, integrated by API precisely so that no screen is involved.

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No hosted or local browser, desktop session, or remote or local computer control is described. AI Hub works on documents delivered to it, whether uploaded, pulled from connected repositories or arriving as routed correspondence, and returns structured results to the systems it writes into through connectors. Custom Python function steps in the flow editor run code inside the platform's own runtime, and visual reasoning, where the advanced model reads images, diagrams, watermarks, layout, colors, text styling and handwritten markup, analyzes pixels in a document without driving a screen.

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