The Forrester Wave™: Document Mining And Analytics Platforms, Q2 2026

Document Mining And Analytics Platforms Vendor Product Comparisons 2026

Key features and pros and cons of top Document Mining And Analytics Platforms products:

Hyperscience - Leaders

Pros:

Enterprise-grade agentic AI integration and functionality with agents that generate and execute observable, auditable, and reversible plans
Stateless, horizontally scalable orchestration with backward-compatible connectors and built-in monitoring, logging, and versioning
Strong scores in ML-based AI, model lifecycle management, hybrid RAG and guardrails, document intelligence including separation, deployment, and data governance
FinOps-aware model switching and schema-validated outputs for regulated and air-gapped environments
Responsive team during implementation and ongoing support
Customer Favorite designation

Cons:

Does not publish pricing publicly
More narrowly focused on IDP than suite-oriented vendors
Below par on pricing transparency and supporting products and services

UiPath - Leaders

Pros:

Strong vision, innovation, and roadmap supported by sustained R&D investment and relevant M&A
Large global network of systems integrator and technology partners
WorkFusion acquisition extends strategy with specialized AI agents for AML, KYC, and fraud investigation in banking and financial services
High marks for agentic AI operations and architecture, agentic AI functionality, and agentic AI integration
Governed agent execution with user-reviewable plans and OTEL/OTLP-compliant tracing and auditability
Strong AI model orchestration with hybrid pipelines routing work based on confidence/thresholds with human-in-the-loop
Robust composite-document splitting and validation feedback loops
Strong business value and reliable, scalable platform
Document privacy/sovereignty and quality of customer service

Cons:

Trails peers in document quality and validation rules
Limited out-of-the-box capabilities for LLM-driven validation or rule generation
Lacks established masking engine with override tuning
Below par on data masking and platform breadth
Most customers still in proof of concept stages for agentic AI capabilities

Automation Anywhere - Strong Performers

Pros:

Strong vision positioning IDP as foundational layer within broader AI and automation platform
Significant R&D investments in IDP
Acquired Aisera in November 2025 to enhance AI-driven automation capabilities
Top marks for agentic AI operations and architecture, genAI architecture, and NLP
Agents generate user-reviewable execution plans with strong observability and auditability including SIEM-exportable logs and rollback
Model-agnostic genAI architecture with governed orchestration, dynamic LLM selection, and FinOps controls
Advanced NLP capabilities including document clustering with generated summaries and joint entity-relation extraction
Conversational access to document repositories
Platform stability and reliability
Works well for both business users and IT professionals

Cons:

Lacks publicly available pricing
Scores on par for agentic AI functionality and genAI functionality
Below par for UI and other techniques for accuracy verification
Below par for document quality/validation rules

Hyland - Strong Performers

Pros:

Credible strategy aligned with customer needs and supported by planned product enhancements
Broad network of global systems integrators and technology partners
Native content repository integration
Excels in knowledge-based AI with pretrained language models across multiple document types
Strong domain and industry specialization and platform breadth
Ontology-driven approach with strong input/output guardrails including grounding checks and policy filtering
Packaged vertical and horizontal solutions spanning IDP, knowledge discovery, and content-centric workflows
Strong business value and customer service
Approachable platform for business users
Out-of-the-box integration with EMR platforms such as Epic

Cons:

Trails peers in agentic AI functionality and integration
Below par in ML-based AI, genAI architecture, document separation, deployment options, and globalization
Comparatively thin out-of-the-box capabilities in several areas
One reference customer indicated achieving time to value required more effort than initially anticipated

Iron Mountain - Strong Performers

Pros:

Top scorer for agentic AI functionality, document labeling, accuracy verification, document separation in batch, validation rules, globalization, data privacy, and platform breadth
Pairing automation with AI agents and LLM-driven rule generation
Strong HITL/maker-checker controls including double-blind review and double keying
Low-code agent builder with execution plan review, auditability and rollback
Model context protocol (MCP) support
Monitoring for integrations
Transparent publicly available pricing via online marketplaces
Responsive account support in MSP engagements
User-friendly experience and cost savings

Cons:

Limited global systems integrator (GSI) relationships within MSP-heavy delivery model
Should formalize and expand partner program, especially with GSIs as it shifts toward more customer self-service deployments
Scores on par for agentic AI operations/architecture and integration
Provided only one reference customer for this evaluation

Rossum - Strong Performers

Pros:

Clear and credible strategy centered on supply chain paperwork automation
Proprietary transactional LLM (Aurora) pretrained for accurate processing of supply chain documents
High R&D investment posture
Roadmap expands role-specific cognitive agents for higher-autonomy automation bridging transactional documents into ERP systems
High marks for UI and other techniques for accuracy verification, document quality, and validation rules
LLM-driven rule generation (Rules & Actions)
Broad language and UI localization coverage to support global transactional workflows
Strong business value and fast time to value
Scalability and reliability
Usability for both business and technical users
Responsive vendor support

Cons:

Below par for agentic AI ops/architecture, knowledge/rules-based AI, vectorization and hybrid RAG, long-document mining/analytics, NLP, and data masking
Absence of broader configurable genAI features beyond transactional capture
Limited packaged use-case coverage beyond core transactional focus
Primary focus on supply chain documents may not suit broader use cases

EdgeVerve - Contenders

Pros:

Good alignment with market needs
Strong rating for supporting services benefiting from breadth and depth of Infosys capabilities
High marks for genAI and AI model orchestration including cost-aware routing
Strong long-document mining especially for legal documents
Prompt- and schema-based checks
Presidio-based data masking
Scores on par for ML-based AI, genAI architecture, hybrid RAG, and genAI guardrails
Customers generally positive about the platform

Cons:

Below par vision scores
Opportunities to strengthen partner ecosystem beyond Infosys as primary global services partner
Need to enhance pricing transparency
Lacks out-of-the-box differentiation such as more vendor-provided models and mature guardrail patterns
Lags in agentic AI ops and functionality partially due to limited evidence of reversible agent execution
Below par in model lifecycle management, pretrained LMs across document types, document separation triggers, and NLP
Many customers report difficulty separating value of EdgeVerve product from Infosys professional services
Customers report not having tried platform's agentic AI features yet

OpenText - Contenders

Pros:

High marks for supporting products enabling customers to adopt DMAP as part of broader information lifecycle, automation, and AI programs
Above par for knowledge and rules-based AI, NLP, business domain and industry specialization, deployment options, and platform breadth
Support for regulated use cases including sensitive-data detection via curated grammars
Platform scalability
Generally positive support experiences

Cons:

Below par on vision, innovation, and roadmap
Ongoing need to rebuild go-to-market approach including sales and marketing to take DMAP offering to broader market
Below par for agentic AI ops/architecture and integration, genAI architecture, and model orchestration
Did not clearly demonstrate stronger out-of-the-box IDP orchestration patterns such as confidence-threshold routing (currently only available in Knowledge Discovery)
Trails peers in pretrained LMs across multiple document types, document quality rules, and data privacy
One customer reference expressed reservation about product not being popular with end users

FAQs

Q: Which vendors are Leaders in 2026?

A: Hyperscience, UiPath

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders demonstrate strong current offerings and robust strategies. In the Document Mining and Analytics Platforms market, Leaders excel across multiple dimensions including agentic AI capabilities, platform functionality, innovation, vision, and roadmap. They typically have strong partner ecosystems, sustained R&D investment, and proven track records of delivering business value to customers. Leaders in this evaluation show particular strength in areas such as governed agent execution, model orchestration, document intelligence, and enterprise-grade architecture suitable for complex, regulated use cases.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for Document Mining And Analytics Platforms in 2026

Document Mining And Analytics Platforms Vendor Product Comparisons 2026

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FAQs

Q: Which vendors are Leaders in 2026?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

Reference