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IBM Commercial and Professional Services Global

Tech Mahindra

Document Processing · Enterprise Knowledge Search

Tech Mahindra needed a way to extract value from long, complex, unstructured enterprise documents while also proving that generative-AI outputs could be governed, explained, and trusted in regulated customer environments.

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30% productivity improvement
70% faster document processing
GBP 500,000+ annual cost savings from optimized document workflows
reducing manual data extraction up to 50%–70%

Reported impact

Business outcome

Tech Mahindra used the platform to help customers process documents faster, lower workflow costs, improve analyst productivity, and adopt AI with stronger governance and compliance controls.

  • Compliance
  • Cost Reduction
  • Productivity Improvement

Solution

Tech Mahindra built its Document AmplifAler platform with IBM to convert document-heavy workflows into governed AI processes. IBM watsonx.ai provides the core AI studio, IBM Granite 13b foundation models and IBM Deep Search supply the multimodel reasoning and retrieval capabilities, and IBM watsonx.governance is embedded to keep every AI-driven process transparent, explainable, and compliant. The platform was designed to interpret documents that mix narrative text, large tables, and images across use cases such as financial reports, contracts, onboarding forms, invoices, and publishing dockets. By combining multi-agent, multimodel extraction and summarization with embedded governance, Document AmplifAler turns unstructured files into searchable intelligence and downstream workflow inputs. Tech Mahindra also used the joint program to create an AI center of excellence and train practitioners so customers can scale adoption beyond single pilots.

Data flow

Financial reports, contracts, onboarding forms, and other large documents enter Document AmplifAler, where watsonx.ai orchestrates Granite-powered, multi-agent analysis and Deep Search retrieval to extract and summarize insights, while watsonx.governance tracks outputs so customers can use the results in downstream legal, onboarding, telecom, and publishing workflows.

Solution architecture

4 components · 3 layers
  1. Compute
    • IBM watsonx.ai Provides the AI studio where Document AmplifAler's document-understanding workflows are built and run.
    • IBM Granite 13b Powers the foundation-model reasoning used to analyze complex document structures.
  2. Serving
    • IBM Deep Search Helps the platform extract and enrich insights from large documents that combine text, tables, and images.
  3. Governance
    • IBM watsonx.governance Adds explainability, transparency, and compliance controls to every AI-driven document workflow.

Architecture clues

  • The joint program established an AI center of excellence for continued rollout.
  • The platform uses multi-agent, multimodel AI rather than a single extraction model.
  • watsonx.governance is embedded at the core of the platform.

Evidence from the source

70% faster document processing
Built with IBM watsonx.ai AI studio, IBM Granite 13b foundation models and IBM Deep Search, Document AmplifAler uses multi-agent, multimodel AI to extract, analyze and enrich insights
GBP 500,000+ annual cost savings
With the IBM watsonx.governance toolkit for AI governance embedded at the core, Document AmplifAler helps ensure every AI-driven process is transparent, explainable and compliant
reducing manual data extraction up to 50%–70%
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. IBM

    Tech Mahindra customer story

    First-party
    https://www.ibm.com/case-studies/tech-mahindra
    Source type
    Vendor-published customer story
    Published
    Dec 10, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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