IBM Software / SaaS United States

Seismic

Document Processing · Enterprise Knowledge Search

Seismic needed an enterprise-grade generative AI platform for meeting summarization and content recommendations because early prompt and model experiments were not keeping pace with the sophistication and scale of the product it wanted to build.

Reported impact

Business outcome

Seismic improved summarization capabilities, strengthened recommendation features, and established a foundation for broader AI-powered search and data experiences.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Seismic worked with IBM's Ecosystem Engineering Build Lab team to create a minimum viable product that improved how meeting transcripts are processed, summarized, and turned into recommendations inside the Seismic platform. IBM watsonx.ai supplied the generative AI capabilities used to chunk and analyze transcript files, enabling faster prototyping and more seamless embedding of AI into the existing product. The build team also helped Seismic write Terraform code so the enhanced summarization service could be deployed more consistently, and ran knowledge-transfer sessions so Seismic engineers could sustain future projects themselves. The resulting implementation linked transcript processing to the collaboration tools customers already use, including Zoom, Microsoft Teams, and Webex, which made the feature operationally useful rather than a stand-alone demo. Seismic therefore upgraded a promising GenAI feature into a more scalable, production-oriented content-processing service.

Data flow

Meeting transcript files are chunked and processed by watsonx.ai, the generated summaries and recommendations are embedded back into Seismic's platform, and the service integrates with collaboration tools such as Zoom, Teams, and Webex.

Solution architecture

3 components · 2 layers
  1. Compute
    • IBM watsonx.ai Processes meeting transcripts to generate summaries and improve recommendation quality inside the Seismic platform.
  2. Orchestration
    • IBM Build Lab Worked with Seismic to prototype the MVP and transfer implementation knowledge to Seismic developers.
    • Terraform Provides the infrastructure-as-code approach IBM helped Seismic use for smoother deployment of the enhanced service.

Architecture clues

  • IBM assisted with Terraform code so the service could be deployed more consistently.
  • The MVP improved transcript chunking and processing rather than just prompt design.
  • The same partnership is being extended into enhanced search and natural-language-to-SQL proof-of-concept work.

Evidence from the source

"The MVP, powered by IBM watsonx.ai, unlocked enhanced functionalities and performance improvements"
"streamlined the chunking and processing of meeting transcript files"
"supported Seismic needs in crafting effective Terraform code for seamless infrastructure deployment"
Transparency Evidence & methodology 75/100 reference value · 82% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence82%
  • 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 evidence15/20
    3 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 specificity8/10
    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

    Seismic customer story

    First-party
    https://www.ibm.com/case-studies/seismic
    Source type
    Vendor-published customer story
    Published
    Aug 1, 2024
    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.