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Google Cloud Software / SaaS Singapore

cynapse.ai

Real-time Analytics · Security and Compliance

When cynapse.ai was founded, its solutions needed to be deployed on-premises, but as customers, including large enterprises and government agencies, began requesting more flexible cloud-based options, the team had a vision to make that a reality.

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Reported impact

Business outcome

Consolidate AI and product R&D workloads into a single environment; Launched new cloud-based solutions to meet rising customer demand Empowers sales teams with up-to-date demo environments; Improved access to training, testing, and fine-tuning VLMs.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

cynapse.ai cynapse.ai powers cloud-based solutions and future-proofs its agentic video AI research with Compute Engine. When cynapse.ai was founded, its solutions needed to be deployed on-premises, but as customers, including large enterprises and government agencies, began requesting more flexible cloud-based options, the team had a vision to make that a reality. Making the change required finding centralized GPU resources to avoid fragmenting compute across multiple regions. “GPU Hardware availability made it challenging to stand up cloud-based services in a few regions, so we needed an alternative that offered scalable, flexible deployments and would work well with our existing infrastructure. Having the Gemini Enterprise Agent Platform included was a strong added benefit,” shares Illy Gruber, CMO and Product Head at cynapse.ai.

Solution architecture

4 components · 4 layers
  1. Compute
    • Compute Engine Runs serverless or virtualized application services with elastic scale for production AI workloads.
  2. Storage
    • Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
  3. Serving
    • Cloud Load Balancing Routes highly variable enterprise campaign traffic into the ML platform.
  4. Orchestration
    • Gemini Enterprise Agent Platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

  • Cloud Load Balancing
  • Cloud Storage
  • Compute Engine
  • GPU Hardware availability made it challenging to stand up cloud-based services in a few regions, so we needed an alternative that offered scalable, flexible deployments and would work well with our existing infrastructure. Having the Gemini
  • When cynapse.ai was founded, its solutions needed to be deployed on-premises, but as customers, including large enterprises and government agencies, began requesting more flexible cloud-based options, the team had a vision to make that a re
  • cynapse.ai cynapse.ai powers cloud-based solutions and future-proofs its agentic video AI research with Compute Engine.

Evidence from the source

Consolidate AI and product R&D workloads into a single environment
GPU Hardware availability made it challenging to stand up cloud-based services in a few regions, so we needed an alternative that offered scalable, flexible deployments and would work well with our existing infrastructure. Having the Gemini
Improved access to training, testing, and fine-tuning VLMs
Launched new cloud-based solutions to meet rising customer demand Empowers sales teams with up-to-date demo environments
When cynapse.ai was founded, its solutions needed to be deployed on-premises, but as customers, including large enterprises and government agencies, began requesting more flexible cloud-based options, the team had a vision to make that a re
cynapse.ai cynapse.ai powers cloud-based solutions and future-proofs its agentic video AI research with Compute Engine.
Transparency Evidence & methodology 65/100 reference value · 75% 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 value65/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 confidence75%
  • 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
    6 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity6/10
    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. Google Cloud

    cynapse.ai customer story

    First-party
    https://cloud.google.com/customers/cynapse
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Sitemap last modified Aug 31, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Aug 31, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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