Google Cloud Software / SaaS United States

Aible

Enterprise Knowledge Search · Real-time Analytics

Aible needed to deploy secure enterprise AI agents quickly while keeping customer data inside each enterprise cloud boundary, avoiding idle GPU cost, and proving that large-scale analytics and model orchestration could be both fast and economical.

1000x analytics cost efficiency gain
25% performance boost for multi-step agent workflows
a mere $10
processed 5 billion rows of data and evaluated 10 million variable combinations in less than 10 minutes

Reported impact

Business outcome

Aible shortened time to value for enterprise AI agents, cut analytics cost dramatically, and showed that governed, data-in-place agent workflows can scale without forcing customers to surrender data control.

  • Cost Reduction
  • Scalability
  • Speed to Market

Solution

Aible built its enterprise agent platform on Google Cloud by combining serverless analytics with scalable model infrastructure. BigQuery is the core data plane, allowing Aible to evaluate millions of questions across billions of rows without moving data outside the customer's cloud boundary. Cloud Run and Google Kubernetes Engine support the application and agent runtime, while G4 and newer NVIDIA-backed GPU configurations provide the model capacity needed for long-running planning tasks and rapid deployment of fine-tuned variants. This stack lets Aible stand up trusted agents in as little as 30 days, migrate key workloads quickly, and run many model variants per GPU without paying for idle capacity. By pairing BigQuery's serverless economics with GPU-backed agent execution, Aible created a platform where enterprise customers can keep control of their data while still getting large-scale generative AI assistance tied directly to business KPIs.

Data flow

Enterprise data stays in BigQuery, where Aible evaluates questions and variable combinations at large scale. Agent services on Cloud Run and GKE call that data plane, and GPU-backed model runtimes handle planning and inference before returning KPI-aligned recommendations to users.

Solution architecture

4 components · 3 layers
  1. Compute
    • Cloud Run Hosts agent services and supports rapid deployment of Aible's application workloads.
    • G4 GPUs Provide the accelerated compute needed for fine-tuned models and long-running planning tasks.
  2. Storage
    • BigQuery Keeps enterprise data in place and powers the large-scale analytics workloads that feed Aible's AI agents.
  3. Orchestration
    • Google Kubernetes Engine (GKE) Runs scalable application and model-serving infrastructure for longer-running agent workflows.

Architecture clues

  • Aible migrated to Cloud Run and G4 VMs in less than 2 hours
  • Enterprise data remains stored inside Google BigQuery
  • Long-running planning tasks can run on Google Cloud with GPU-backed models
  • The platform is designed to keep data under the customer's own control

Evidence from the source

Aible delivers secure, scalable AI impact within 30 days.
Aible successfully processed 5 billion rows of data and evaluated 10 million variable combinations in less than 10 minutes.
By using Google Cloud’s serverless architecture with NVIDIA Blackwell GPUs, we demonstrated a 1000x improvement in analytics efficiency.
enterprise data remains safely stored inside Google BigQuery
running millions of automated, AI-initiated queries costs a mere $10.
Transparency Evidence & methodology 95/100 reference value · 82% confidence · 2 sources

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 value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 confidence82%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency9/10
    2 source references 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. Google Cloud

    Aible customer story

    First-party
    https://cloud.google.com/customers/aible
    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
  2. GetLatka

    Aible Revenue 2025: $5.1M Est.

    Independent secondary
    https://getlatka.com/companies/aible.com

    The company profile says Aible has 46 employees (July 2025, LinkedIn).

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

  • First archived Jul 31, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 27, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.