SAGA — Success Stories | AGORA Intelligence

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Real companies that built something and can show the numbers behind it.

  1. 1d ago

    SAGA — Success Stories

    Real companies that built something and can show the numbers behind it.

  2. 1d ago

    Solo-Founder AI Startup: 210,000 Tenders, 24 Milliseconds

    In September 2026 Google Cloud documented the infrastructure behind Lucius AI, a tender intelligence platform covering more than 210,000 public tenders and run by a single founder. Consolidating the relational catalogue, audit logs and embeddings into AlloyDB for PostgreSQL, together with moving semantic search onto a ScaNN index, brought the latency of a representative production query from 1.14 seconds to 24 milliseconds. An agent connected through the Model Context Protocol handles query analysis, data freshness checks and incident reconstruction under least-privilege permissions. The lever

  3. 2d ago

    Condé Nast cut video search from 250 minutes to under 2

    Condé Nast editorial teams spent an average of 250 minutes on every content discovery task across an archive of more than 140,000 videos. With a solution built together with the AWS Generative AI Innovation Center on Amazon Bedrock and Amazon OpenSearch Service, using TwelveLabs Marengo multimodal embeddings, the same task now closes in under 2 minutes. The real lever is the decision to search for intent instead of strings, with two separate planes for ingestion and response. The figure comes from the vendor that built the system: it counts as an announced result, pending independent verificat

  4. 3d ago

    AI Agents in the Enterprise: AI21 Cuts Training Job Waits from 72 to 12 Hours

    On 3 October 2026 AI21 Labs published the before and after of its training environment: the wait for high-priority jobs fell from 72 hours to 12 hours, and manual scheduling interventions went from twenty a week to zero. The lever is pooling the entire GPU fleet on a shared Google Kubernetes Engine cluster, with a priority policy replacing negotiation on Slack. The numbers remain a first-hand statement, published on the cloud provider's blog.

  5. 5d ago

    Takeda: How AI Reshaped Drug Discovery R&D

    The MIT Sloan Management Review case of 1 October 2026 tells the story of Takeda Pharmaceutical: AI inside the research and development workflows, scientists moved from broad experimentation to data-driven prediction with targeted validation, and four conditions (three of them organisational) underpinning scale. The mandate from then-chief executive Christophe Weber called for rethinking 80% of processes by 2028. The before/after figures on discovery productivity stay outside the published text.

  6. 6d ago

    Scribd Classified 400 Million Documents With Gemini

    On 25 September 2026 Google Cloud published the numbers behind Scribd, Inc.: more than 400 million user-uploaded documents and over 12 billion pages classified for trust and safety with Gemini Enterprise batch prediction, in a few months. The case rests on two decisions: giving the model the native PDF (more than 99% of the corpus passes through as-is, no OCR and no rendering) and accepting the asynchronous latency of batch at half the interactive price. Before Gemini the team had tried off-the-shelf moderation tools and open models, with quality insufficient at that scale.

  7. Sep 30

    Target's wish lists: 45% more demand, then AI on top

    In September 2026 Target described how its site works during the back-to-school season: customers who build a wish list generate roughly 45% more demand in the category, and AI arrives on top of that lever to suggest the products and to give the «next best action widget» its seasonal context. The order of operations is the lesson of the case: first the behaviour measured with a denominator, then the model. The rough point remains the design of the measurement, with the correlation stated by the company and the control group still outside the public account.

  8. Sep 29

    Munich Re Insures AI: Who Pays When the Agent Gets It Wrong

    Munich Re's aiSure insures the performance of AI systems: under the «Contractual Liabilities» structure the vendor guarantees the customer that the model will do what was promised, and the insurer stands behind that guarantee after a technical assessment of the solution. This is economic loss between businesses, the very ground that Article 6 of European Directive 2024/2853 leaves outside the scope of recoverable damages. The article sets out what the guarantee covers, what the insurer measures before signing, the role of the agent's log as evidence, and the questions a CFO should take to the

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