Use Case Intelligence RSS Search use cases Search
Google Cloud Healthcare United States

Reveleer

Document Processing · Risk Management

In healthcare, no two patients are exactly alike. Therefore, providing the most valuable care requires a deep understanding of patients, their health concerns, and their goals.

Read the original story Explore all use cases
"Doctors often have just a couple of minutes to review a patient's history and understand the highest value care gaps before an appointment," says Alan Tam, Chief Marketing Officer at Reveleer.
Achieves strong balance between intelligence and cost-efficiency with Gemini
Continuous improvement: The engine is enhanced by clinician feedback and self-correcting AI agents-formulas and evidence rules evolve rapidly, reflecting best practices and regulatory changes.
Reduces false positives and improves accuracy of clinical intelligence

Reported impact

Business outcome

Rapid adaptability and customization: Reveleer's deterministic formulas and agentic AI enable customers to update logic, filters, and models in minutes instead of months, ensuring fast alignment with regulatory, clinical, or local needs.

  • Customer Experience
  • Productivity Improvement

Solution

Reveleer uses Google Gemini and Vertex AI to build a next-generation, AI-powered prospective risk adjustment solution that gives doctors the information they need to help patients at the point of care. One of the highlights of the Reveleer platform is its prospective risk adjustment solution. The hybrid AI-powered solution extracts and analyzes both structured and unstructured data from electronic health records, lab reports, doctor's notes, and prescriptions from various data sources. It then delivers provable, explainable, audit-ready insights directly into a physician's workflow, reducing provider abrasion and improving the patient experience and outcome. Reveleer started its search for the right LLM with Vertex AI. Developers started experimenting with top first-party, third-party, and open source models through Model Garden on Vertex AI.

Solution architecture

6 components · 4 layers
  1. Compute
    • Gemini Flash Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Cloud Run Runs serverless application services and APIs with automatic scaling for bursty workloads.
  2. Storage
    • BigQuery Serves as the governed analytics and storage layer for high-volume operational data, features, and reporting.
    • Firestore Stores transactional state and application data for the operational workflow at low latency.
  3. Orchestration
    • Cloud Composer Schedules and orchestrates recurring data and AI pipelines.
  4. Governance
    • Model Garden Adds governance, security, and control to the production AI and data environment.

Architecture clues

  • Agentic evidence extraction: Complex generative AI translates structured and unstructured medical charts into a standardized clinical corpus.
  • Rapid adaptability and customization: Reveleer's deterministic formulas and agentic AI enable customers to update logic, filters, and models in minutes instead of months, ensuring fast alignment with regulatory, clinical, or local needs.
  • Reveleer started its search for the right LLM with Vertex AI.

Evidence from the source

Gemini Flash and Flash-Lite unlock so many new use cases and capabilities for us. We're beyond excited at what this means for Reveleer and our customers.
Manages data efficiently within BigQuery
Reveleer makes extensive use of the Google Cloud ecosystem within its prospective risk adjustment solution for suspecting. Cloud Run is the foundation used to develop and run the AI. Data initially runs through Firestore
Reveleer started its search for the right LLM with Vertex AI. Developers started experimenting with top first-party, third-party, and open source models through Model Garden on Vertex AI. At the end of these tests, devel
Vertex AI Cloud Run Firestore Cloud Composer
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
    5 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

    Reveleer customer story

    First-party
    https://cloud.google.com/customers/reveleer
    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

  • First archived Jan 20, 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 Jan 28, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
Google Cloud Modified · Oct 9, 2026 Policybazaar Financial Services India Motor insurance options in India can be complex, which is why Policybazaar built its market-leading reputation by making the consumer journey entirely transparent and accessible. The digital platform empowers millions… Achieved 97% issuance within five minutes in PAYD using Gemini models Document ProcessingRisk Management Cloud Storage · Gemini 2.5 Flash · Gemini 2.5 Flash-Lite Google Cloud Archived · Sep 17, 2026 Parsewise Software / SaaS United Kingdom For risk specialists in the financial services and insurance industries, reading can feel like a full-time job. Whether assessing an insurance claim or approving a loan, these subject matter experts must search through… 29% lower error rate than best-performing LLM on grounded-reasoning benchmark Document ProcessingRisk Management Gemini · Gemini 3.0 Flash · Gemini 3.5 Flash In-house Jul 28, 2026 Novartis Healthcare global Novartis R&D needs to turn large volumes of scientific, clinical, operational, and real-world data into faster, higher-quality decisions across drug discovery and development, reducing cycle times, surfacing safety… less than 10 percent of drug candidates historically reach the market Document ProcessingReal-time Analytics AI models · AI-enhanced analytics · Generative AI