Reported impact
Business outcome
Scaled data pipelines to enrich 400,000 advisor profiles and 1,500 transactions
- Productivity Improvement
- Revenue Growth
- Scalability
Google CloudFinancial ServicesReal-time AnalyticsSales AutomationAI and Machine LearningCloud NativeData AnalyticsProductivity ImprovementRevenue GrowthScalabilityUndated Google CloudGC#4285f4Financial ServicesUnited StatesTo build a data infrastructure capable of supporting advanced machine learning calculations without expanding operational overhead, RIA Catalyst migrated its user interface layer to a decoupled Next.js web application…Reclaimed 20 engineering hours per month by automating storage ingestion pipelinesModified · Jul 29, 20265Real-time Analytics|Sales AutomationAlloyDB · AlloyDB Omni · BigQuery0 2026-07-29 5
RIA Catalyst
Real-time Analytics · Sales Automation
To build a data infrastructure capable of supporting advanced machine learning calculations without expanding operational overhead, RIA Catalyst migrated its user interface layer to a decoupled Next.js web application architecture.
Business clients in the high-net-worth broker-dealer ecosystem demanded highly responsive, low-latency data interactions that rigid visualization templates simply could not sustain.
Integrating AlloyDB Omni and Cloud Run directly solved the severe latency and concurrency blockages that threatened platform reliability under heavy user loads.
New database deployments went from hours to a few minutes
RIA Catalyst significantly reduces database latency and automates M&A sourcing with AlloyDB Omni
Solution
RIA Catalyst paired AlloyDB Omni and Cloud Run to process complex regulatory data, transforming wealth management mergers and acquisitions processes with real-time machine learning. RIA Catalyst , a disruptive mergers and acquisitions software platform headquartered in Dallas, Texas, operates at the specialized intersection of wealth management intelligence and machine learning technology. The startup provides predictive sourcing resources for large registered investment advisors managing upwards of 100 billion dollars in assets under management, alongside institutional private equity groups executing strategic sector consolidations. When the company first launched, the engineering group constructed a basic minimum viable product by piping structured datasets straight from BigQuery into a standard, out-of-the-box PowerBI visualization interface.
Solution architecture
- Compute
- Cloud Run Runs serverless application services and APIs with automatic scaling for bursty workloads.
- Storage
- BigQuery Serves as the governed analytics and storage layer for high-volume operational data, features, and reporting.
- AlloyDB Omni Stores transactional state and application data for the operational workflow at low latency.
- AlloyDB Stores transactional state and application data for the operational workflow at low latency.
Architecture clues
- Eradicating concurrency deadlocks to accelerate sourcing performance
- Integrating AlloyDB Omni and Cloud Run directly solved the severe latency and concurrency blockages that threatened platform reliability under heavy user loads.
- Powering agentic AI pathways to disrupt investment banking
- To build a data infrastructure capable of supporting advanced machine learning calculations without expanding operational overhead, RIA Catalyst migrated its user interface layer to a decoupled Next.js web application architecture.
Evidence from the source
Our early PowerBI setup quickly hit structural limitations when we introduced sophisticated machine learning models. Graduating to a native Next.js interface backed by AlloyDB Omni allowed us to scale easily, completely
RIA Catalyst , a disruptive mergers and acquisitions software platform headquartered in Dallas, Texas, operates at the specialized intersection of wealth management intelligence and machine learning technology. The start
RIA Catalyst paired AlloyDB Omni and Cloud Run to process complex regulatory data, transforming wealth management mergers and acquisitions processes with real-time machine learning.
RIA Catalyst significantly reduces database latency and automates M&A sourcing with AlloyDB Omni
Scaled data pipelines to enrich 400,000 advisor profiles and 1,500 transactions
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
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 value82/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth15/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity12/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/252 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence75%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance0/10The publication date has no documented provenance.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity6/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://cloud.google.com/customers/ria-catalystGoogle CloudFirst-party
RIA Catalyst 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 Jul 29, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.Open date source
- Unverified extracted date Jul 29, 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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