NVIDIA Financial Services Canada

RBC Capital Markets

Document Processing · Enterprise Knowledge Search

RBC Capital Markets needed to speed up document-heavy research and banking workflows so analysts and bankers could deliver faster insights, prepare client materials more efficiently, and answer complex questions without hours of manual review.

10x increase in document processing capacity
More than 8,000 users
Near-real-time answers to complex client queries
Up to 60% faster Research QuickTakes generation

Reported impact

Business outcome

RBC increased document processing capacity by 10x, cut Research QuickTakes generation time by up to 60 percent, moved complex client queries to near-real-time answers, and expanded the Aiden platform to more than 8,000 users across capital-markets teams.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

RBC Capital Markets expanded its Aiden AI platform with NVIDIA AI Enterprise so specialized agents could automate research and banking workflows without replacing expert judgment. The AidenResearch Platform uses AI agents to generate earnings content, summarize calls, surface insights from data catalogs, and provide real-time access to filings for the Global Research division. RBC is extending the same pattern into AidenBanker, where agents automate pitchbook creation, pre-meeting preparation, and note-taking for bankers. NVIDIA NeMo and NIM are part of the platform stack for model customization and serving, while NVIDIA RAPIDS supports higher-throughput data processing underneath the agent workflows. Together these layers create a reusable agentic platform that sits between capital-markets data sources and end users, reducing manual effort while keeping outputs tied to line-of-business workflows.

Data flow

Filings, call transcripts, research catalogs, and banker notes are processed through the Aiden platform, where customized models and agent workflows summarize, generate, and surface relevant insights. Those outputs are then delivered into analyst and banker experiences for report generation, pitchbook creation, pre-meeting preparation, and question answering.

Solution architecture

4 components · 3 layers
  1. Compute
    • NVIDIA RAPIDS Accelerates the data-processing workloads that feed the agentic AI experiences used by analysts and bankers.
  2. Serving
    • NVIDIA NIM Serves the inference layer for the Aiden platform's specialized AI agents.
  3. Orchestration
    • NVIDIA AI Enterprise Provides the software platform RBC uses to scale agentic AI across research and banking workflows.
    • NVIDIA NeMo Supports model customization and enterprise adaptation for RBC's specialized research and banking agents.

Architecture clues

  • Agent workflows sit directly inside capital-markets line-of-business processes
  • AidenBanker connects pitchbook creation, meeting prep, and note-taking
  • AidenResearch is a suite of specialized AI agents for the Global Research division
  • The platform is designed to support RBC's C$700 million to C$1 billion AI value ambition

Evidence from the source

Building on this success, RBC is developing AidenBanker-a unified platform for bankers that connects essential functions like automated pitchbook creation, pre-meeting preparation, and comprehensive note-taking.
Document processing capacity has increased 10x, with improved accuracy.
RBC Capital Markets developed the AidenResearch Platform-a suite of specialized AI agents tailored for its Global Research division.
Research QuickTakes Report generation time is up to 60% faster.
Transparency Evidence & methodology 100/100 reference value · 89% 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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference 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. NVIDIA

    RBC Capital Markets customer story

    First-party
    https://www.nvidia.com/en-us/case-studies/rbc-capital-markets/
    Source type
    Vendor-published customer story
    Published
    Feb 2, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 2, 2026 · NVIDIA · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.