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IBM Software / SaaS Mexico

NDS Cognitive Labs

Customer Service Automation · Sales Automation

NDS Cognitive Labs needed to help Clip support a fast-growing base of first-time digital-finance users at scale, answering questions about fees, setup, payments, and credit without relying on expensive manual support or traditional banking infrastructure.

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28% annual increase in chatbot usage by Clip’s users
80% faster average interaction time when compared to traditional call centers
More than 6,000 simultaneous interactions handled by the virtual assistant
Over 66% of customer inquiries autonomously resolved with generative AI

Reported impact

Business outcome

The NDS-built assistant improved customer support efficiency for Clip, resolved most inquiries autonomously, shortened interaction times dramatically, and increased digital-service adoption among users who are often new to financial technology.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

NDS Cognitive Labs used IBM watsonx natural-language capabilities, NeuralSeek, and IBM Cloud to build a retrieval-augmented virtual assistant tailored to Clip’s fintech support model. The system identifies customer intent, retrieves real-time information from external sources, and generates context-rich responses about remote payments, credit lines, fees, and onboarding rather than serving static FAQ content. Salesforce Cloud integration lets the assistant validate identities, generate leads, and open level-2 support cases when conversations need escalation, while omnichannel delivery extends the experience across websites, social media, WhatsApp, and other digital touchpoints. By combining NLP, RAG, CRM data, and real-time retrieval, NDS created a service layer that advances financial inclusion for underserved businesses and individuals while also improving operational efficiency for the fintech. The result is a scalable conversational platform that handles high concurrency and leaves human agents free to focus on harder cases.

Data flow

Customer questions arrive through web, social, WhatsApp, and other digital channels, watsonx and NeuralSeek interpret the request and retrieve relevant real-time information, Salesforce Cloud contributes CRM context for validation or escalation, and the assistant returns a direct answer or opens a higher-tier support case.

Solution architecture

4 components · 4 layers
  1. Compute
    • IBM watsonx Provides the natural language and generative AI capabilities behind the virtual assistant.
  2. Storage
    • Salesforce Cloud Provides CRM data and workflow hooks for lead generation, case creation, and identity validation.
  3. Serving
    • IBM Cloud Hosts the conversational AI platform that supports high concurrency and omnichannel delivery.
  4. Orchestration
    • NeuralSeek Augments the assistant with retrieval-augmented generation so it can ground responses in current information.

Architecture clues

  • Salesforce integration enables lead generation, level 2 case creation, and identity validation.
  • The assistant uses retrieval-augmented generation to combine instant retrieval with generative responses.
  • The design supports omnichannel service across websites, social platforms, and WhatsApp.

Evidence from the source

“It can handle more than 6,000 simultaneous interactions”
“NDS Cognitive Labs used IBM watsonx natural language processing (NLP), integrated with NeuralSeek and powered by the IBM Cloud platform”
“its seamless integration with Salesforce Cloud”
“leverages the retrieval-augmented generation (RAG) technique”
“resolves over 66% of inquiries autonomously by using RAG.”
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
    5 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. IBM

    NDS Cognitive Labs customer story

    First-party
    https://www.ibm.com/case-studies/nds-cognitive-labs
    Source type
    Vendor-published customer story
    Published
    Feb 25, 2025
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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