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IBM Software / SaaS India

Assisto Technologies

Customer Service Automation

Assisto needed to analyze high volumes of multilingual, code-mixed customer calls in real time so enterprises could extract sentiment, emotion, and actionable insights without relying on slow manual transcription and review.

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15%–20% improvement in customer retention and engagement
20%–30% increase in customer satisfaction scores
60%–70% reduction in time spent on manual transcription
Up to 50% cost savings through intelligent automation

Reported impact

Business outcome

Assisto helped enterprises reduce transcription effort and support costs while increasing customer satisfaction, retention, and engagement through more inclusive multilingual voice interactions and richer call analytics.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Assisto integrated IBM watsonx.ai and fine-tuned IBM Granite large language models into its iAssist conversational AI platform to create a more human-like multilingual voice bot. Assisto's in-house automatic speech recognition and text-to-speech engines transcribe and vocalize code-mixed customer speech, then watsonx.ai detects sentiment and emotion in real time while the Granite models improve response quality for multilingual interactions. The platform also generates call summaries and performs contextual text analysis so customer-service teams can understand each conversation faster. To operationalize those insights, IBM Cognos Analytics aggregates issue trends, sentiment patterns, and agent performance into a unified dashboard. This architecture transforms raw audio into structured business intelligence, giving banking, telecom, insurance, and retail customers better conversational automation and visibility into what callers are experiencing across every voice-based touchpoint.

Data flow

Customer calls are transcribed by Assisto's ASR layer, watsonx.ai and Granite analyze the text for intent, sentiment, emotion, and response generation, the platform produces summaries and contextual text analysis, and Cognos Analytics presents the resulting insights to support teams through a unified dashboard.

Solution architecture

3 components · 2 layers
  1. Compute
    • IBM watsonx.ai Provides real-time sentiment and emotion detection for multilingual customer calls.
    • IBM Granite Supplies the fine-tuned large language models that improve response quality in code-mixed interactions.
  2. Serving
    • IBM Cognos Analytics Aggregates issue trends, sentiment patterns, and agent performance into an analytics dashboard.

Architecture clues

  • Summaries and contextual analysis are generated directly from unstructured audio interactions.
  • The platform uses code-mixed multilingual voice input rather than single-language text workflows.
  • The upgraded solution is available across the U.S., Europe, and Asia Pacific.

Evidence from the source

20%–30% increase in customer satisfaction scores
Assisto and IBM collaborated to integrate IBM watsonx.ai and IBM Granite large language models (LLM), fine-tuned by Assisto, into Assisto’s conversational AI platform, iAssist.
The iAssist platform also autogenerates concise call summaries and supports advanced text analysis for contextual understanding.
Watsonx.ai enables real-time sentiment and emotion detection, while fine-tuned Granite LLMs enhance response quality in code-mixed interactions.
an analytics dashboard built with IBM Cognos Analytics aggregates insights
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
    4 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

    Assisto Technologies customer story

    First-party
    https://www.ibm.com/case-studies/assisto-technologies
    Source type
    Vendor-published customer story
    Published
    Jul 17, 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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