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IBM Commercial and Professional Services United States

Humach

Customer Service Automation

Humach wanted to evolve its contact-center offering so digital assistants and human agents could work together more effectively, because rising customer expectations required faster, more contextual resolutions without losing the empathy and escalation paths that live agents provide.

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18% increase in overall customer satisfaction
20% increase in call automation efficiency

Reported impact

Business outcome

Humach improved automation efficiency and overall customer satisfaction while creating a more scalable human-plus-AI contact-center model that can handle peaks without sacrificing service quality.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Humach partnered with Innovative Solutions, IBM, and AWS to upgrade its mAI Pilot platform with a conversational language module built on IBM watsonx Assistant and the Tailwinds GenAI service. The solution uses watsonx Assistant’s interface and APIs to detect customer intent, manage flow control across operators and teams, and keep human and digital channels aligned instead of separated. Running on AWS, the stack also includes Deepgram speech-to-text and text-to-speech, NVIDIA GPU-backed processing, and Weaviate indexing so the assistant can retrieve and respond against large historical and live datasets quickly. This design lets Humach support omnichannel service with both automation and live-agent handoff, improving speed without removing people from the experience. By combining IBM tooling with cloud infrastructure and retrieval components, Humach created a scalable support architecture that reduces routine load, keeps interactions context aware, and gives agents more time for the high-value moments where human judgment matters most.

Data flow

Customer queries arrive through Humach’s support channels, watsonx Assistant and Tailwinds identify intent and orchestrate routing, Deepgram converts voice interactions when needed, Weaviate retrieves context from historical and current data, and the system either resolves the issue automatically or hands it to live agents with the right context.

Solution architecture

5 components · 4 layers
  1. Ingest
    • Deepgram Adds speech-to-text and text-to-speech functions for voice-enabled customer interactions.
  2. Compute
    • Tailwinds Provides the GenAI service layer that powers Humach’s conversational language module framework.
  3. Storage
    • Weaviate Indexes large datasets so the assistant can resolve customer queries quickly with relevant context.
  4. Serving
    • IBM watsonx Assistant Handles intent understanding and system-flow management across digital assistants, operators, and teams.
    • Amazon Web Services Hosts the AI-enhanced customer engagement platform and its supporting infrastructure.

Architecture clues

  • Deepgram provides STT/TTS while Weaviate supports fast retrieval from large datasets.
  • The platform is designed to keep humans in the loop for critical customer interactions.
  • The solution runs on AWS and uses NVIDIA GPU-equipped servers.

Evidence from the source

“Humach ensured enhanced data processing speeds, improved accuracy rates, and heightened cybersecurity compliance levels”
“It led to an 18% increase in overall customer satisfaction and a 20% increase in call automation efficiency.”
“The Weaviate indexing engines ... enable swift query resolution from vast datasets.”
“The automated routing systems intelligently streamline incoming traffic.”
“Together, they crafted a comprehensive conversational language module (CLM) framework powered by Innovative Solution’s GenAI service Tailwinds and IBM watsonx Assistant on Amazon Web Services”
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
    2 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

    Humach customer story

    First-party
    https://www.ibm.com/case-studies/humach
    Source type
    Vendor-published customer story
    Published
    Nov 6, 2024
    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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