Reported impact
Business outcome
User engagement quadrupled while resolution time was reduced from two days to a few clicks.
- Cost Reduction
- Customer Experience
- Productivity Improvement
- Revenue Growth
MicrosoftSoftware / SaaSCustomer Service AutomationAI and Machine LearningContact CenterGenerative AI / LLMCost ReductionCustomer ExperienceProductivity ImprovementRevenue Growth2024 MicrosoftMS#0078d4Software / SaaSMalaysiaMaking sense of the startup scene One of the key challenges of Malaysia's startup ecosystem was fragmented access to essential resources and support, causing inefficiencies that hindered startups' growth and success.Dec 3, 20245Customer Service AutomationAzure OpenAI Service0 2024-12-03 5
Cradle
Customer Service Automation
Making sense of the startup scene One of the key challenges of Malaysia's startup ecosystem was fragmented access to essential resources and support, causing inefficiencies that hindered startups' growth and success.
Solution
Cradle built its Microsoft solution to improve customer-service and conversational-support workflows. Making sense of the startup scene One of the key challenges of Malaysia's startup ecosystem was fragmented access to essential resources and support, causing inefficiencies that hindered startups' growth and success. Azure openai service ran model inference or heavy processing. User prompts and domain content are orchestrated through Azure OpenAI Service, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so Cradle can return context-aware answers or automation in the flow of work. The governed Microsoft stack gave Cradle a reusable foundation for generative ai / llm, ai and machine learning while keeping deployment, security, and day-to-day operations inside the Microsoft estate. User engagement quadrupled while resolution time was reduced from two days to a few clicks.
Data flow
User prompts and domain content are orchestrated through Azure OpenAI Service, processed by Azure OpenAI Service, and then delivered through Azure OpenAI Service so Cradle can return context-aware answers or automation in the flow of work.
Solution architecture
- Compute
- Azure OpenAI Service Runs large language models that generate grounded responses, summaries, and copilots.
Architecture clues
- "After exploring other solutions available in the market, it was clear that Microsoft's Azure OpenAI Service would best fit all our needs in terms of scalability, security, integration, support, and reliability," says Ts.
- The end goal is for MYStartup Single Window to function as a self-sufficient venture capital platform that will handle everything a startup needs.
Evidence from the source
"After exploring other solutions available in the market, it was clear that Microsoft's Azure OpenAI Service would best fit all our needs in terms of scalability, security, integration, support, and reliability," says Ts.
"One of the added benefits of working with Microsoft is that it can also support startups that are a part of the MYStartup initiative with the necessary infrastructure and tools." Under Microsoft's guidance and with help from Agmo Group, a Microsoft partner, Cradle built a chatbot powered by Azure OpenAI Service.
Making sense of the startup scene One of the key challenges of Malaysia's startup ecosystem was fragmented access to essential resources and support, causing inefficiencies that hindered startups' growth and success.
User engagement quadrupled while resolution time was reduced from two days to a few clicks.
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 value61/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity16/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact0/250 metrics describe measurable impact.
- Deployment scale5/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
- 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 evidence20/204 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards 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://www.microsoft.com/en/customers/story/18847-cradle-fund-azure-open-ai-serviceMicrosoftFirst-party
Cradle 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
- Publication date
Date evidence
- Published on source page Dec 3, 2024 · Microsoft · First-partyOpen 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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