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Modernize your cloud. Maximize business impact.
Customer snapshot
Eli Lilly is a global pharmaceutical company and one of the world's largest producers of insulin and other critical medications. As part of an AWS ProServe engagement, Cloudtech was brought in to automate and scale Lilly's Innovation Readiness Assessment process — a complex, manual workflow requiring significant effort from domain experts across multiple data sources.
Goal
Design and implement an enterprise Agentic AI platform on AWS that automates Eli Lilly's Innovation Readiness Assessment process — replacing manual SME effort with a standardised, scalable, evidence-backed AI workflow.
Key results
Challenge
Manual assessments could not scale
Eli Lilly's Innovation Readiness Assessment required domain experts to manually collect, analyse, and evaluate information across internal, public, and licensed data sources. Each assessment was time-consuming, difficult to standardise, and challenging to replicate across different products and therapeutic areas.
Evidence was fragmented and inconsistent
Information lived across enterprise knowledge bases, public APIs, licensed data sources, and proprietary tools. Without a unified retrieval layer, assessments lacked traceability and consistency — making it difficult for SMEs to validate findings or understand how conclusions were reached.
SME oversight could not be eliminated
Despite the need for automation, human expert review and override capability had to be preserved. The solution needed to augment SMEs, not replace them — with confidence scoring, source evidence, and human-in-the-loop controls built into every assessment.
Solution
Cloudtech designed and implemented an enterprise Agentic AI platform inside Eli Lilly's AWS environment. Eight specialised AI agents operate in parallel to evaluate IRA attributes, retrieve supporting evidence, generate structured scores and rationales, and consolidate findings into a comprehensive readiness assessment.
Multi-agent orchestration
A multi-agent architecture using LangGraph and LangChain enables eight specialised domain agents to execute in parallel and consolidate their findings into a standardised IRA assessment — reducing assessment time from days to hours.
Evidence-grounded retrieval
RAG implemented across enterprise knowledge, public APIs, licensed data sources, and custom data tools provides traceable, contextually relevant insights — with confidence scoring, contradiction detection, and coverage-gap identification built into every output.
Human-in-the-loop controls
Domain SMEs can review, validate, and override AI-generated results at any point. Persistent assessment history and conversational refinement allow SMEs to interact with completed assessments and understand the reasoning behind every finding.
Scope + Timeline
Outcomes
- Eight specialised AI agents operating in parallel — replacing a manual, SME-intensive process with a standardised, repeatable workflow
- Evidence-grounded retrieval across enterprise knowledge, public APIs, and licensed data sources — with full source traceability on every finding
- Confidence scoring, contradiction detection, and coverage-gap identification built into every assessment output
- Human-in-the-loop controls maintained — SMEs retain full ability to review, validate, and override AI-generated results
- Persistent assessment history enables conversational refinement — SMEs can interrogate findings and understand reasoning
- Reusable enterprise Agentic AI pattern established — extendable across products, therapeutic areas, and other knowledge-intensive workflows
Technology stack
Agentic AI: LangGraph, LangChain, Claude Models
RAG and Embeddings: Retrieval-Augmented Generation, Embedding Models, Evidence-Grounded Generation
Backend: Python, FastAPI, REST APIs
Data and Storage: Amazon DynamoDB, Amazon S3
Deployment: Amazon EKS, Docker, Amazon ECR
CI/CD: GitHub Actions, ArgoCD, AWS CloudFormation
Monitoring: Amazon CloudWatch
Interface: React.js Web Portal, Microsoft Entra ID Authentication
Want similar results for your business?
Schedule a call with the Cloudtech team to scope an Agentic AI engagement for your use case. We typically go from baseline to production in four to six weeks.
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Customer snapshot
Ascend BPO is a US-based healthcare business process outsourcing company managing appointment scheduling and
patient intake for healthcare providers across the United States. With high inbound call volumes and strict HIPAA compliance
requirements, Ascend BPO needed an AI voice agent capable of handling thousands of monthly calls accurately, securely,
and with seamless escalation to human agents when needed.
Goal:
Deploy a HIPAA-compliant AI voice agent on AWS to automate inbound appointment scheduling at scale — handling 2,500–
5,000 calls per month with intelligent human escalation.
Key results
Challenge
High call volume, limited staff capacity
Ascend BPO's healthcare clients receive thousands of appointment scheduling calls every month. Human agents were
spending the majority of their time on routine intake workflows that could be automated — creating bottlenecks and
inconsistent caller experiences
HIPAA compliance at every layer
Every interaction involved Protected Health Information — patient names, dates of birth, insurance IDs, and appointment
details. Any solution needed end-to-end HIPAA compliance covering secure storage, encrypted transmission, access
controls, and full audit trails.
Unpredictable real-world callers
Real callers don't follow scripts. Confused patients, incorrect insurance details, urgent booking requests, and off-topic
questions are all part of everyday call volume. The system needed to handle each scenario gracefully — and escalate to a
human agent without losing context or forcing the caller to repeat themselves.
Solution
Cloudtech designed and deployed a HIPAA-compliant AI voice agent inside Ascend BPO's AWS environment, built on
Amazon Bedrock, Amazon Transcribe, and Amazon Polly. The agent handles the complete appointment scheduling workflow
end to end — from greeting to confirmed booking — without requiring a human for routine calls.
Scope + Timeline
Outcomes
- ,5 00–5,000 inbound calls per month handled autonomously — no human required for routine scheduling
- Identity and insurance verification completed in under 60 seconds per call
- Warm transfer to human agent in under 2 seconds, with full context handed over seamlessly
- Fully HIPAA-compliant — encrypted PHI handling, access controls, and audit trail on AWS
- Stress tested across complex real-world scenarios including confused callers, aggressive behaviour, tangent
conversations, and urgent bookings - Complete patient lifecycle supported — create record, verify and update insurance, check availability, book and confirm
appointment
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Salesforce-to-S3 Data Pipeline
Source One Spares needed a secure, cost-effective way to back up Salesforce data to AWS, without managing complex
infrastructure. They required encryption, auditing, compliance controls, and a solution their on-prem team could use with
their existing stack.
Goal:
Build a production-ready AWS backup pipeline, no compute overhead, no ongoing management, no complexity.
Key results
Challenge
No infrastructure overhead
Source One Spares didn't want to manage servers, Lambda functions, or complex pipelines. They needed a backup solution that just works, set it and forget it.
Enterprise-grade security
Salesforce data is sensitive. They required encryption at rest and in transit, strict access controls, and full audit trails for compliance.
Cost efficiency at scale
Storing years of Salesforce backups can get expensive fast. They needed intelligent tiering to minimize costs without sacrificing access when needed.
What We Built
A production-ready AWS backup pipeline; zero compute, zero complexity.
Cost Optimization
Deliverables
- Architecture diagrams ( VPC + S3 backup flow)
- IAM & Access Runbook with sample code
- Validation Report with security verification
- Live handoff session with connectivity test
7 Security Controls Enforced
- SSE-KMS encryption
- HTTPS-only
- Least-privilege IAM
- CloudTrail logging S3 access logs
- Versioning enabled
- Blocked public access
Want Similar Results for your business?
Schedule a call with the Cloudtech team to scope a voice-agent pilot for your use
case. We typically go from baseline to production in four weeks.
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A family-owned travel company in Myrtle Beach built an outbound voice agent that qualifies leads naturally, then hands off to
human bookers , cutting cost-per-call 67% while keeping their U.S. based phone team at the center of every booking.
Key results
Customer Snapshot
Monster Reservations Group has booked vacations for thousands of families across 50+ destinations since 2006.
Their competitive edge has always been their U.S.-based phone team — fast, friendly, and the reason customers come back. The challenge was scaling this team without losing that quality.
The Challenge
Slow responses broke the flow
The existing AI agent took 1.5 seconds to respond; long enough for callers to notice, interrupt, or lose
confidence. Conversations felt robotic, not human.
Manual outreach was inefficient
Agents spent time on initial calls gathering basic preferences before they could focus on actually booking
vacations. This limited how many customers they could serve.
The Bar for Success
Agents spent time on initial calls gathering basic preferences before they could focus on actually booking vacations. This limited how many customers they could serve.
The Solution
CloudTech built an outbound AI voice agent designed for one job: qualify leads efficiently. Built on AWS using Amazon
Bedrock for reasoning, Amazon Connect for telephony, and ElevenLabs for natural voice synthesis.
Outbound calling
The AI agent initiates calls to customers and engages in natural conversation about their vacation preferences.
Preference gathering
Captures destination interests, travel dates, group size, and budget through guided dialogue.
Seamless handoff
Once preferences are gathered, the call transfers to a human agent via Amazon Connect with full context. No need for the customer to repeat anything.
Scope & timeline
Outcomes
- Projected 67% reduction in cost-per-call by automating initial outreach
- 500ms response latency, conversations feel natural, not robotic
- 95%+ accuracy in gathering customer preferences
- Human agents now start conversations with full context, ready to book
What we tuned
Model selection
Amazon Bedrock for reasoning, ElevenLabs for natural voice
Conversation flow
Refined dialogue to gather preferences naturally
Handoff timing
Smooth transfer via Amazon Connect with full context
Where it can be replicated
Outbound sales & lead qualification at scale
Teams that need to make hundreds or thousands of initial contact calls where the goal is to qualify, not close.
Information gathering before human conversation
Businesses where every human conversation is more valuable when it starts with full context
healthcare intake, financial pre-qualification, service scheduling.
Automated outreach with human closing
Any team that wants to automate the first 30 seconds of every call without losing the human touch on
the booking, sale, or decision moment.
Want Similar Results for your business?
Schedule a call with the Cloudtech team to scope a voice-agent pilot for your use case. We typically go from baseline to production in four weeks.
Get started on your cloud modernization journey today!
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