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Modernize your cloud. Maximize business impact.
Customer snapshot
InVivo Analytics (InVivoAX) is a preclinical imaging platform trusted by leading research institutions including Pfizer, Boston University, and the University of Washington. Their AWS-hosted platform enables drug discovery teams to process multimodal imaging datasets and extract radiomic biomarkers at scale.
Goal
Deploy a natural-language AI assistant inside InVivoAX's AWS platform, enabling researchers to query study data, trigger computations, and generate automated study reports through conversational prompts.
Key results
Challenge
Researchers were managing data instead of generating insight
Accessing imaging data required manual pipeline navigation. Researchers had to find the data themselves rather than simply asking for it.
Computation required multiple manual steps
ROI calculations, statistical comparisons, and cohort analysis all required separate manual effort outside the core workflow.
Security and data isolation were non-negotiable
Any AI assistant needed JWT-based authentication, user-isolated access, read-only defaults, and explicit confirmation before any data changes.
Solution
Cloudtech deployed a conversational AI assistant inside InVivoAX's AWS environment. Researchers ask plain-language questions "Which treatment group shows the highest tumour signal at day 14?" and receive immediate answers drawn directly from the Atlas MongoDB database.
Secure MCP server and API proxy
FastMCP server with read-only tools for cohorts, ROIs, timepoints, and pipeline state. JWT-authenticated API proxy, no direct agent-to-backend access.
LangGraph agent with session persistence
LangGraph agent on Amazon Bedrock handles streaming responses, project switching, and MongoDB-backed session persistence across conversations.
Human-in-the-loop computation
ROI generation, statistical analysis (t-test, ANOVA, regression), and chart display — all triggered conversationally with explicit user confirmation before any data is written.
Automated study report generation
Comprehensive reports synthesising findings, flagging data quality issues, and producing regulatory-ready narratives with PDF export generated from a single prompt.
Scope + Timeline
Outcomes
- Researchers query data, trigger computations, and generate reports through plain-language prompts
- Human-in-the-loop controls, all data changes require explicit approval and are fully logged
- Statistical comparisons available on demand through the conversational interface
- Study reports generated in a single prompt, regulatory-ready with tables, figures, and quality flags
- InVivo described the team as "professional, responsive, and excellent" and extended the engagement beyond the initial phase
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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.
Get started on your cloud modernization journey today!
Let Cloudtech build a modern AWS infrastructure that’s right for your business.