Real-Time Intelligence Platform for the Longevity Ecosystem: Company Evaluation in Under 20 Minutes
DS Stream built a real-time intelligence platform for First Longevity, a healthcare client, on AWS with Bedrock Agents. It replaced manual consolidation of websites, publications, patents and databases with an automated research workflow. Company evaluation time dropped from 3-5 days to under 20 minutes, and data quality improved by about 30 percent.
Results at a glance
- Faster analysis: company evaluation time fell from 3-5 days to under 20 minutes, enabling near real-time decisions at scale.
- Higher data quality: about 30 percent better, through automation and cross-checking across trusted sources.
- Time saved: hundreds of analyst hours each month, by cutting manual research and reconciliation.
- Always-current intelligence: continuous updates on clinical progress, trials, funding, IP activity, partnerships, leadership changes and pipeline positioning.
What problem did First Longevity need to solve?
First Longevity operates in a fast-moving market. It needed to consolidate insights from many sources in a more efficient and scalable way. Analysts had to cover websites, publications, patents and databases, but the work depended on manual consolidation and static snapshots. That made timely analysis hard to sustain as the volume of information grew.
What did DS Stream build with generative AI agents on Amazon Bedrock?
DS Stream designed and delivered an AI research platform for longevity on AWS. Bedrock Agents drive a scalable research workflow that gathers information, supports review and turns it into actionable insight. The platform brings automated research, structured data handling, workflow orchestration and human review into one operating model.
Core building blocks
- Automated research: generative AI agents on Amazon Bedrock collect information from external sources.
- Structured data handling: gathered findings are organized for analysis instead of kept as static snapshots.
- Workflow orchestration: each research step runs as part of a managed, repeatable flow.
- Human review: analysts stay in the loop to validate results before they are used.
How does the platform keep research data trustworthy?
The platform adds quality controls and a self-service review process. Reviewers validate data before it moves on, and only validated data is promoted into the trusted layer of the platform. Automation and cross-checking across trusted sources raised data quality by approximately 30 percent, while human review keeps final judgment with the analysts.
Which services and technologies did the project use?
The project combined three service areas. Technology names below are listed as delivered.
AI and data intelligence
- Generative AI
- AI agents
- Amazon Bedrock
Cloud and data engineering
- AWS-based architecture
- Workflow automation
- Data pipelines
Analytics and delivery
- BI and big data consulting
- Custom software development
- DevOps managed services
DS Stream drew on its expertise in AI development, generative AI, cloud consulting, BI and big data, custom software and DevOps. Related capabilities: agentic AI architecture and data pipeline architecture.
Measured outcomes
Under 20 minutes
Company evaluation time after launch, down from 3-5 days.
Source: DS Stream case study: First Longevity
~30%
Approximate improvement in data quality through automation and cross-checking across trusted sources.
Source: DS Stream case study: First Longevity
Hundreds of hours
Analyst hours saved each month by reducing manual research and reconciliation.
Source: DS Stream case study: First Longevity
How did research change before and after the platform?
| Criterion | Before | After |
|---|---|---|
| Company evaluation time | 3-5 days | Under 20 minutes |
| Research method | Manual research and consolidation | Automated research with Bedrock Agents plus human review |
| Market view | Static snapshots | Continuous updates across the healthcare ecosystem |
| Data validation | Manual reconciliation | Quality controls and self-service review before promotion to the trusted layer |
| Analyst effort | Heavy manual workload | Hundreds of analyst hours saved each month |
What did First Longevity say about the project?
The move from manual research to a scalable, AI-powered platform gave First Longevity faster analysis, better data quality and a continuous view of a rapidly evolving healthcare market. The client summarized the work this way:
"DS Stream proved to be a trusted partner in building our AI-powered research platform for longevity. Their work helped us turn manual processes into scalable, real-time intelligence, delivering clear improvements in speed, data quality, and operational efficiency."
Questions about this longevity ecosystem AI platform
Which sources does the longevity ecosystem AI platform analyze?
The platform consolidates insights from websites, publications, patents and databases. Before the project, First Longevity combined these sources by hand and worked from static snapshots. Now Bedrock Agents gather the information automatically, and the results are structured, reviewed and promoted into a trusted data layer for analysis.
What market signals does the platform track?
It provides continuous updates on clinical progress, trials, funding, IP activity, partnerships, leadership changes and pipeline positioning across the healthcare ecosystem. This gives First Longevity an always-current view of the companies it evaluates, instead of point-in-time research that goes stale quickly.
Does the AI replace analysts in the research workflow?
No. The operating model combines automated research with human review. Analysts validate data through a self-service review process, and quality controls decide what is promoted into the trusted layer. Automation removes manual research and reconciliation work, which saves hundreds of analyst hours each month.
Related pages
- Healthcare data and AIThe industry page behind this case study, covering DS Stream work for healthcare organizations.
- Agentic AI ArchitectureThe service for designing agent-based systems like the Bedrock Agents research workflow built here.
- Generative AI DevelopmentHow DS Stream builds generative AI solutions from use case to production.
- Cloud SolutionsCloud architecture and engineering services, relevant to the AWS-based platform in this project.
- Optimizing Feature Store and Model Execution for KplerAnother data-intensive platform case study, this time in global trade intelligence.
- All case studiesBrowse DS Stream projects across healthcare, FMCG, e-commerce and global trade.
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