Customer Snapshot
Customer: Develeap (Internal Platform)
Industry: Cloud & DevOps Consulting
Company Size: Mid-size AWS Premier Partner
AWS Services Used: Amazon Bedrock, Amazon RDS (PostgreSQL + PGVector), Amazon EC2, Amazon S3, AWS IAM, Amazon VPC
At Develeap, we built an internal AI-powered platform to transform how organizational knowledge is captured and accessed—turning weekly reports and operational data into real-time insights on employee expertise, experience, and activity, powered by AWS.
The Business Challenge
As a growing DevOps and cloud consultancy, a significant amount of valuable knowledge at Develeap is generated continuously—through weekly reports, ongoing projects, internal communications, and hands-on work across customers.
However, this knowledge was fragmented and difficult to access.
Understanding who worked on what, identifying relevant experience, or finding the right expert for a specific need often required manual effort, personal familiarity, or internal coordination. Certifications, hands-on experience, and recent activity were not easily searchable or connected.
As the organization scaled, this created gaps in visibility, slowed down internal decision-making, and made it harder to fully leverage the collective knowledge of the team.
We needed a solution that could:
- Centralize knowledge from multiple internal sources (with a focus on weekly reports)
- Provide visibility into employee experience, certifications, and recent work
- Enable intuitive search and natural language exploration of internal knowledge
- Support faster, data-driven decisions across delivery and partnerships
The Strategic Solution with AWS
To address this, we built an internal AI-powered platform that aggregates, structures, and analyzes organizational knowledge—making it accessible through both search and conversational interfaces.
The system continuously ingests data from internal sources, primarily weekly reports written by engineers, and enriches it using Generative AI.
The architecture is built on AWS:
- Amazon Bedrock to power natural language understanding, summarization, and Q&A
- Amazon RDS (PostgreSQL + PGVector) to store structured data alongside vector embeddings for semantic search
- Amazon EC2 to run orchestration and API services
- Amazon S3 for data ingestion and storage
- AWS IAM & VPC to ensure secure access and data isolation
The platform provides multiple ways to interact with the data:
- A competency explorer that allows filtering and searching employees by skills (e.g., VPN, pipelines, etc.)
- A profile view that surfaces certifications, technologies, cloud experience, and recent work
- A natural language chat interface that allows users to ask questions and receive contextual insights
- A semantic search layer that connects related work, experience, and knowledge across the organization
Instead of relying on static documents or tribal knowledge, teams can now instantly discover expertise, understand experience, and make informed decisions.
Business Outcomes & Impact
The platform significantly improved visibility and accessibility of organizational knowledge:
- Centralized knowledge from weekly reports and internal activity into a single system
- Reduced time to identify relevant experts and experience
- Improved decision-making in staffing, delivery, and partnerships
- Increased transparency across teams and projects
- Enabled non-technical stakeholders to access insights through natural language
The ability to combine certifications, hands-on experience, and recent activity created a more accurate and dynamic view of employee expertise.
Beyond efficiency, the platform strengthened how Develeap leverages its people—turning dispersed knowledge into a strategic asset.
Working with AWS
Using AWS-native services allowed us to quickly build and scale the platform while maintaining security and flexibility.
Amazon Bedrock enabled us to integrate Generative AI capabilities without managing models or infrastructure, while services like RDS and EC2 provided a simple and effective foundation for data processing and APIs.
This allowed us to focus on building a product that delivers real internal value, rather than investing in operational complexity.
Next Steps
Following the success of the platform, we are continuing to expand its capabilities:
- Enriching the knowledge base with additional internal data sources
- Improving AI-driven insights and recommendations
- Expanding use cases across delivery, sales, and partnerships
- Enhancing the chat experience to support deeper analysis and decision-making
With this foundation in place, we are turning internal knowledge into a continuously evolving, AI-powered system—supporting smarter decisions and better utilization of expertise across the organization.
