Building an AI Agent–Driven Platform  for Complex Decision-Making

The Challenge

Scalable AI & Data Integration
Automatically scrape public data and use AI Agents to identify critical issues and stakeholder relationships.

Monitoring & News LinkingContinuously integrate web-crawled news, linking content to stakeholders and issues using AI Agents.

Organizational User StructureAn intuitive UX/UI was key to simplifying stakeholder relations, issue mapping, and data visualization.

UX & TrustProvide an intuitive interface with timestamped, fully traceable data sources to build user confidence.

The Solution

Design, build, and continuously evolve a scalable and AI Agent–augmented SaaS tool.

AI Agent Issue & Stakeholder Mapping

• AI Agents identify relevant issues and stakeholders from public data.

• Issues are visualized by relevance, impact, and influenceability.
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• Dynamic Stakeholder Map links entities to issues.

AI Agent Monitoring 

• Continuous web-crawled news monitoring.

• AI Agents match news to issues and stakeholders.

• Users validate news relevance and trigger AI summaries.

User-Centric Design

• Flexible organization setup, user invitations, roles, and assignments.

• Workflow-aligned notifications and onboarding.

• Secure login via email or phone.

Data Scraping and Integration (Upcoming)

• Cloud architecture using Neon and Vercel.

• Database isolation with automated backups and history.

• Export dashboards as PNG/PDF for reports.

Technical Challenges

1. Scalable Stakeholder Mapping

Challenge: Dynamic relationships between issues, stakeholders, and data sources.

Solution: Modular DB architecture with weighted relations for real-time adaptability.

2. Monitor Quality of AI Agents

Challenge: High-fidelity news matching using AI.

Solution: Multiple AI Agents for content parsing and validation; summary generation on demand to save tokens.

3. Fast-Paced Feature Changes

Challenge: Rapid releases and feature shifts risked technical debt.

Planned Solution: Focused backlog on UX and tech debt: UI libraries updated, tests restructured.

Results

Feature-Rich MVP & Beyond
Issue & Stakeholder Maps powered by AI Agents. Monitoring section using multiple AI Agents. AI-generated insights tied to real data.

Transparent & Actionable
Timestamped sources, manual and AI-generated summaries, and traceable news improve trust.
Adoption-Ready UX
Exportable maps, custom roles, organizational flexibility, and simplified onboarding all contribute to early user satisfaction.

Growth-Ready Architecture
Setup ready for chatbots, deeper AI Agent analysis, and geographic expansion.

Performance Metrics

Development Timeline
12-week MVP launch. Continuous delivery for Monitoring, Organizations, and AI Agent upgrades.

Client Satisfaction
Monthly retrospectives rated highly by DELOS.

User Engagement
Positive beta testing. Feedback-driven improvements in Monitoring and Maps.

Client Reviews

Silvan Krähenbühl

CEO

Streaver has successfully launched the MVP in live production, serving paying end clients. The team has kept everything on track, enabling them to consistently deliver high-quality work in a short timeframe. They also communicate well internally and externally to ensure everyone is aligned.

AI Agent Issue & Stakeholder Mapping

AI Agent Monitoring

User-Centric Design

Data Scraping and Integration (Upcoming)

1. Scalable Stakeholder Mapping

2. Monitor Quality of AI Agents

3. Fast-Paced Feature Changes

Performance Metrics

Tech Stack

Client Reviews

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