Professional services · Public affairs
Five agents that work like an extra team member
RPP Group, a leading public affairs consultancy, was losing strategic capacity to routine manual work: document analysis, data structuring, drafting. We built ChatRPP, a multi-agent system fitted to their workflows, their professional tone and their compliance requirements.
Key results
Client: RPP Group
A leading public affairs consultancy specialising in policy development and political communication.
- Industry
- Public affairs and consulting
- Use case
- Document analysis, content generation, research
- AI approach
- Multi-agent architecture with RAG
- Data source
- Internal documents and external policy data
- Compliance
- GDPR-compliant, inside their own infrastructure
- Partner
- Policy-Insider.AI
In short
The work that crowded out the work that pays
- RPP Group analyses complex political documents, structures policy information, drafts strategic communications and monitors regulation. All of it demands expertise, and much of it follows a predictable pattern.
- Summarising long documents, extracting policy positions, structuring data for a presentation, writing a first draft: that consumed the hours that strategic, client-facing work needed.
- A generic chatbot was never the answer. The system had to understand the domain, match the firm’s professional tone, respect the compliance requirements and fit how the team already works.
- ChatRPP now carries that routine layer, routine work is down 70 percent, and the confidential material never leaves RPP Group’s own environment.
The starting point
The challenge
The problem was not finding a chatbot. It was building a system that understood RPP Group’s specific domain, matched their professional tone, respected their compliance requirements and fitted into how their team actually works.
RPP Group works at the intersection of policy, politics and communication. The team regularly analyses complex political documents, structures policy information, drafts strategic communications and monitors regulatory developments: work that demands deep expertise but is full of repetitive manual steps.
A significant share of their time went into tasks that follow predictable patterns: summarising lengthy documents, extracting key policy positions, structuring data for presentations, drafting initial versions of communications materials.
That left less time for the strategic, client-facing work where their expertise creates the most value.
The build
What we built
Together with Policy-Insider.AI we developed ChatRPP, a custom multi-agent system built for RPP Group’s workflows.
One orchestrator in front of five specialists
Rather than a single general-purpose chatbot, ChatRPP is several specialised agents, each focused on one type of task. A main orchestration agent routes the query to the right specialist, whether that is document analysis, content drafting, strategic planning or policy research, so every task is handled with the right context and the right tools.
Hundreds of pages into what matters
A team member uploads a policy paper, a regulatory text or a legislative proposal and gets back a structured summary, the key takeaways and answers to follow-up questions, in real time.
Drafts in the firm’s own voice
ChatRPP writes strategic plans, core messaging and communication drafts that match RPP Group’s professional tone and corporate identity. Not generic text: content shaped the way the team actually addresses stakeholders and policymakers.
Confidential material stays inside
Political affairs work is sensitive, so the system was built to strict data security. All processing is GDPR-compliant and runs inside RPP Group’s own infrastructure, which means confidential information never leaves their controlled environment.
Agents that check the other agents
Specialised QA agents verify outputs against internal standards, which lowers the risk of an inaccuracy reaching a client and keeps generated content at the quality RPP Group’s clients expect.
What changed
The results
Before
Hours spent on manual document analysis, data structuring and first drafts. Strategic work crowded out by routine tasks.
After
Automated analysis, real-time answers and drafts generated in seconds. The team back on high-value strategic and client-facing work.
Time spent on routine manual tasks fell sharply. The system handles the document summarisation, data structuring and initial drafting that had been taking hours of senior staff time every week.
The team describes ChatRPP as an intelligent additional team member: it sits inside the daily workflow, it understands the domain, and its output needs little editing.
Decisions come faster, because the relevant data and insight are available in real time rather than after hours of manual research.
Questions about this project
Document analysis, content generation, research. The problem was not finding a chatbot. It was building a system that understood RPP Group’s specific domain, matched their professional tone, respected their compliance requirements and fitted into how their team actually works.
Less time spent on routine manual tasks: –70%. Specialised agents working as one coordinated system: 5 agents. Answers from internal and external data sources: Real time. Confidential data processed inside their own infrastructure: GDPR.
Multi-agent architecture with RAG. Technology used: Multi-agent architecture, Large Language Models, RAG, Document processing, Policy-Insider.AI integration, Python, GDPR-compliant infrastructure.
Technology used
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