Government and public affairs · Document processing
Policy documents summarised at the speed they are published
Public affairs professionals track hundreds of policy documents published daily across EU institutions and languages. Reading and summarising them by hand was a full-time job that could not scale. We extended the Policy-Insider.AI platform with generative AI that summarises each document automatically and sends targeted alerts, turning hours of reading into seconds of extraction.
Key results
Client: Policy-Insider.AI
A platform for monitoring and analysing public policy across European institutions. theBlue.ai built its AI layer and continues to develop it.
- Industry
- Government and public affairs
- Use case
- Policy document summarisation and retrieval
- AI approach
- LLM with advanced prompting and LLMOps
- Languages
- Several EU languages
- Integration
- Platform extension with email alerts
- Engagement
- Ongoing development
In short
The bottleneck between publication and knowing
- The European Commission, the European Parliament, national governments and regulatory agencies publish policy documents daily, often in different languages.
- Analysts read them, decided which mattered to which client and wrote the summaries, and that limited how many topics and institutions the platform could cover at all.
- Large language models with carefully engineered prompts now summarise each document as it is published, using its type, institution and policy area as context.
- Users set an interest profile, and someone tracking AI regulation gets only the documents about AI regulation, summarised, by email.
The starting point
The challenge
Policy professionals had to track and understand hundreds of documents published across many institutions and languages every day. Manual reading and summarisation could not keep up, and critical developments were missed or arrived too late.
Policy-Insider.AI serves public affairs professionals, lobbyists and corporate government relations teams who have to stay on top of EU legislation, regulation and policy. The volume is enormous: documents published daily by the European Commission, the European Parliament, national governments and regulatory agencies, often in different languages.
Before this project, keeping up meant reading and summarising by hand. Analysts scanned the documents, worked out which were relevant to a client’s interests and wrote the summary.
At the scale of EU policy output that was a bottleneck. It limited how many topics and institutions the platform could cover, and how quickly a user heard about something that affected them.
The build
What we built
We extended the platform with a generative AI layer that automates the most time-intensive part of policy monitoring: reading a document and pulling out what matters.
Summaries written as the document appears
Large language models with carefully engineered prompts produce concise, accurate summaries at publication time. The prompts carry the relevant context, document type, issuing institution, policy area, so the summary is immediately useful rather than a generic abstract.
Language is not a barrier
EU policy documents appear in several languages. The system processes a document regardless of the language it was written in and produces a summary that carries the substance across the linguistic boundary.
Alerts filtered before they are sent
Users set up interest profiles from topics, institutions or keywords. The system writes a targeted summary and delivers it by email, so someone tracking AI regulation sees documents about AI regulation and nothing else, with no manual filtering.
LLMOps, because a good prompt is not a product
Running generative AI in production for professional users needs more than a prompt that worked once. We built an iterative evaluation pipeline to monitor and hold output quality against the real problems: inconsistent output, hallucination risk, and how hard generated text is to evaluate at scale.
What changed
The results
Before
Manual reading and summarising of policy documents. Limited coverage, slow turnaround, and analysts sitting as the bottleneck between a published document and a user knowing about it.
After
Summarisation at the speed of publication, coverage across languages, keyword-targeted delivery, and analysts free to interpret rather than read.
The reading bottleneck is gone. Documents are summarised as they are published, not hours or days later.
The platform’s coverage expanded, because summarisation capacity is no longer limited by how many documents a human team can read in a day.
For enterprise public affairs teams that means faster awareness, broader monitoring, and human expertise spent on interpretation and strategy instead of information gathering.
Questions about this project
Policy document summarisation and retrieval. Policy professionals had to track and understand hundreds of documents published across many institutions and languages every day. Manual reading and summarisation could not keep up, and critical developments were missed or arrived too late.
Policy documents summarised without manual reading: Auto. Documents processed across several EU languages: Multi-lang. Email notifications with keyword-targeted summaries: Alerts. Production evaluation and quality assurance pipeline: LLMOps.
LLM with advanced prompting and LLMOps. Technology used: Large Language Models, Advanced prompt engineering, LLMOps, Multi-language NLP, Document summarisation, Email alerts, Platform integration.
Technology used
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