Case studies

Government and public affairs · Full platform build

Political monitoring, from days to real time

Public affairs teams tracked millions of political documents across institutions and languages by hand, which missed things and could not scale. A group of senior public affairs experts from the RPP Group founded Policy-Insider.AI to solve it with AI. They brought the domain expertise; we built the entire technical platform, from proof of concept to production.

Key results

Millions
Political documents processed across several languages
PoC to prod
The full journey from proof of concept to a launched product
LLM + NLP
Collection, structuring and analysis in one pipeline
Real time
Personal dashboards with AI-generated summaries

Client: Policy-Insider.AI
Founded by senior public affairs professionals with backgrounds at the RPP Group. theBlue.ai delivered the AI development and the platform engineering.

Policy-Insider.AI
Industry
Government and public affairs
Use case
Political monitoring and analysis platform
AI approach
NLP, LLMs and data architecture
Scale
Millions of documents, multiple languages
Engagement
Proof of concept through production launch
Our role
AI development and the platform build

In short

What an analyst team cannot cover

  • Legislative proposals, committee minutes, regulatory updates, parliamentary questions and policy papers appear daily, across national and EU institutions, in several languages.
  • Analysts scanned the sources, read documents in full, worked out what mattered to which client and wrote the summaries. At the scale of modern political output that could not keep up.
  • The platform collects those documents automatically, structures them, and uses NLP and LLMs to identify topics, classify by policy area and write the summary.
  • Users see a dashboard built from their own topics, institutions and countries, and the time from publication to usable insight went from days to real time.

The starting point

The challenge

Millions of political documents published in complex, domain-specific language across many institutions and languages, and no automated way to collect them, structure them and get the relevant part to the person who needed it.

Public affairs professionals in corporations, consultancies and government relations teams have to track political developments that could affect their organisation or their clients. The volume is staggering: legislative proposals, committee minutes, regulatory updates, parliamentary questions and policy papers, published daily across national and EU-level institutions, in several languages.

Before this platform that monitoring was largely manual. Analysts scanned the sources, read the documents in full, decided what was relevant to which client and wrote the summaries.

Important developments were missed and responses came late. Covering every relevant topic and institution properly would have meant an analyst team too expensive to maintain.

The build

What we built

The founders’ public affairs experience defined what the platform had to do: which institutions to watch, which document types matter, how practitioners actually work. We took that and built the entire AI and engineering layer, from the first proof of concept to a launched product with paying customers.

01

Collecting and structuring the sources

The platform gathers political documents from national and international sources into a data architecture built to hold millions of documents in several languages. Raw, unstructured political content comes out the other side as structured, searchable data.

02

Analysis that understands political language

NLP algorithms and large language models read the collected documents: identifying topics, extracting the key development, classifying by policy area and writing a concise summary. Political and legal texts do not behave like general-purpose text, and the system is built for that difference.

03

A dashboard per user, not per product

Each user sees a view built from their own topics, institutions, countries and keywords. AI-generated summaries surface what changed in real time, so nobody reads a full document to find out whether it mattered.

04

Proof of concept through to launch

The engagement covered the whole product lifecycle: a proof of concept to validate the approach, iterative development of the platform, and a production launch with real paying customers.

Political monitoring, from days to real time

What changed

The results

Before

Manual monitoring by analyst teams. Limited coverage, slow turnaround, a high cost per topic covered, and relevant developments missed or found too late.

After

Automated collection and analysis of millions of documents, personal real-time dashboards, AI-written summaries, and coverage across languages and institutions.

Political monitoring went from a manual, analyst-dependent process to a scalable service, and coverage expanded in both topics and institutions.

The time from a document being published to an actionable insight dropped from days to real time.

For theBlue.ai this was the full AI and engineering partnership for a new product company: not a feature added to something existing, but the technical foundation the company runs on.

Our close collaboration with theBlue.ai was key to unlocking the potential of AI for our needs. From the initial proof of concept through to the product launch, their structured and agile approach kept things efficient and on schedule. Early customer feedback has been incredibly positive.
Marc-Angelo Bisotti CEO, Policy-Insider.AI

Questions about this project

Political monitoring and analysis platform. Millions of political documents published in complex, domain-specific language across many institutions and languages, and no automated way to collect them, structure them and get the relevant part to the person who needed it.

Political documents processed across several languages: Millions. The full journey from proof of concept to a launched product: PoC to prod. Collection, structuring and analysis in one pipeline: LLM + NLP. Personal dashboards with AI-generated summaries: Real time.

NLP, LLMs and data architecture. Technology used: Large Language Models, Natural Language Processing, Multi-language processing, Data architecture, Document classification, AI summarisation, Personalised dashboards.

Technology used

Large Language Models Natural Language Processing Multi-language processing Data architecture Document classification AI summarisation Personalised dashboards

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