CTRL+F à la Poubelle
Western democracies pride themselves on transparency. The policy ecosystem depends on the data democracies produce, but struggles to process it efficiently. The ecosystem has a search problem.
(policy AND analysis) NOT efficient
Western democracies are data factories. Governments publish thousands of pages across parliamentary proceedings, regulatory filings, consultation responses, and policy documents. Think tanks and academic institutions release reports and studies. This torrent of information should be democracy's greatest asset; a rich foundation for evidence-based policymaking and informed public debate. Instead, it has become an overwhelming flood that threatens to drown the very insights it contains, and give rise to disinformation instead. Critical connections between disparate pieces of information remain hidden, important developments get lost in the noise, and the policy ecosystem finds itself making choices based on incomplete pictures simply because the relevant information exists but cannot be found efficiently.
The ecosystem has a search problem, and it’s called keyword matching. This crude approach to information retrieval permeates the entire policy sector. Analysts scan documents using CTRL+F. Software tools allow users to input a few key terms, and the system dutifully returns any datapoint that contains those exact words. It's simple, predictable, and works perfectly if you're looking for very specific information; say, any mention of "GDPR" or "carbon tax". But this approach fails spectacularly for almost every other use case. The reason is deceptively simple yet profoundly limiting: synonymy. String matching only matches the very string you specify, and nothing else. If you're tracking discussions about AI but only search for "artificial intelligence", you'll miss conversations about "machine learning" or "neural networks". Each of these terms could be referring to essentially the same policy concerns, but traditional keyword matching treats them as completely unrelated.
Policy and regulatory compliance are notoriously subtle and complex areas where context, nuance, and interconnections matter enormously. Yet the sector relies on search methods that are reminiscent of the early days of Google search, with its Boolean operators, and strip away precisely these qualities. It's an obvious bottleneck that hampers effective governance and informed decision-making, and the main driver why Pepijn and I decided to start Prismos.
Enter semantic search
The "prism" feature is the heart of our software, and we designed it specifically to overcome this fundamental limitation of traditional search. Prisms don't rely on string matching, but on vector search; one of the most remarkable achievements of modern Natural Language Processing. Vectors (sequences of numbers, typically anywhere between a couple hundred to a couple thousand dimensions) can encode meaning in ways that mirror human intuition about language. State-of-the-art deep-learning models will assign nearly identical vector representations to near-synonyms like "artificial intelligence" and "machine learning", while unrelated concepts like "artificial intelligence" and "European Parliament" will be assigned very different vectors.
When you create a prism, you provide a description of what you're looking for - not just keywords, but a richer explanation of the concepts, policies, or issues you want to track. This could be something like "discussions about renewable energy subsidies and their impact on the energy grid" or "debates around data privacy regulations affecting social media platforms". Prism descriptions are turned into vectors, so we’re able to understand what you really mean. We do the same thing with all the political content we process: each meeting fragment, legislative excerpt, policy document, or discussion snippet gets analysed for meaning. When we find content that matches the meaning of what you're looking for, we flag it as relevant to your interests.
This approach solves the synonymy problem elegantly. Your prism about "renewable energy subsidies" will catch discussions about "clean energy tax credits," "solar panel incentives," "wind power support programs," and "green energy funding", even if those exact terms never appeared in your original description. More importantly, it captures the subtle connections and relationships that make policy work so complex. A discussion about "battery parks" might be highly relevant to your renewable energy prism because the underlying concepts are closely related, even if the surface-level keywords seem different.
Instead of playing keyword whack-a-mole, constantly updating your search terms as new phrases emerge, you can describe what you care about in natural language and trust that the system will understand the deeper meaning. Prisms don't solve every challenge in policy monitoring, but they do address the fundamental search problem that has limited traditional approaches. Instead of worrying about whether you're missing information, you can focus on interpreting and responding to the developments and ideas that matter most to your work.
CTRL+F à la poubelle!