When we first built Policy-Insider.AI, our goal was clear: to give professionals the deep dive at their fingertips— instant access to the complete, official record of European and national policy developments, searchable across institutions, topics, and time.
The platform now includes more than seven million policy documents, and we continue to add around 15,000 pages per day, usually within hours after publication. The result is one of the most comprehensive institutional databases in Europe, covering past and present developments across all policy fields and governance levels.
But information alone is not enough.
As the database grew, so did one question: how can we make this wealth of verified data more accessible to professionals who don’t have time for daily searches?
The answer is Insights by Policy-Insider.AI— a new service of AI-curated, human-audited newsletters designed to make policy intelligence both trustworthy and scalable.
Can an AI be trusted to explain policy developments?
In programming, design, and data science, the answer is already yes.
But in public affairs and government relations, manual monitoring still dominates. Professionals rely on staff or agencies to read institutional websites, copy information into spreadsheets, and write summaries. It is slow, repetitive work — yet people keep doing it because they can trust it.
After twenty years in EU and pan-European public and government affairs, I understand why. Automation in this field has often disappointed: hallucinated content, broken links, or incomplete coverage erode confidence quickly. The challenge isn’t AI itself — it’s how AI is used.
Models such as GPT or Gemini are impressive, but they are generalists. They excel at creative generation, brainstorming, or language style transfer.
Public-policy intelligence, however, requires something else: accuracy, reproducibility, and verifiable sources.
The newest generation of language models is brilliant at agreeing with the user — they tend to confirm expectations and smooth over uncertainty. That makes them sound polite, but not necessarily right. Unless explicitly designed and instructed to stay factual, these models produce flattering summaries, not critical analysis.
That is why we built Policy-Insider.AI around a different principle: factual grounding in institutional documents. Every piece of information starts from an official publication and is processed through a transparent retrieval-augmented generation (RAG) framework. The AI cannot improvise where no source exists.
Our first series of newsletters, PQ Insights, is based on the complete set of Parliamentary Questions (PQs) and answers published by the European Commission.
Instead of relying on keyword searches or committee filters, the system performs an individual relevance assessment of every single PQ. This enables coverage of all political signals — including those that traditional monitoring might miss because they don’t fit pre-defined categories.
Because the entire PQ corpus is stored in our database, there is no dependence on Google searches or manual scraping from institutional sites. The system can assess thousands of documents daily, in multiple languages, and update its summaries within hours of publication.
And PQ Insights is only the beginning.
We are currently developing and testing additional newsletters that leverage the same institutional foundation across:
- Vertical datasets, such as healthcare, energy, and digital policy, and
- Horizontal datasets, such as advisory bodies, agencies, consultations, and Member-State-level developments.
Each follows the same logic: verifiable sources first, AI synthesis second, human review always.
Our confidence in automation rests on a hybrid audit system.
Every newsletter output goes through two layers of quality control:
- AI reviews for structural consistency, completeness, and data integrity.
- Human audits for tone, contextual relevance, and factual precision.
These reviews are conducted regularly as part of internal audit cycles, combining algorithmic checks with editorial oversight. The result is machine precision anchored in human judgment.
It is this interplay that produces trust. The AI guarantees scale and reproducibility; humans ensure that the result meets professional standards. One without the other would fail the test of credibility.
When I speak of “sustainability,” I mean continuity of quality over time.
Traditional policy monitoring depends on people — their experience, intuition, and institutional memory. When key staff move on, knowledge gaps appear, and processes must restart from scratch.
We take a different approach. Every report generated through Policy-Insider.AI is reproducible— it can be regenerated in the same form, with the same source structure, by any authorized user, at any time.
As accuracy and stability improve, specific newsletters gradually reach a stage where full automation becomes possible, allowing our team to focus on developing new coverage areas rather than rewriting existing ones.
This reproducibility is what makes AI-driven policy monitoring sustainable: the quality no longer depends on who happens to be in the office.
Our workflow always begins with institutional data.
Only after a policy development is identified do we extend the context with verified web sources— media statements, stakeholder reactions, or academic commentary.
This sequence matters: the web complements the official record, but never replaces it.
Starting from policy ensures factual grounding; extending to the web provides the broader picture professionals need. It is an additive, not substitutive, use of AI search.
Platforms like Politico or Euractiv continue to set high standards for manual policy journalism. Their editorial craftsmanship is exceptional.
But their necessary focus on a broad readership means that depth and coverage must sometimes give way to curation and selectivity.
Insights by Policy-Insider.AI fills a different role.
We provide:
- Tailored newsletters for organizations that need issue-specific monitoring, and
- Publicly available newsletters that demonstrate our analytical capabilities and showcase the underlying data infrastructure.
Both rely on the same audited AI-human system, offering precision, transparency, and reproducibility at scale.
Trust is often treated as an emotion; in our work, it is a measurable outcome.
It can be tested, reproduced, and audited.
Our readers can verify every link, trace every source, and regenerate every report.
That transparency is the foundation of reliability — and the reason why we believe Insights by Policy-Insider.AI represents not just an evolution of our platform, but a new standard for how AI can serve the public-affairs profession.
AI will not replace policy analysts. It will amplify them.
By taking over the mechanical parts of monitoring, it allows professionals to focus on interpretation, strategy, and decision-making.
But this only works if the underlying technology is disciplined — if it knows when to stop predicting and start verifying.
That is what we strive for at Policy-Insider.AI: AI that respects facts more than assumptions, and transparency more than performance.
AI can read everything, but understanding still requires direction.
At Policy-Insider.AI, our mission is to provide that direction — through verifiable data, reproducible processes, and transparent editorial standards.
If you want to see what responsible, auditable automation looks like in public affairs,
subscribe to our newsletters or book a call.
I will be glad to show you how trust, transparency, and technology can finally work together.
—
Dr. Marc-Angelo Bisotti
Founder & CEO, Policy-Insider.AI
With more than 20 years of experience in EU and pan-European public and government affairs, he is dedicated to building verifiable, AI-driven policy intelligence that professionals can trust.
