.svg){kind=small}
.svg)
.svg)
The SILREAL Policy Intelligence AI Agent
SILREAL has created an AI agent tailored for regulated industries (like pharma and healthcare) to navigate the complexities of EU and cross-border policymaking.
SILREAL built a Policy Intelligence AI Agent that turns fragmented EU (and cross-border) policy noise into defensible, execution-ready decisions for regulated industries—especially pharma and healthcare.Government affairs teams face a structural problem: early policy signals emerge across dozens of institutions and languages, but uncertainty is implicit, credibility varies, and “what to do next” is often manual and slow. The result is delayed positioning, reactive stakeholder management, and missed early warning windows.Our AI Agent solves this end-to-end. It continuously ingests and structures policy and regulatory signals from public sources, classifies them by topic and business impact, and makes uncertainty explicit through transparent scoring. For each priority signal, it generates a practical playbook: recommended actions, accountable owners, deadlines, and progress tracking—so teams move from monitoring to coordinated execution.The platform provides a single dashboard for signal triage, exposure analysis (pressure / controversy / uncertainty indicators), and cross-jurisdiction comparisons. This compresses analysis cycles from days to hours, improves traceability for leadership, and helps organizations act earlier with lower reputational and regulatory risk.In short: a pragmatic agentic AI system that converts policy complexity into measurable operational speed and strategic leverage.
Organizations in regulated industries are exposed to policy-driven value swings—pricing, market access, HTA, data governance, workforce rules—yet their monitoring and response processes are often artisanal. Teams rely on manual scanning, scattered spreadsheets, and informal interpretation, which creates latency and weak governance.The client problem is not “lack of information”; it is lack of a defensible decision system: a repeatable way to capture signals early, compare jurisdictions consistently, quantify uncertainty, and translate insights into controlled execution.Without this, leadership receives late or conflicting briefs, internal alignment is slow, and opportunities to shape outcomes (consultations, alliances, stakeholder engagement) are missed. The cost shows up as delayed action, higher compliance and reputational risk, and unnecessary headcount growth to keep up with policy volatility.This project addresses the gap by providing a structured signal stream, explicit uncertainty scoring, exposure dashboards, and actionable playbooks that standardize how the organization detects, decides, and acts on policy change.
AI Solution Implemented (technical details)The project implements an agentic Policy Intelligence system that converts public-policy sources into structured signals, scored risk exposure, and execution-ready playbooks. 1. Ingestion & Source Management: A curated source registry (EU and cross-border public institutions, regulators, parliamentary materials, consultations, press releases, etc.) feeds a pipeline that captures new items on a recurring schedule. Each item is stored with provenance metadata (source, timestamp, jurisdiction, URL/reference). 2. Extraction & Normalization (LLM-assisted): An LLM extraction agent converts unstructured texts into a normalized “Policy Signal” schema (topic, affected product/therapy/business area, policy instrument, timeline, stakeholders, directionality, and evidence snippets). A deduplication step clusters near-duplicate items across sources. 3. Retrieval-Augmented Intelligence (RAG): For each signal, retrieval pulls relevant prior signals, internal guidance notes, and jurisdiction baselines to ground analysis and reduce hallucinations. Outputs include citations back to the underlying source fragments. 4. Scoring & Exposure Analytics: A scoring module produces interpretable scores (e.g., credibility, restriction level, uncertainty/maturity, urgency) and aggregates them into an exposure view (pressure / controversy / uncertainty indicators) for cross-jurisdiction comparison and prioritization. 5. Playbook Generation & Tracking: A response agent generates action playbooks (recommended positioning, next actions, owners, deadlines, and escalation paths). The system tracks status and progress to move from monitoring to execution. 6. UX: A dashboard supports triage (signal feed + filters), deep-dive views (signal + evidence + scoring), and playbook management (tasks, ownership, progress).
This project is built to deliver measurable cycle-time reduction and lower coordination cost in policy/regulatory monitoring and response. The KPIs below are the quantifiable outcomes we track and report in a client pilot/rollout: • Time saved on monitoring & triage: from [Baseline] hrs/week to [New] hrs/week per analyst (Δ [X] hrs/week, [Y]% reduction). • Time-to-brief (signal → leadership-ready summary): reduced from [Baseline] days to [New] hours/days (Δ [X]). • Coverage expansion: [#] public sources monitored across [#] jurisdictions, generating [#] structured signals/week with provenance. • Signal quality / governance: [%] of priority signals include explicit uncertainty + credibility scoring and source traceability (audit-ready). • Execution conversion: [%] of priority signals converted into playbooks within [X] hours, with [%] tasks assigned (named owner + deadline) and [%] completion within SLA. • Issue leakage reduction: [#] missed / late escalations decreased vs baseline period [Baseline window].ROI model (financial):Annual value ≈ (hours saved/week × fully loaded hourly cost × 52) + avoided external research spend + risk avoidance proxy (e.g., reduced escalation/firefighting hours).Example calculation fields: hours saved/week = [ ], hourly cost = [ ]€, annualized savings = [ ]€.
This project deserves to win because it demonstrates what most GenAI initiatives fail to deliver: a completed, operational system with traceable outputs that measurably accelerates decisions in a high-stakes, regulated domain. 1. End-to-end impact, not a demo. We built a full workflow—from public-source signal capture to structured analysis, exposure visibility, and playbooks with owners and deadlines. It replaces fragmented monitoring with a repeatable execution engine. 2. Defensibility by design. Policy intelligence is useless if it can’t be trusted. The system makes uncertainty explicit (credibility/maturity/impact scoring) and preserves provenance, enabling leadership to act with transparent confidence rather than “black box” outputs. 3. Agentic execution + UX that changes behavior. The value is not just summarization; it is conversion of signals into accountable actions. The UX supports triage, deep dives, and progress tracking so adoption is operationally natural for policy and government affairs teams. 4. High-value domain complexity. Applying agentic RAG and scoring to multi-jurisdiction policy is harder than typical GenAI use cases (customer support, marketing copy). We handle ambiguity, conflicting sources, and timelines while keeping outputs auditable. 5. Commercially meaningful outcomes. The platform is designed to compress cycle time (days → hours), expand coverage without headcount growth, and reduce regulatory/reputational risk through earlier, coordinated action.In short: this is GenAI used where it matters—turning policy complexity into faster, safer decisions with measurable operational leverage.
.svg){kind=icon}