AI Policy Intelligence for the German Foreign Office: Turning Complex Policy Documents into Decision-Ready Insights

German Foreign Office policy officers had to manually review hundreds of UN stakeholder submissions with no way to compare positions across documents. Omdena partnered with the Data Innovation Lab to build an AI system that could search, summarize, and compare the full corpus in 12 weeks. Delivered: three summary types, a natural language policy chatbot, and stakeholder alignment heatmaps — verified with no hallucinations.

Key Outcomes

The Problem: Hundreds of Unstructured Submissions and No Tool to Navigate Them

During the UN Global Digital Compact consultation, the German Federal Foreign Office received hundreds of submissions from governments, NGOs, and private sector entities. Documents arrived in two formats — structured PDFs and Power BI dashboard entries — with no shared schema and no tool for cross-document analysis or position comparison.

Foreign policy officers had to read, compare, and synthesize this corpus under tight negotiation timelines. There was no structured way to identify consensus or divergence between stakeholder positions before entering talks.

The Partnership: Omdena and the German Federal Foreign Office’s Data Innovation Lab

Omdena collaborated with the German Federal Foreign Office’s Data Innovation Lab — affiliated with GovTech Campus Germany — over 12 weeks to build a proof-of-concept policy document intelligence system. The DIL selected the UN Global Digital Compact negotiations as the test case: a well-scoped corpus with seven defined digital policy topics and a clear end user. A 14-member Omdena data science team delivered the build.

Why Policy Document Intelligence Is Hard: Format Diversity, Diplomatic Language, and Hallucination Risk

• Inconsistent document formats: PDFs varied widely in layout, encoding, and scan quality. IBM DeepSearch could not parse several scanned submissions accurately, and no fallback OCR approach achieved adequate quality within the PoC timeline.
• Diplomatic language is deliberately convergent: K-means, DBSCAN, and hierarchical clustering all produced poor Silhouette scores on the GDC corpus. UN entities use shared diplomatic framing that suppresses surface differences between positions — a property of the data, not a model failure.
• Hallucination risk in a high-stakes context: A policy officer acting on a misattributed summary risks misjudging a negotiating partner. TruLens monitoring was built in from the outset, covering groundedness, relevance, coherence, correctness, and harmfulness across both pipelines.

Building Diplomacy GPT: Two LLM Pipeline Architectures

The project ran in two phases. Phase 1 established core retrieval and query functionality. Phase 2 replaced it with a more capable custom pipeline suited for production development.

Phase 1: LlamaIndex Prototype

A five-component LlamaIndex pipeline ingested PDFs via PyPDF, indexed document chunks using text-embedding-ada-002 in Weaviate, and surfaced responses through a gpt-3.5-turbo-1106 retrieval agent. The agent broke complex queries into subqueries before combining answers and returning them to the Streamlit UI. This served as a working prototype before committing to a custom architecture.

Phase 2: Custom ETL Pipeline

Phase 2 used Prefect orchestration and IBM DeepSearch to structure PDFs into labeled sections in 600-token chunks. gpt-4-1106-preview handled NER across six entity types — PERSON, ORG, GPE, EVENTS, DATE, LAW — selected for its 128K context window and one-third lower cost than gpt-4.

Three summary types were generated per document-topic pair at ingestion time and stored in PostgreSQL:

• Extractive: Most relevant sentences per chunk (~40% of original document length)
• Abstractive: LLM-generated paraphrase (~400 words)
• Chain of Density: 3–5 compressed sentences, iteratively dense

All summaries were embedded using text-embedding-ada-002 across four Weaviate indexes, with raw documents in S3 on AWS. Ingestion took 5–10 minutes per document. Retrieval was near-instantaneous.

Heatmap Visualization

Pairwise similarity heatmaps compared entity positions across GDC topics. Three distance metrics were tested; Manhattan distance produced the widest, most interpretable range of distinction and was selected for all stakeholder visualizations.

What Was Delivered: Diplomacy GPT — A Policy Intelligence Web Application

Diplomacy GPT was delivered as a Streamlit web application hosted on AWS EC2. Policy officers registered, logged in, and queried the full GDC corpus in natural language. The system embedded each query, retrieved relevant chunks from Weaviate, and returned LLM-generated responses with source document attribution. Three interface pages:

• Chat: Natural language queries; sourced LLM-generated responses (e.g., “How does the EU’s position differ from Microsoft’s on internet fragmentation?”)
• Comparison: Select entities and a GDC topic; all three summary types returned in a structured, downloadable CSV table
• Heatmaps: Pre-computed Manhattan distance matrices across all seven GDC topics — no statistical expertise required

Impact: A Queryable Policy Intelligence Layer Where None Existed Before

Before this project, the DIL had no queryable layer over the GDC corpus. Policy officers reviewed documents manually with no structured way to compare positions across entities. Diplomacy GPT made the full corpus queryable for the first time — designed explicitly as decision support, with all final judgments remaining with the policy officer.

The TruLens evaluation gave the DIL more than a working demo. Relevance 0.88–0.94, groundedness 0.88–0.99, correctness 0.90–0.96, no hallucination alarms. Any future production build can be measured against those numbers.

Benefits for Clients: The Data Innovation Lab and German Federal Foreign Office

• Queryable intelligence layer: A working PoC with a clean technical foundation — Prefect ETL, Weaviate, PostgreSQL, TruLens baseline — ready for production development without rebuilding from scratch.
• No technical training required: Policy officers register, query, and download comparative summaries in minutes. Heatmaps visualize position alignment without requiring statistical expertise.
• Open-source via OmdenaLore: The full pipeline is auditable and adaptable by any government agency or research institution at no license cost.

What Building Diplomacy GPT Taught Us

Standard clustering fails on diplomatic data — the language is designed to converge

K-means, DBSCAN, and hierarchical clustering all failed on the GDC corpus — poor Silhouette scores across every configuration tested. UN entities use shared diplomatic framing that suppresses surface differences between positions. That is a property of the data, not a model limitation. Knowledge graphs, which capture relationship structure rather than semantic similarity, are the more promising direction.

Pre-generating summaries at ingestion is the right trade-off for a read-many corpus”

Five-to-ten minutes to ingest per document; near-instantaneous at query time. Batch-ahead generation is clearly the right call for a corpus ingested once and queried many times.

Government PoCs need an ethical evaluation framework built in from day one

The team applied the Microsoft RAI Impact Assessment Framework and TruLens from day one — treating responsible AI as a design constraint, not a compliance checkbox. The result: a concrete harm inventory, XAI objectives, and quantified quality scores the DIL could use in their own governance process. A government agency cannot act on a PoC it cannot audit. Without this baseline, further investment is a much harder decision to justify.

What’s Next: Production Infrastructure, Expanded Corpus, and Knowledge Graphs

The PoC processed 15 documents as a deliberate scope decision. A production build needs:

• Full corpus: Ingest the complete UN GDC submission corpus
• Scanned PDFs: Azure AI Document Intelligence — tested and confirmed capable of parsing scanned submissions to structured JSON
• Knowledge graphs: A layer over the existing NER output to surface entity relationship patterns that vector similarity cannot detect
• Topic modeling: BERTopic, most promising of three approaches tested alongside LDA and NMF
• Production stack: Django/React.js UI, AWS Beanstalk for auto-scaling, parallelized summary generation to reduce the 5–10 minute ingest window

Key Achievements 

Three-mode LLM summarization system evaluated for diplomatic use

Extractive, abstractive (~400 words), and Chain of Density (3–5 sentences) — generated per entity-topic pair at ingestion time. TruLens evaluation: abstractive index achieved relevance 0.94, groundedness 0.88, correctness 0.96.

Stakeholder position heatmap visualization for negotiation preparation

Manhattan distance matrices across seven GDC topics, showing entity alignment at a glance. Selected over cosine similarity and Euclidean distance after adversarial testing confirmed the widest interpretable range of distinction.

Fully evaluated RAG pipeline ready for beta testing

Phase 2 pipeline — Prefect ETL, IBM DeepSearch, gpt-4-1106-preview NER, Weaviate, PostgreSQL — evaluated across twelve TruLens metrics. No hallucination alarms flagged.

About This Project

Omdena partnered with the German Federal Foreign Office’s Data Innovation Lab over 12 weeks to build a proof-of-concept AI policy assistant for the UN Global Digital Compact negotiations. A 14-member team delivered a two-phase LLM pipeline combining Prefect-orchestrated ETL, IBM DeepSearch document structuring, three-mode summarization, Weaviate vector retrieval, and stakeholder heatmap visualization, evaluated using TruLens across twelve quality metrics..