The Analyst AI Workbench Revolutionizing a Geopolitical Risk Consulting Firm

Background

The client is a geopolitical intelligence firm. Its competitive edge is a decades-deep network of subject matter experts (SMEs) and an informed view of what counts as signal in a noisy world. The firm runs two key business arms on that foundation: custom research for corporations and investment firms, and a subscription product covering the geopolitical situations that shape global markets.

Beyond Data Consulting was engaged to:

• Build a data strategy roadmap.
• Build the company's data foundation, according to the strategy roadmap.
• Build the AI and analytics operating system of the organization thereupon.

The mandate was clear: scale the firm's revenue with AI by operationalizing intelligence that already lived inside the org, but lay dormant.

The Challenge

The firm knew where it wanted to go — an AI-led future where its proprietary intelligence powered scalable products — but the path there was blocked by technology and organizational obstacles:

• Software bloat, data sprawl, and no single source of truth. Four AI notetakers across the org; no central repository linking key business data: client profiles, client deliverables, projects, SME conversations.
• The firm's most valuable intelligence was single-use only. Every paid SME conversation, every client delivery, and all intelligence research was used point-in-time, never to be used by the firm again.
• General-purpose AI tools were the wrong shape. Off-the-shelf chat tools didn't know what data the firm trusts, couldn't separate proprietary expert insight from open-web news, and started cold every session.

Our Approach

0. Setting the Foundation

Before any code was written, Beyond Data ran a 6-week Data Audit to understand the firm's data, business processes, and business and AI ambitions, with the ultimate goal of producing the roadmap that everything else would build on.

The audit's central finding: the firm was already most of the way to a usable data foundation, but the remaining distance was the hard part, and it was as much organizational as technical. The firm's scrappy, fast-moving culture was a genuine strength, but at their size it had begun to work against itself — fragmented tooling, inconsistent data hygiene, and siloed initiatives were quietly undermining the very AI ambitions leadership was excited about.

We delivered and executed a sequenced Data Foundation roadmap that married leadership's product vision to the analysts' day-to-day reality: establish a single source of truth and migrate all data and systems thereto, get the highest-value data (SME engagement transcripts, client deliverables) structured and accessible, put lightweight governance in place, and only then build AI products on top.

1. Designing a Platform That Compounds, Not a Point Solution

Beyond Data Consulting designed "Core" — a foundational platform rather than a single app, a shared layer where all business data is modeled once and exposed to every internal product, so each new product starts further along instead of rebuilding from scratch. Fundamentally, it is a knowledge graph structured around the discrete observations, assessments, and forecasts extracted from each expert conversation and client deliverable, every one linked back to singularly modeled sources, people, places, and topics. The result is institutional memory that compounds with every conversation recorded and every piece of work executed, rather than evaporating after the project ends.

2. The Analyst AI Workbench

On top of that foundation, Beyond Data built the application analysts actually use: a chat-style workspace that combines the firm's proprietary content, its curated sources, and real-time web research into cited, evidence-grounded answers, with every answer explicitly and clearly cited. It launched across the full organization in April 2026 and immediately moved into ongoing feature development — new trusted content sources, AI intelligence enhancements, and analyst workflow features — with ongoing feature requests feeding straight back into the roadmap.

Results

• 1,000+ hours per year of client delivery work saved. Analyst time previously spent collecting intelligence and coordinating with SMEs is now spent on higher-leverage work, raising the firm's effective capacity without raising its headcount.
• 500+ hours of SME calls operationalized per year. Every paid expert call that the firm conducts is now ingested, structured, and made searchable, turning a recurring cost center into a compounding asset.
• 100% adoption across the full 50-person organization. Every analyst, every day, querying the same compounding knowledge base.
• 1 new Revenue Stream created. The underlying "Core" data foundation has unlocked a new revenue stream (intelligence API as a Service)
• Multiple AI-native use cases unlocked, including the capability for AI to answer clients' questions automatically.