Client Context
A global advisory firm producing intelligence reports at scale.
The client is a global advisory firm whose clients include Big Four and top-tier consulting firms. Their nine-person consulting team produces pillar-based intelligence reports for each engagement — synthesising stakeholder interviews, market research, and internal data into structured deliverables.
The firm's value is in its analytical rigour and depth of insight. The challenge was producing that quality fast enough to meet the pace their clients expected.
The Challenge
A consulting workflow built around weeks of manual synthesis.
A global advisory firm whose clients include Big Four and top-tier consulting firms had a nine-person consulting team spending weeks manually reviewing 40 to 50 stakeholder interview transcripts per project.
Each engagement required pillar-based intelligence reports that were thorough but painfully slow to produce. The bottleneck was not a lack of talent. It was the sheer volume of unstructured information that needed to be synthesised into structured, actionable insight.
The team's best people were doing work that was necessary but non-differentiating: reading, organising, cross-referencing, and drafting from scratch before any real consulting judgement could begin.
Volume without structure
40 to 50 stakeholder transcripts per project arrived without a system to structure them for synthesis.
Weeks per engagement
Each report took multiple weeks of manual review before any real consulting judgement could begin.
Blank-page drafting
Every engagement started from scratch with no reusable intelligence layer from prior projects.
Expert time misallocated
The team's best people were reading, organising, and cross-referencing before they could do the work only they could do.
What We Built
An AI report generation engine grounded in the firm's own methodology.
60x built a custom AI report generation engine backed by an enterprise knowledge graph of approximately 10,000 ingested files across 70 projects, with 13 bespoke metadata tags designed around the firm's proprietary analytical framework.
The system ingests interview transcripts, research documents, and internal notes, then generates first-draft observations, implications, and recommendations while preserving source traceability and the client's own analytical structure.
Knowledge graph foundation
Approximately 10,000 files indexed across 70 projects in a single retrieval layer.
Firm-specific logic
13 custom metadata tags mirrored the client's own analytical framework rather than a generic template.
Traceable outputs
Every generated insight can be verified against the underlying source material.
Instead of asking consultants to trust generic AI drafting, the system was designed to reflect how the firm's senior people already structured problems, surfaced themes, and moved from raw evidence to client-ready recommendations.
The Results
From blank-page drafting to substantive first drafts in minutes.
Case Study · Advisory & Consulting
- Report drafting time: from one full week of manual work to structured first drafts generated in minutes.
- Projected capacity: from 10 to 15 concurrent projects per year without adding headcount.
- Source confidence: every AI-generated recommendation links back to original evidence.
The knowledge graph does not stop being useful after the first report. It continuously ingests new documents as they land, so every new project, interview, and internal brainstorm makes the system richer and more context aware. Leadership now views the platform as essential infrastructure rather than a point solution — with plans already underway to extend the same foundation across executive search, business development, and internal operations.