80%
Reduction in manual analysis, speeding up research time
Global consultancy firm cuts analysis time by 80%, accelerating strategy with AI
Developed InsightVault™, an LLM-powered platform to transform research and analysis, enabling a global payments and fintech consultancy to deliver data-driven strategies for clients faster.
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100%
Data coverage, analyzing all of the unstructured data simultaneously
5x
Faster insight generation, decision-making, and consulting workflows
Client Overview
Edgar, Dunn & Company is a global strategy consultancy firm specializing in payments and digital financial services, serving banks, payment networks, fintechs, and enterprises across 45+ countries. It is widely recognized for its expertise in areas such as omnichannel payment optimization, M&A advisory, and litigation support, delivering actionable, data-driven strategies in a rapidly evolving fintech ecosystem.
The firm relies heavily on analyzing large volumes of qualitative data to develop client strategies. Recognizing the limitations of manual analysis at scale, it aimed to leverage AI and Large Language Models (LLMs).
Challenges
Building an AI-powered platform to automate analysis of interview transcripts required addressing several challenges associated with processing large volumes of unstructured qualitative data while ensuring accuracy, reliability, and usability for consulting workflows.
Manual analysis bottlenecks
Processing and analyzing hundreds of interview transcripts, reports, and other research documents required designing a system that could replace sequential, effort-intensive manual workflows. The challenge was to enable parallel processing of large datasets while maintaining speed and consistency for downstream insight generation.
Unstructured data challenges
Transforming thousands of pages of qualitative dialogue into a structured, searchable knowledge base required robust mechanisms for data preprocessing, context extraction, and thematic mapping. The challenge was to preserve the nuance and richness of qualitative insights while making them easily accessible.
Limited cross-dataset visibility
Identifying patterns, correlations, and trends across multiple transcripts and data sources required building capabilities to analyze data holistically at scale. At the same time, ensuring high accuracy, minimizing bias, and avoiding missed insights were critical to generating reliable, evidence-backed recommendations.
Solution
Simform designed and built InsightVault™, an LLM-powered platform on Microsoft Azure to transform how large volumes of unstructured research data are analyzed, enabling faster and more scalable insight generation for consulting workflows.
LLM-powered transcript analysis with RAG
Implemented a Retrieval-Augmented Generation (RAG) architecture using GPT-4o with vector embeddings and context retrieval to generate accurate, grounded responses while preventing hallucinations with guardrails.
Scalable ingestion and preprocessing pipeline
Built a data pipeline to ingest, clean, and normalize large volumes of transcripts and research documents. This ensures consistent processing across varied formats and enables scalable analysis across engagements.
Chat-based natural language querying interface
Developed a conversational interface that allows consultants to query data using natural language and receive insights in text, tables, and charts. This eliminates manual review and accelerates insight discovery.
Azure-based architecture with secure access
Deployed the platform on Microsoft Azure using Azure OpenAI, Cognitive Search, App Service, and Key Vault. Implemented role-based access and data isolation to ensure compliance and full control over sensitive data.
Business Impact
The InsightVault™ platform transformed how consulting teams analyze and utilize large volumes of research data, enabling faster, more consistent, and evidence-driven decision-making.
Reduced analyst fatigue and bias
Automated analysis and interactive querying across large datasets significantly reduced reading, tagging, and synthesis effort. This minimized cognitive load and bias, allowing consultants to focus on interpretation and strategic decision-making rather than data processing.
Greater consulting efficiency
Consultants can move from the discovery phase to strategy significantly faster by analyzing research inputs in minutes instead of weeks. This accelerated project timelines and increased the firm’s ability to take on more engagements without increasing headcount.
Enhanced client recommendations
By mining unstructured data across transcripts, workshops, and client interactions, the platform enabled precise identification of customer pain points and their linkage to business processes. This allowed consultants to deliver more tailored, evidence-backed, and forward-looking recommendations.
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