The Friction
"Analysts and decision-makers are drowning in unstructured data (PDFs, legacy docs), leading to slow research cycles and missed strategic insights."
A secure 'Research Commons' that ingests proprietary data and uses 'Agentic RAG packs' to ground AI answers in fact, preventing hallucinations.
Philosophy: Security-first, groundedness-driven.
Analyst Context Acceleration (ACA)
Ingests and embeds domain data to speed up report production.
Digital Twin Simulation
Allows clients to run scenarios on their own data within a secure portal.
Grounded QA
Citations and source trails for every AI-generated insight.
Neural Path
Ingestion (S3)
Vectorization (OpenSearch)
Agentic Retrieval
Synthesis (LLM)
Technology Stack
Impact
50% reduction in research production time; 40% reduction in cycle time for benchmark tasks.
Further reading
From Chatbots to Agentic AI: Why Orchestration is the New Standard
The shift from reactive chatbots to proactive agentic systems is not an upgrade—it's a fundamental architectural rethink. Here's why orchestration is the only path forward for enterprise AI.
Understanding Domain Agent Taxonomies: Industry → Process → Function
Why monolithic AI agents fail at enterprise scale—and how a structured three-tier taxonomy (industry, process, function) delivers the specificity and reliability that complex deployments demand.
Agentic RAG Packs: Speeding Up Context Ingestion for Global Teams
Global enterprises have massive, multilingual, multi-format knowledge bases. Agentic RAG Packs are pre-packaged ingestion and retrieval configurations that compress deployment time from months to days.