Agentic operations for data-intensive industries.
Knowledge platforms, agentic operations, conversational data analysis, and content workflows — engineered for telecom carriers, media networks, and operators handling data and customer volumes at scale.
- ▸Data science requests overwhelm specialist teams — every query becomes a multi-day analysis ticket.
- ▸Customer service queues are dominated by repetitive intent that doesn't need a human first.
- ▸Content and metadata workflows require classification at scales that manual review can't reach.
- ▸Internal knowledge is scattered across SharePoint, Confluence, drives — search returns documents, not answers.
- ▸Forecasting and optimisation models exist but don't reach the operators who would act on them.
The capabilities most often deployed here.
Conversational data science
Agentic data assistants — LangGraph on Azure — for self-service exploration, modelling, and reporting.
RAG knowledge platforms
Secure retrieval over SharePoint, Confluence, databases — respecting your access controls.
Agentic operations
Multi-agent flows for service, billing, ops — execution, not just chat.
Content classification & routing
Intelligent processing for catalogue, content, and metadata workflows.
Demand & capacity forecasting
Custom models tuned to your traffic, seasonality, and channel dynamics.
Managed operations
Operating AI in regulated telecom and media environments under SLAs.
Useful entry points into IDEA Foundation’s work for this sector.
Customer story · Data Science Agent
LangGraph-powered conversational data assistant — days to minutes for business-user analytics.
Customer story · BD Agent
Real-time AI for business development — LiveKit, Twilio, AWS.
Applied & Generative AI
Forecasting, RAG, agents, managed operations — for data-intensive sectors.