From generic LLMs to
Thurro Intelligence
See how Thurro’s private AI architecture overcomes the accuracy, security and context gaps that limit open language models in institutional settings.
Why generic LLMs fail institutionsOpen large language models pose critical limitations for financial and institutional intelligence | The Thurro advantageA private, AI-powered intelligence layer, tailored for your organisation |
 Overconfident hallucinationsGeneric models generate false information when data is missing, presenting speculation as fact without warning |  No hallucinationsPrecise, contextualised information grounded securely in your verified data sources |
 Slow and shallow analysisGeneric models generate false information when data is missing, presenting speculation as fact without warning |  Unified intelligence layerMarket, proprietary & alternative data combined into one layer |
 Data exposure risksOpen training loops & lack of enterprise-grade controls |  Enterprise-grade privacyDeployed in your cloud; never used for model training |
 Garbage-in, garbage-outInconsistent source quality = unreliable decisions |  Tailored, reliable outputsCustom reports, charts & models tuned to institutional needs |
From generic LLMs to
Thurro Intelligence
See how Thurro’s private AI architecture overcomes the accuracy, security and context gaps that limit open language models in institutional settings.
Thurro AnswersPurpose-built for decision-makers | Generic AI chatbotsGeneral-purpose conversation |
 Curated DataLakehouseBuilt on 800+ authoritative sources, 25 million+ daily data points, 5+ years of Indian financial and alternative data |  Web-scraped trainingTrained on general internet data with limited financial or corporate depth and currency |
 Decision-ready insightsDelivers structured analysis, not shallow summaries. Every answer includes citations and sources |  Surface-level responsesProvides conversational answers without deep analysis or reliable source verification |
 Unified data analysisCombines structured and unstructured data: filings, transcripts, alternative data, market intelligence |  Limited data integrationCannot access real-time financial data, filings, or proprietary business intelligence |
 ReliabilityTransparently states what it does not know, offering only accurate, available data |  Hallucination riskMay generate plausible sounding but incorrect information, especially for specific queries |
 Domain expertiseOptimised for corporate strategy, M&A, equity research, and investment analysis |  General knowledgeBroad but shallow understanding across domains without specialised research capabilities |
 Workflow efficiencyStreamline your workflow with automated portfolio analysis (watchlists, templates), organized research notebooks, and efficient background processing for large-scale data |  Repetitive dragInefficient workflows often stem from manual, repetitive querying, a lack of systematic portfolio monitoring, inconsistent analysis frameworks, and the need for real-time interaction with |
Data governance is the key in getting AI right
Data sources
Identify sources
Keep data pipelines clean
Track updates
Chunk data
Different formates (xls, pdf, doc, audio)
Embed data
Stamp data for effective retrieval
Put in vector db for easy fetch later
Retrieval augmented generation (RAG) process
Identify the embedded chunks that are relevant to respond to a question
Run response via Claude 3.7
Send only the relevant chunks to a LLM for making the response human-ready