Empower your investment decisions with advanced analytics focused on Ukraine

How Our Systems Deliver It

• Prompts, agent roles for our MASs and retrieval logic are designed from our multiyear Ukraine-specific experience and insights, producing an enhanced reasoning logic at the core of proprietary RAR engine for every domain.
• Addressing inherent uncertainty in questions marked by variability, such as war outcome analysis, by prompting a scenario analysis and a curated ensemble of generated responses (also known as 'prediction fusion').

Why General-Purpose LLMs Fall Short

General-purpose LLMs rely on generic prompts and opaque internal heuristics, so reasoning paths are neither transparent nor domain-optimised. Each new thread starts “fresh”, so logical decomposition, factor weights and answer depth drift from run to run.

General-purpose LLMs return one stochastic forecast per call; users must loop and post-process themselves.

How Our Systems Deliver It

• Pre-tuned, multi-agent, consistent multi-step automated workflows handling discrete tasks such as source selection, summarization, critical review, and formatting.
• Memory management system that caches and recalls intermediate outputs and previous generations, ingests relevant cross-domain generated insights.

Why General-Purpose LLMs Fall Short

General-purpose LLMs’ reasoning logic is not consistent, lose earlier context, yielding divergent answers to identical prompts and forcing users to reconcile contradictions. Our systems better track past outputs and update them as conditions change - ensuring better consistency across time compared to general-purpose LLMs like ChatGPT

How Our Systems Deliver It

• Senior-analyst QA loop with a system tweaking feedback whenever an inaccuracy/hallucination is detected.
• Source-priority logic ranks input information by its recency and reliability.

Why General-Purpose LLMs Fall Short

General-purpose LLMs have no curated Ukraine knowledge base with source-priority logic or continuous human QA, so hallucinations and factual drift remain common.

How Our Systems Deliver It

• Context development with a vetted corpus of proprietary datasets, with instructions based on insights built over years of Ukraine-specific work:

• curated open sources (IFI/think-tank/bank research, Western media)
• non-public and not easily accessible data (poll data, social media scraping)
• library of our own insights

• Structuring of semi-structured data for RAG, such as pre-processing PDF reports, categorizing extracted data, and pre-parsing it into a separate database used for context enrichment.

Why General-Purpose LLMs Fall Short

General-purpose LLMs stacks index only easily-crawlable web data and cannot guarantee inclusion (or correct processing, structuring and weighting) of niche but critical sources. Our approach gives us a contextual advantage over general-purpose LLMs trained on broad global corpora and still lacking understanding of local (and often non-public) context.

How Our Systems Deliver It

• Use of API connectors with word processing applications.
• Dedicated AI agents generate layout-consistent visuals and tables for every report.
• Use of ‘few shots’ (exemplary prompts) technique to achieve consistent formatting and explainability.

Why General-Purpose LLMs Fall Short

General-purpose LLMs return snippets, files with truncated processed data, and/or erratic illustrations that users must post-process, format and/or standardise by hand, risking inconsistencies.