A research and analytics organization struggling with fragmented data access and inconsistent use of large language models was able to significantly accelerate insight generation. Blackstraw helped them by bringing everything together on a single, LLM-driven market intelligence platform. As a result, analysis cycles became 40–60% faster, insights were more consistent across teams, and decision-makers could access both structured and unstructured data in one place without adding operational complexity.
The client’s research and analytics workflows depended on multiple disconnected data sources including databases, reports, surveys, APIs, and external content. Analysts often recreated prompts manually, combined insights, and experimented inconsistently with different LLMs. This approach led to varying outcomes, slower analysis cycles, and limited use of existing knowledge. As the use of LLMs grew, the organization needed a reliable and scalable way to implement AI across research workflows seeking a solution that could standardize prompt behavior, securely integrate enterprise data, and improve performance and cost while accommodating different LLM providers.
Blackstraw worked with the client to modernize their market intelligence workflows using an enterprise-ready LLM orchestration framework.
Unified LLM Mesh Platform: Implemented a centralized LLM Mesh that orchestrates intent-based queries and manages LLM interactions across research workflows.
Research Bot with Reusable Prompts: Deployed a Research Bot capable of executing standardized, reusable prompts with dynamic variable embedding and directives to ensure consistent analytical outcomes.
Multi-Source Data Integration: Integrated structured and unstructured data from databases, reports, surveys, APIs, and external sources into a single AI-driven analytical experience.
Multi-LLM Support: Enabled seamless use of multiple LLMs, including OpenAI and Azure OpenAI, allowing flexibility in model selection without changing workflows.
Performance and Cost Optimization: Built intelligent caching and parallel execution into the platform to reduce latency and control inference costs at scale.
Faster Insight Turnaround: Reduced research and analysis cycles by 40–60%, enabling quicker responses to business questions.
Consistent, Governed AI Outputs: Standardized prompt behavior and intent modeling improved reliability and trust in LLM-driven insights.
Scalable Research Automation: Supported analytics, chatbots, and knowledge management use cases without additional operational overhead.
Cost-Efficient LLM Adoption: Lowered latency and inference costs through smart caching and optimized execution strategies.
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