A prominent aerospace manufacturing company faced challenges in scaling AI assistants due to fragmented knowledge pipelines, inconsistent document processing, and slow knowledge retrieval. Blackstraw implemented an Intelligent Knowledge Source (IKS) Accelerator standardizing the way enterprise knowledge was gathered, indexed, and retrieved. The solution allowed engineers to access validated, context-aware knowledge quickly. This significantly cut down troubleshooting time and improved knowledge reuse among engineering teams.
Agentic AI systems and assistance are largely run on existing data; the completeness and storage of that data directly impacts the performance of these AI systems. The client dealt with several separate knowledge pipelines created by different teams using various tools and methods. This situation resulted in duplicated effort, inconsistent document-processing quality, slow retrieval, and a higher risk of inaccuracies due to poor semantic indexing. As AI adoption grew in engineering and operations, the organization required a reliable way to ingest, process, and deliver trusted knowledge to AI systems without rebuilding pipelines for each use case.
IKS Accelerator: Implemented a configurable, ready-to-launch knowledge pipeline that standardized ingestion, processing, indexing, and retrieval of enterprise knowledge.
Multi-Modal Document Processing: Deployed a custom document processing engine capable of extracting text, tables, images, and embedded content from complex technical documents with high accuracy.
Enterprise Data Adapters: Integrated native connectors for common enterprise systems, including cloud storage platforms, collaboration tools, data platforms, and IT service systems.
Low-Latency Semantic Retrieval APIs: Enabled fast, agent-ready semantic retrieval optimized for AI assistants and multi-agent workflows.
Grounding and Hallucination Safeguards: Built in controls to improve response grounding and reduce hallucination risk through high-quality indexing and validated knowledge sources.
Faster Model Deployment: Reduced model deployment time by 60–70%, accelerating release cycles from experimentation to production.
Improved Reproducibility and Compliance: Delivered consistent, traceable ML workflows supporting audit readiness and governance requirements.
Stable Model Performance: Maintained performance reliability through continuous monitoring and proactive retraining.
Stronger Collaboration Across Teams: Improved coordination between data science and DevOps teams through standardized, automated workflows.
Scalable, Production-Ready ML Systems: Enabled the organization to deploy and manage ML models reliably across multiple environments without increasing operational complexity.
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