Splashlake AI Companion

Solving Scientific Challenges with Splashlake AI Companion

Splashlake offers a common way to access data across multiple source systems, without having to migrate. This offers a 360° view on experiments and their context. Permissions and identity ensure access control, even when using prompts. Exposing the data to large language models and AI agents leads to universal access to lab data.

Questions related to any aspect of data stored in Splashlake can be answered through LLM exposure:

  • Operational questions related to inventory, instrument status, workload and more
  • Scientific questions such as formulation guidance, chromatography peak drift analysis, similarities across chemical structures, or the optimum environmental conditions for a test

Splashlake’s unique ability to consolidate a variety of data types across multiple sources provides scientists with an unrivalled opportunity to answer questions that could seriously advance their business and scientific operations. The solution delivers a productized approach to integrations and data transformations, enabling true scalability built on a foundation of robust data governance. We work with customers to understand key business objectives and deliver an AI Companion solution to support their business both today and into the future.

Contact us to discuss how Splashlake can support your organization in gaining transformative knowledge through exposing data to LLMs.

Data Value Creation Using Large Language Models

By exposing scientific data to Large Language Models, organizations can realize a previously untapped level of knowledge. As well as enabling significant commercial advancements by guiding scientists with additional clarity or expediting decisions through the product discovery and R&D processes, LLMs are able to deliver business intelligence to support continuous improvement across their people, processes, instruments and systems. These productivity and efficiency gains can mean improved time to market, faster delivery to consumers/patients, and improved product quality. Often, the value that scientific organizations can realize through AI can be limited due to:

  • Only querying datasets from LIMS or individual data sources at a time. Although this can provide significant advancements, the ability to feed data from multiple sources brings additional insurmountable insights.
  • The type of data being restricted to results, and not taking into account metadata, time series data or chemical structures. This can include information from environmental sensors, adding rich context to results.
  • A lack of scalability beyond the proof of concept. Projects often fail when scaled due to poor data quality, API and code challenges, and unclear business aims, leading to stalled, costly, and ineffective deployments.

Splashlake works with customers to prioritize and secure data governance from the ground up, providing a solid base to build a robust AI and machine learning strategy, with tangible and achievable business objectives.