L7 Notebooks
Time to challenge the ELN status quo.
Traditional methods like paper-based lab notebooks and spreadsheets are increasingly insufficient due to their well-known logistical, scientific, and data-related challenges. Annotating experiments, compiling results, and maintaining diligent records are labor-intensive, time-consuming, and prone to errors. Furthermore, the advent of advanced technologies, such as high-throughput and high-frequency measurement devices, has significantly increased the volume and complexity of data produced in R&D studies and experiments. The outdated paper and spreadsheet paradigms are ill-equipped for the realities of modern R&D. They lack the scalability needed to meet current business and scientific demands, offer no structure for data analysis in downstream processes, fail to integrate with broader laboratory systems, hinder collaboration, and complicate the retrieval of historical data.
Introducing L7 Notebooks.
L7 Notebooks provides a robust solution to the numerous challenges faced by life sciences research organizations, particularly those requiring streamlined operations, enhanced flexibility, and the capacity to swiftly adapt to market fluctuations.
Streamlined protocol transfer
Efficiently transition protocols from research to production, minimizing tech transfer times and ensuring consistent data recording and analysis.
Enhanced data security + access control
Implement role-based user access and robust data security measures to facilitate secure cross-departmental collaboration.
Standardized lab processes
Create reusable templates that standardize laboratory processes, enhancing productivity and precision.
Efficient data reporting
Rapidly query and report laboratory data for presentation to customers, managers, and other stakeholders.
L7 Notebooks outperforms traditional ELN point solutions.
L7 Notebooks is an integral component of the L7|ESP Unified Platform, which consolidates protocols and notebook library data within the same database that houses LIMS, inventory data, scheduling tools, and various other applications. This system leverages the existing workflow chain configurations of L7|ESP to automate task orchestration seamlessly. For instance, when a benchtop researcher logs a sample collection in Notebooks, the system automatically schedules the corresponding analytical procedure in L7 LIMS and then retrieves the results back into Notebooks for analysis and interpretation.
Dive deeper into the platform.
L7|SYNAPSE™ Datasheet
L7|SYNAPSE™ is the agentic AI layer of L7|ESP®, connecting natural-language interaction with governed scientific data, approved documentation, user permissions, and workflow execution. Designed for regulated life sciences environments, it grounds responses in an organization’s own knowledge and operational context. This datasheet provides an introduction to L7|SYNAPSE and its capabilities for retrieving information across L7|ESP, generating…
DownloadL7|ESP® Platform Brochure
L7|ESP® is the Agentic Operating System for Precision Science, bringing workflow orchestration, data contextualization, scientific applications, and system connectivity into one operating environment. It supports research, development, manufacturing, quality, and diagnostics while preserving the relationships among processes, materials, instruments, people, and results as work is executed. This brochure provides an overview of the L7|ESP architecture,…
DownloadL7 Notebooks Datasheet
L7 Notebooks is the electronic lab notebook (ELN) application within L7|ESP®. It captures structured and unstructured experimental data for research, assay development, process development, and analytical development while connecting notebook entries with protocols, samples, inventory, instruments, and related laboratory records. The datasheet introduces capabilities for reusable experiment templates, protocol execution, experiment tracking, collaborative review, role-based…
DownloadToday’s Best ELN May Not Be Another ELN
Swapping one ELN for another won’t fix silos or deliver sustainable ROI. In this POV, Vasu Rangadass, Ph.D., explains why life sciences companies must stop the cycle of point-solution replacements and embrace a digital unified platform that contextualizes data, orchestrates workflows, and prepares R&D and manufacturing for AI.
ReadThe Agentic Pivot: Strategic Reorientation for Regulated Process Industries
Point solutions digitized the work, but they didn't unify it. For pharma, biotech, and CDMO organizations, the result is systemic fragmentation that drains scientific talent, slows batch release, and blocks AI from delivering real value. In this white paper, Vasu Rangadass, Ph.D., President and CEO of L7 Informatics, makes the case and explains how regulated…
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Only 2 percent of a production-grade AI system is decision logic. The other 98 percent is operational infrastructure. In pharmaceutical manufacturing, that infrastructure carries the weight of GxP compliance, audit, and validation. In this white paper, Vasu Rangadass, Ph.D., President and CEO at L7 Informatics, makes the case that AI orchestration, not model sophistication, will…
DownloadActive Knowledge Graphs: Why AI in Regulated Life Sciences Stalls Without Them
Most enterprise AI stalls in regulated life sciences because it runs on passive, retrospective knowledge graphs. This white paper introduces the active knowledge graph, generated by L7|ESP® at the point of execution, and shows how a neuro-symbolic architecture makes AI accurate, auditable, and AI-actionable across research, development, and GxP manufacturing.
DownloadELN Replacement: Complete FAQ for Life Sciences Organizations
This FAQ helps life sciences organizations evaluate electronic lab notebook replacement decisions. It covers hidden costs, common pitfalls, and explores digital unified platforms as an alternative approach. Based on industry research showing 600-1,000 annual replacement RFPs in a $480M market with poor ROI outcomes.
DownloadWhat customers are saying.
By utilizing L7 Notebooks as part of the L7|ESP Unified Platform, researchers benefit from a seamless integration of structured and unstructured data which is what most experimental paradigms require. This ultimately leads to efficient tech transfer from research to production and streamlined research and production operations.