ELN

Seal's Electronic Lab Notebook (ELN) captures experiments as structured data rather than as free-form documents. It is designed for R&D organizations in biotech and life sciences where scientists generate large volumes of experimental data that must remain searchable, reproducible, and traceable across the full development pipeline — from discovery through to quality control and manufacturing.

Seal addresses a core problem in laboratory data management: most ELNs digitize the notebook format rather than the underlying science. The result is data that is difficult to query, impossible to compare across experiments, and effectively lost when scientists leave. Seal structures experiments as instances of defined protocols, enforcing capture of critical parameters while retaining flexibility for scientists to add steps, change parameters, and record observations freely.

Core Approach: Structured Experiments

• Each experiment is an instance of a protocol, not a blank page. Protocols define required parameters — for example, a PCR protocol specifies template concentration, primer sequences, annealing temperature, and cycle count.
• Scientists fill in specific values when running an experiment. The structure enforces capture of key parameters; free-text observations and additional steps remain available alongside structured fields.
• Structured data enables direct queries: finding all PCR experiments where cycle count exceeded 30, or comparing yield across all runs of a given protocol, without manual review of notebooks.
• Reagent lot recall impact, experiment reproducibility checks, and cross-experiment pattern analysis are all supported by the structured data model.

Connectivity and Traceability

• Samples are real objects in the system. Experiments link to sample records, and scientists can navigate full sample lineage — origin, processing history, and derivatives.
• Equipment is linked to calibration records. When an experiment references a piece of equipment, its qualification status at the time of the run is accessible directly from the experiment record.
• Reagents and materials are tracked at the lot level. Linking experiments to specific lots means that if a lot is recalled, every affected experiment is identified immediately. If an experiment fails, all other experiments using the same lot can be reviewed instantly.
• Results are stored as structured outcome data — yields, concentrations, counts — queryable across every experiment in the system.
• Attachments such as chromatograms, gel images, and spectra are locked to the experiment record as evidence.

Collaboration and Witnessing

• Witnessing is a one-click workflow. A scientist requests a colleague to review and sign off on a critical step; the colleague is notified immediately, and their signature locks that portion of the record with a timestamp for IP defensibility.
• Projects function as shared workspaces. Team members are added with granular access controls — edit, view-only, or restricted access for sensitive experiments.
• Real-time notifications alert team members when dependent experiments are completed or when a reagent lot they have used is flagged.

Tech Transfer and System Integration

• ELN protocols share a data structure with QC methods. A method developed in the ELN can be locked down as a controlled QC method with specifications and acceptance criteria, then executed in the LIMS with compliance controls — without re-entry or copy-paste translation.
• Protocols can be promoted to manufacturing execution system (MES) use, with parameters, ranges, and acceptance criteria transferred to batch records intact.
• The platform connects to LIMS, MES, and QMS, supporting a unified data flow from discovery to release.

Search and Reproducibility

• Structured queries support filtering by parameter ranges, outcomes, materials used, scientist, date, and combinations of criteria — for example, all PCR experiments by a specific scientist in a given quarter where yield fell below a threshold.
• When reproducing an experiment, the record contains the exact protocol version, specific reagent lots, equipment used, environmental conditions, raw data, and scientist observations.
• The system prompts for critical parameters by design, so reproducibility-relevant data is captured consistently rather than depending on what a scientist remembers to record.

AI Capabilities (Neil)

• Neil, Seal's AI, generates complete protocol structures from a conversational description. Describing an ELISA with a standard curve and sample dilutions produces a structured protocol with fields, plate layout, embedded calculations, and result formatting.
• Protocol libraries can be populated in days rather than months. Scientists describe their assay portfolio and Neil generates templates based on standard methods, which scientists then customize.
• Within experiments, Neil suggests parameters based on outcomes from recent runs, flags anomalies against historical patterns, and identifies which conditions correlate with yield or aggregation across a campaign.
• AI surfaces relevant publications and similar prior experiments from within the organization as scientists work, providing context from both internal history and external literature.

Implementation and Onboarding

• Onboarding is designed to start with one team and one assay type. Scientists describe their current workflow to Neil, Neil generates the protocol structure, and the team refines it over a week of actual use.
• Protocols developed by early teams can be shared with subsequent teams. Common elements become library protocols; variations become linked protocols that inherit a base structure.
• Historical data can be imported: spreadsheets as structured data, paper notebooks as scanned attachments linked to experiments by date and project.
• Custom scripts in Python and JavaScript are supported for complex data processing, custom calculations, and integrations.

Additional Capabilities

• Built-in calculations: formulas for dilution factors, yield percentages, and statistical analysis compute automatically from entered data.
• Reagent inventory tracking: lot expiry, stock levels, low-stock alerts, and experiment-to-lot linkage.
• Sample labeling: compliant labels with barcodes that link to full sample records; scanning a barcode displays the sample's full lineage.
• Automated reporting: project summaries, regulatory submissions, and method validations generated from structured experiment data.
• AI protocol optimization: parameter adjustment suggestions based on historical outcomes, with specific quantitative observations drawn from the experiment dataset.

Seal is designed to function as a knowledge system rather than a documentation system, keeping experimental knowledge accessible and useful after scientists leave, supporting regulatory data requests, and providing the structured foundation required for AI-driven analysis across an organization's full experiment history.