Seal vs Labguru

1Labguru and Seal, side by side.

Labguru and Seal both put the notebook, sample and inventory records and some production work on one platform, and both support validated GxP use. They differ in how a change to a process reaches the next run. Labguru combines an electronic lab notebook, LIMS, inventory, automation and informatics, and adds QC and electronic batch record modules.¹² In Seal, teams configure laboratory, manufacturing and quality workflows on one platform around a shared ontology, and each change to a workflow is released as a changeset with its own verification.

2What Labguru is built for.

Labguru is built for life science laboratories that want one cloud system for experiments, samples, inventory, equipment and requests, from academic groups to pharmaceutical companies.

Labguru began in 2008, founded by Jonathan Gross, and joined the Battery Ventures portfolio in April 2024. In July 2025 it and Titian Software, maker of the Mosaic sample management system, rebranded together as Cenevo, a company headquartered in London with offices in the US, Israel and Poland. Cenevo says Labguru and Mosaic remain products in their own right, used alone or together.³⁴

Labguru’s modules cover the research lab and its operations:

• The notebook plans and documents experiments, links annotated results to them, compares results across datasets and includes chemical drawing; a Requests module lets QC labs, CROs and service labs submit, schedule and track work.⁵
• Inventory tracks samples, reagents and storage locations, centralises ordering and alerts when stocks run low; equipment scheduling and maintenance sit alongside it.¹
• For QC labs, Labguru offers forms that produce structured data, alerts for deviations from procedures, out-of-range values and uncalibrated instruments, calibration management, configurable audit trails, tailored signature flows, and permissions that keep only effective SOP versions in use. It says it is compliant with 21 CFR Part 11 and GxP guidelines.⁶
• The electronic batch record module designs, executes and documents batch production on the same platform as research. Each time a process is executed, a new batch record is created from the process template, and batches link to the projects, protocols, experiments and materials behind them.²

Labguru runs on Amazon Web Services.¹ For GxP customers it offers a validated cloud with a continuous validation model: a dedicated validated instance with an adjustable validation plan, or a shared validated platform with a fixed number of scheduled updates a year, each with a test environment for performance qualification. Its Validation Management team assesses every change since the previous update, updates the functional requirements specification, master risk assessment and test protocols, and issues a validation summary report; the customer performs PQ.⁷ Developers can use a REST API, a GraphQL interface, R and Python packages, SQL-based dashboards and a Workflow Editor add-on for automations.⁸ Labguru’s AI includes the Labguru Assistant for troubleshooting, analysis and plain-language queries across notebook and inventory records, a Protocol Conversion Agent that turns Word, PDF and spreadsheet files into interactive Labguru protocols, and an Automation Agent that turns plain-language requests into automation configurations.⁹

3A failed test and the sample behind the repeat.

Both products link samples to the experiments that use them. The difference shows when a test fails and the repeat depends on how much of a sample is left, where it is and what it is already promised to.

In Labguru, samples and supplies sit in inventory collections with custom fields, physical locations down to the tube, barcodes, remaining quantities and expiry dates. Inventory items are added to notebook entries, so a team can view every experiment performed on a sample, mark stocks as consumed after use and receive notifications when stocks run low or expire.¹⁰ For QC labs, Labguru links SOPs, samples, equipment and results, and alerts on out-of-range values and uncalibrated instruments.⁶

In Seal, each use of a sample is a recorded transaction against the aliquot it consumed. In Seal’s sample management blueprint, three aliquots of process-development sample S-084 were prepared from 4.00 mL. Run RUN-084 consumed 0.40 mL from aliquot A1 before its result was invalidated; A2 is reserved for a stability request, REQ-085; and A3 has moved to another freezer while its condition assessment, EXC-084, is still open. The repeat, REQ-086, needs 0.80 mL. Asked for candidate allocations, Neil shows that A1 is 0.20 mL short after RUN-084, that A2 is committed to REQ-085, that A3’s hold is still open, and that the 0.70 mL parent residual is insufficient alone and not assessed for this use. It prepares a linked assessment comparing recollection with an authorised reallocation, including the effect on REQ-085.

No material is allocated, no hold is cleared and no reservation changes without an explicit decision by the custodian. An invalidated result does not restore the material it consumed, and the investigation into it starts from the same records: the aliquot’s use is connected to the experiment, method, result and quality assessment that depend on it.

4Where Labguru is the better fit.

Labguru is the better fit for research organisations that want one system for experiments, samples, inventory and lab operations.

• You run research labs, from academic groups to biotech, that want notebook, inventory, ordering and equipment in one cloud product.¹
• You manage large sample collections and automated stores, and want Labguru alongside Mosaic’s sample management from the same company.⁴
• Your QC or service lab wants request intake, scheduling and tracking in the notebook platform.⁵
• Your data scientists want GraphQL, R and Python access to notebook and inventory data.⁸
• You want a vendor that validates its own releases for you under a documented continuous validation model, with your team performing PQ.⁷

5Where Seal is the better fit.

Seal is the better fit when the processes themselves keep changing under GMP and each change has to show it worked.

• Your corrective actions should become verified changes to the workflow, measured on the batches that follow, with deviations, CAPA, change control, document control and training on the same records as the batch and the lab.
• You want each change your team makes to a protocol, calculation or batch record to carry its specifications, acceptance tests and approvals, while earlier runs keep the version they used.
• You want an agent that prepares that work. Neil configures workflows from your procedures, assembles investigation evidence across records and connected systems, and drafts changes with their verification for people to approve. In line with the EU’s draft GMP Annex 22 on AI, Neil is not used in GMP execution. It helps set up configuration, which your team verifies and releases under change control.
• You want laboratory and manufacturing work on one ontology from lab to commercial scale. MiAlgae runs ELN, quality and electronic batch records in Seal.

6Running Seal alongside Labguru.

You can keep research and inventory in Labguru and run one controlled process in Seal. Seal’s Labguru connector tracks a stock’s identity, lot and expiry alongside a Seal workflow. It reads selected stocks with a personal API token from a dedicated Labguru account, and it does not write to Labguru.

To set it up, generate the token for a dedicated account that can see only the intended stocks, then open Settings → Integrations → Labguru in Seal, enter the HTTPS workspace root ending in labguru.com and the token, and choose Test and connect. Select stocks by browsing or by exact numeric stock ID, choose the Seal groups to share them with, and compare lot and expiry in the first capture with Labguru. Shared stocks can then be referenced with @ in documents, comments and Neil.

The connector captures stock ID and name, the associated inventory-item ID and type, and the storage, lot, barcode, expiry and modification fields the API returns. Quantity and concentration, linked inventory-item content, experiments, analytical results, custom fields and attachments are not included, and a stock summary is not a release certificate. Labguru’s v1 API sends the token as a query parameter, so treat query strings in API access logs as sensitive. With the stocks shared, Neil can:

• compare the lot, expiry and storage identifiers of selected stocks with a batch or experiment record before use;
• identify which Seal records reference a selected stock, its lot and its barcode during an investigation;
• list the selected stocks that expire before planned work.

7Moving from Labguru to Seal.

Move one process first, and keep Labguru for research and inventory until you decide otherwise.

1. Choose a process where changes need to be controlled: a batch record template, a QC test or a development method approaching transfer.
2. Bring the procedure or template, a representative batch or run and the records it touches. Start with sample material, or arrange an NDA before sharing confidential records.
3. Neil configures the workflow from your procedure, and your team checks it against how the work runs. Once the scope is agreed and the inputs received, Seal prepares the first working process and its validation pack within 48 hours. Production timing then depends on scope, interfaces, migration and your release requirements.
4. Connect the Labguru stocks the process uses, and exercise the workflow and its failure cases, including a change to a limit, before you approve release.
5. If records move, scope exports, attachments, metadata, permissions and history first, map reusable fields, preserve the source records and keep gaps visible.

Retention obligations continue for the records that stay in Labguru. The ELN blueprint and sample management blueprint show the experiment and sample workflows in detail.