Lab Management & ELN Software Buyer’s Guide for R&D

Who This Guide Is For

You need this guide if you are any of the following:

1) R&D Operations or Lab IT Leader

Job titles: Director of R&D Operations, Lab Informatics Manager, Scientific IT Lead, Digital Lab Transformation Lead Your pain points:

• Scientists are still using spreadsheets, shared drives, and paper notebooks
• Data lives in too many systems and none of them talk to each other
• You need audit trails, version control, and defensible records
• Every workflow change turns into a mini-IT project

You need to understand how ELN, LIMS, inventory, and workflow automation fit together before you buy.

2) Research or Discovery Leader

Job titles: VP of Research, Head of Discovery, Principal Scientist, Lab Director Your pain points:

• Your team wastes time recreating experiments and hunting for data
• Protocols are inconsistent across teams and sites
• Collaboration is slow, especially across CROs, collaborators, and remote teams
• You need faster cycle times without sacrificing data quality

You need this guide if you care about scientific throughput, reproducibility, and time-to-insight.

3) Quality, Compliance, or Regulatory Lead

Job titles: QA Manager, CSV Lead, Compliance Director, Regulatory Operations Manager Your pain points:

• You need 21 CFR Part 11, GxP, audit trails, signatures, and retention controls
• You’re tired of systems that say “compliant” but can’t prove it in an audit
• Validation effort is eating your team alive
• Your organization is moving from informal research to regulated execution

You need a buyer’s guide that tells you what actually matters in validation and inspection readiness.

4) Budget-Conscious Academic or Startup Buyer

Job titles: Core Facility Manager, Lab Manager, PI, Founder, COO, Head of Platform Your pain points:

• You need a usable system fast, not a 9-month transformation
• You cannot afford enterprise implementation overhead
• You want to avoid vendor lock-in and bloated features
• Your team may outgrow spreadsheets, but not all the way to enterprise complexity

You need to separate free or low-cost tools that are genuinely useful from tools that become expensive later.

Who should NOT read this

This guide is not for:

• People looking for a generic lab software glossary
• Teams that only need a sample freezer label printer
• Buyers who already have a locked-down enterprise platform and are just comparing price quotes
• Anyone not willing to clean up data, standardize workflows, and change behavior

If you want a tool to “just work” with no process change, stop here. That product does not exist.

---

What Lab Management & ELN Software Does

At a high level, Lab Management & ELN software helps you capture, organize, govern, and operationalize scientific work. Depending on the product, it may cover:

• Electronic lab notebooks
• Sample and inventory management
• Protocol and workflow management
• Instrument integrations
• Collaboration and approvals
• Audit trails and electronic signatures
• Analytics and reporting
• AI-assisted search, summarization, and automation

The market is messy because vendors use overlapping terms:

• ELN usually means scientific recordkeeping and experiment documentation
• LIMS usually means sample, chain-of-custody, and lab operations
• Lab Operating System / Scientific Data Cloud usually means a broader platform layer
• AI lab assistant usually means search, summarization, workflow recommendations, or task automation
• Data discovery usually means analytics and structured scientific data access

The right question is not “ELN or LIMS?” It is: What work needs to happen faster, with fewer errors, and with better traceability?

Before vs. after

The business case: where the money actually comes from

Most buyers underestimate the value of lab management software because they focus on software cost, not labor cost.

A simple example:

• 20 scientists
• Each loses 30 minutes per day searching for protocols, sample status, or previous experiment data
• Loaded labor cost: $90/hour
• 30 minutes/day × 20 scientists × 220 working days = 2,200 hours/year
• 2,200 hours × $90 = $198,000/year in recovered productivity

That is before you count:

• Fewer sample mix-ups
• Fewer failed experiments
• Faster audit prep
• Less rework
• Better cross-team collaboration

If the platform also cuts one major workflow cycle by even 5% to 10%, the value can jump sharply. For a discovery team with a $2M annual operating budget, that can mean $100K to $200K in real efficiency or avoided waste.

The lesson: Do not buy software only to digitize records. Buy it to remove friction.

---

Market Landscape 2026

The lab management and ELN market in 2026 is no longer a simple “notebook software” market. It has split into five distinct vendor layers.

1) Pure ELN and researcher-first tools

These are the systems closest to the scientist experience:

• LabArchives ELN
• eLabFTW
• Labstep ELN
• SciNote
• Labguru
• Labii ELN & LIMS
• SciCord
• LabArchives ELN for Education
• LabArchives for Government
• LabArchives Scheduler
• LabArchives Inventory
• SnapGene Viewer as a specialized adjacent workflow tool

These products win when the buyer wants:

• Faster adoption
• Clear notebook-style usability
• Lower implementation burden
• Academic or mid-market fit

They lose when the buyer needs:

• Deep operational orchestration
• Complex enterprise integration
• Sophisticated data model governance
• Heavy multi-site standardization

2) Broad enterprise R&D platforms

These are the heavyweights:

• Benchling Life Sciences R&D Cloud
• Benchling R&D Cloud
• Dotmatics ELN & Data Discovery
• LabVantage ELN
• LabWare LIMS
• STARLIMS ELN
• Sapio Platform
• Sapio LIMS
• Sapio Scientific Data Cloud
• Sapio ELaiN
• SciSure Digital Lab Platform (DLP)
• Scispot LabOS
• Scispot GLUE
• Scispot Labflows
• Scispot Pathology LIMS
• LabLynx LIMS
• LiveDesign
• OMIQ
• Luma
• Hub by Tempus AI

These products win when the buyer needs:

• Platform breadth
• Customizable workflows
• Data standardization
• Integration across instruments and systems
• Regulated or enterprise-grade controls

They lose when buyers want:

• Fast rollout with minimal change management
• Simplicity over configurability
• Low-cost deployment
• “Just a notebook” rather than an ecosystem

3) AI and autonomous lab layer

This is the fastest-moving segment:

• Benchling Assistants
• Scibot™ by Scispot
• Genemod
• Sapio ELaiN
• Botable.ai QA / PLM Chatbot for Arena
• Botable AI Chatbot for Quality Assurance
• Life Star1 by Insilico Medicine
• Ginkgo Automation
• Intelligent Autonomous Lab Solution Platform by XtalPi
• ChemPlus® Desktop Solid Dispenser
• XtalComplete® Intelligent Crystallization Workstation

Some of these are real productivity tools. Some are marketing-first AI wrappers. Buyers should be skeptical.

The real impact of AI in this category is currently:

• Search and summarization
• Workflow suggestions
• Protocol drafting
• Data normalization help
• Querying messy scientific content
• Automation of repetitive admin work

What AI does not do well yet:

• Replace good scientific data models
• Fix bad master data
• Validate itself in regulated contexts
• Rescue a broken implementation
• Infer bad science into good science

Big warning: If a vendor says “AI-native” but cannot explain:

• what model it uses,
• what data it trains on,
• what human review is required,
• and how outputs are governed,

then the AI is probably a demo feature, not a core capability.

4) Open-source / flexible / self-hosted options

• eLabFTW
• eLabFTW API
• eLabFTW Hosting
• eLabFTW Support

These matter for institutions that want:

• Control
• Lower software license cost
• Ability to self-host
• Community-driven development
• Transparency

But “free” is not the same as “cheap.” You still pay for:

• Infrastructure
• Admin time
• Security hardening
• Validation
• Support
• Upgrades

Open-source is often the best answer for academia and smaller organizations with strong internal IT. It is often the worst answer for teams that want vendor accountability with minimal internal burden.

5) Specialized workflow and adjacent data platforms

• Citrine DataManager
• OMIQ
• SnapGene Viewer
• Hub by Tempus AI
• LiveDesign
• Luma by Dotmatics
• Botable.ai offerings
• Ginkgo Automation
• Insilico Medicine
• XtalPi offerings

These are not general ELNs. They solve a narrower problem:

• Materials data management
• Flow cytometry analysis
• Sequence viewing
• Drug discovery design
• AI-guided biology
• Robotics or autonomous experiments

Do not buy a specialized tool and expect it to become your enterprise system of record.

How the vendor landscape is structured

The 53 products in this directory fit into a few broad buying patterns:

What changed in 2026

Three things define the market now:

1) Buyers expect platform interoperability

No one wants another island. The winning vendors can connect to:

• instruments
• identity providers
• storage systems
• analytics stacks
• regulatory systems
• downstream data science environments

2) AI is now table stakes, but only useful if grounded

The market has moved from “Does it have AI?” to:

• Can it retrieve the right experiment?
• Can it summarize changes accurately?
• Can it recommend workflows based on actual metadata?
• Can it do this without creating compliance problems?

3) The market is consolidating around platform depth

Standalone ELNs are still alive, but the center of gravity is shifting toward:

• ELN + LIMS
• ELN + inventory
• ELN + workflow engine
• ELN + analytics
• ELN + AI assistant
• ELN + compliance layer

Vendor reality check

Here is the blunt truth:

• Benchling and Dotmatics are strong when you want broad R&D platforms with serious depth.
• LabArchives ELN, eLabFTW, and SciNote are often better when usability, affordability, or academic fit matters more than platform sprawl.
• Labguru, LabVantage, LabWare, STARLIMS, and Sapio are frequently evaluated when the buyer needs stronger operational control or regulated workflows.
• Scispot and Genemod are interesting for teams that want modern UX and AI-assisted workflows.
• XtalPi, Ginkgo Bioworks, Insilico Medicine, Tempus AI, and some of the chatbot products are better thought of as specialized or adjacent workflow technologies, not default ELN replacements.
• Botable.ai is not an ELN category leader; it belongs in a narrower AI workflow or QA assistant conversation.

---

The 8 Things That Matter Most

1) Workflow fit and scientific usability — 20%

Why it’s critical: If scientists hate the tool, adoption collapses. That is the most common failure mode.

What to evaluate:

• How experiments are created
• Whether templates match actual lab practice
• Click count for common actions
• Mobile or tablet usability in the lab
• How fast new users become productive

What good looks like:

• A bench scientist can log an experiment in under 5 minutes
• Templates match actual workflows without major rework
• Search and navigation are intuitive
• Teams use it voluntarily, not because they are forced

Red flag:

• The system is “powerful” but every common task takes training
• Scientists need admin help to record basic work

Ask the vendor:

• How long does it take a new scientist to enter a standard experiment?
• Can you show me the exact workflow for my top 3 use cases?
• What tasks do scientists actually do themselves vs. what requires admin support?

---

2) Data model flexibility — 15%

Why it’s critical: Your lab data will change. If the model is too rigid, you will create workarounds immediately.

What to evaluate:

• Custom fields and entities
• Protocol structure
• Sample lineage
• Metadata control
• Relationship handling across experiments, samples, and results

What good looks like:

• Configurable without heavy code
• Supports structured and unstructured data
• Can handle your current workflow and future expansion
• Avoids spreadsheet-style chaos

Red flag:

• Every change requires vendor services
• The platform forces your process to fit its template

Ask the vendor:

• How many data model changes can admins make without professional services?
• Can we represent our sample lineage and assay metadata cleanly?
• What happens when our workflow changes in 6 months?

---

3) Integration depth — 15%

Why it’s critical: A lab system that cannot connect to instruments, identity, storage, and analytics becomes a dead-end.

What to evaluate:

• API quality
• Native instrument integrations
• SSO and identity support
• Data export/import
• Integration with downstream analytics and BI

What good looks like:

• API is documented and usable, not just “available”
• Common enterprise integrations are supported
• Data moves bi-directionally where needed
• Integration does not require custom code for every use case

Red flag:

• “We have an API” but no practical examples or limits
• Every integration depends on a paid services team

Ask the vendor:

• Which integrations are native vs. custom?
• Do you support bi-directional sync or just export?
• What is the average integration timeline for systems like ours?

---

4) Compliance, security, and auditability — 15%

Why it’s critical: If you are in regulated life sciences or governed research, weak controls can kill the deal.

What to evaluate:

• 21 CFR Part 11 support
• Audit trails
• Electronic signatures
• Access controls
• Data retention
• SOC 2, ISO 27001, HIPAA, GxP, GDPR, FedRAMP if relevant

What good looks like:

• Clear validation support
• Granular audit logs
• Role-based access control
• Evidence that compliance is built in, not bolted on

Red flag:

• “We are compliant” but cannot provide control details
• No clear validation package or documentation support

Ask the vendor:

• What parts of the platform are validated out of the box?
• How do you support CSV and audit readiness?
• Can you show an audit log for a record change and signature event?

---

5) Implementation effort — 10%

Why it’s critical: Many lab platforms fail because implementation is underestimated by 2x to 3x.

What to evaluate:

• Time to launch
• Data migration tools
• Workflow design effort
• Admin training
• Change management support

What good looks like:

• Pilot in 4 to 8 weeks
• Department rollout in 8 to 16 weeks
• Clear migration plan and responsibilities
• Minimal custom code for first deployment

Red flag:

• The vendor says “fast implementation” but hides 6 months of configuration and validation

Ask the vendor:

• What is the typical implementation timeline for a lab our size?
• What do we need to clean up before migration?
• Which tasks are our responsibility vs. yours?

---

6) Scalability and governance — 10%

Why it’s critical: A platform that works for 10 users may collapse at 300 users or across multiple sites.

What to evaluate:

• Multi-site support
• Permission hierarchy
• Admin delegation
• Standardization across teams
• Change control processes

What good looks like:

• Central governance with local flexibility
• Scales across teams without chaos
• Can support multiple business units or geographies

Red flag:

• Every team becomes its own configuration island
• Permissioning is too coarse or too brittle

Ask the vendor:

• How do large organizations govern templates and standards?
• Can we roll out by site or function?
• How do you prevent configuration drift?

---

7) Analytics and scientific insight — 10%

Why it’s critical: The point is not just to store data. It is to use it.

What to evaluate:

• Reporting
• Dashboards
• Search
• Structured data access
• AI summarization or recommendations
• Export to data science tools

What good looks like:

• Fast access to trusted data
• Search across experiments, samples, and metadata
• Dashboards that reflect actual lab KPIs
• AI that helps, but does not replace verification

Red flag:

• “AI-powered insights” with no explanation of provenance or confidence
• Dashboards that look nice but don’t drive decisions

Ask the vendor:

• What questions can scientists answer in the platform without exporting data?
• How do you support lineage-aware search?
• How are AI outputs validated or reviewed?

---

8) Vendor stability and product roadmap — 5%

Why it’s critical: This is a long-term system. You need confidence the product will still matter in 3 to 5 years.

What to evaluate:

• Financial stability
• Customer base
• Release cadence
• Support model
• Clarity of roadmap

What good looks like:

• Regular releases
• Clear documentation
• Stable support
• Roadmap aligned to your use case

Red flag:

• Product sprawl with no clear direction
• Roadmap promises everything and proves nothing

Ask the vendor:

• What changed in the product over the last 12 months?
• How do you prioritize roadmap items?
• What happens if we need a feature not currently supported?

---

Use-Case Matching: Which Type of Buyer Are You?

1) Growth-stage biotech or diagnostics company

Typical profile: 20 to 250 employees, fast-changing workflows, lean ops team What matters most:

• Fast deployment
• Strong ELN + sample tracking
• Good APIs
• Minimal admin overhead
• AI-assisted search or protocol generation can help, but only after the core workflow is solid

Prioritize:

• Unified ELN/LIMS
• Flexible templates
• Inventory tracking
• Instrument integrations
• SSO and access control
• Exportability so you are not trapped

Usually a bad fit for:

• Heavy enterprise suites that require months of consulting before value
• Overly academic tools with weak ops support

---

2) Enterprise pharma or large life sciences organization

Typical profile: multiple sites, regulated environment, dozens to hundreds of scientists per function What matters most:

• Governance
• Compliance
• Change control
• Multi-site scalability
• Deep integration with enterprise systems
• Validation support

Prioritize:

• Part 11, GxP, audit trails
• API maturity
• Workflow orchestration
• Master data governance
• Role-based permissions
• Vendor services and implementation maturity

Usually a bad fit for:

• “Simple notebook” tools without enterprise controls
• Products that require heroic manual administration to stay coherent

---

3) Academic lab, core facility, or government research team

Typical profile: limited budget, mixed skill levels, collaboration-heavy, lower IT support What matters most:

• Low cost
• Ease of use
• Free or low-friction deployment
• Good collaboration
• Optional hosting/support if needed

Prioritize:

• eLabFTW
• LabArchives ELN
• SciNote
• Labstep ELN
• LabArchives for Government
• LabArchives ELN for Education
• Open-source with managed hosting if you need control

Usually a bad fit for:

• Expensive enterprise R&D clouds with long implementation cycles
• Systems that need dedicated admins just to function

---

4) Specialized science or data-heavy team

Typical profile: materials science, cytometry, computational biology, drug discovery, autonomous lab workflows What matters most:

• Domain-specific data models
• Strong analytics
• Instrument or device integration
• Scientific workflow fit

Prioritize:

• Dotmatics ELN & Data Discovery
• OMIQ
• LiveDesign
• Citrine DataManager
• XtalPi solutions
• Tempus AI Hub
• Insilico Medicine / Ginkgo where automation or AI is central

Usually a bad fit for:

• Generic ELNs with no domain-specific depth
• Broad suites that do everything except your actual science

---

Vendor Evaluation Questions

Features

• Can you show the end-to-end workflow for our top 3 lab use cases?
• How do scientists create, edit, version, and approve experiments?
• Can the system represent our sample lineage and metadata relationships accurately?
• What can be configured by admins without professional services?
• How do templates work across teams and sites?
• How does the platform handle unstructured data, attachments, and instrument output?
• What AI features are included, and what are their limits?
• Can users search across experiments, samples, files, and results in one place?

Integration

• Which instrument integrations are native today?
• What API coverage exists for experiments, samples, users, permissions, and audit logs?
• Do you support SSO, SCIM, and role synchronization?
• Can data be exported in bulk without vendor assistance?
• How do you integrate with downstream analytics, BI, or data lake tools?
• What is your standard integration approach for ERP, identity, and document systems?
• Do you support webhooks, scheduled syncs, or event-driven workflows?

Security and Compliance

• Which certifications do you currently maintain?
• Is the system validated for regulated use, and what does that validation cover?
• How are audit trails stored, accessed, and exported?
• Can electronic signatures be configured for our approval workflow?
• How do you handle data retention, legal hold, and deletion requests?
• What encryption standards are used at rest and in transit?
• How do you separate tenant data and restrict admin access?

Pricing

• What is included in base subscription pricing vs. add-ons?
• Are AI features priced separately?
• What implementation or onboarding fees should we expect?
• Are integrations, validation support, or premium support extra?
• How does pricing change as user count, storage, or workflows grow?
• What happens to pricing at renewal?
• Are there discounts for annual, multi-year, academic, or non-profit contracts?

Implementation

• What is the average time to pilot and time to full rollout?
• What data cleanup or migration work is required before go-live?
• How do you support change management and user training?
• Who owns workflow design: us, you, or both?
• What does success look like in the first 90 days?
• What customer resources are required from our side to stay on schedule?

Support and Vendor Fit

• What support tiers do you offer and what are the response times?
• Will we get a named CSM or technical account manager?
• How often do you release updates and how are they tested?
• What customer references do you have in organizations like ours?
• If we outgrow the product, what does migration or exit look like?

---

Common Mistakes to Avoid

Mistake #1: Buying “ELN” when you really need ELN + LIMS

If your lab tracks samples, reagents, chain-of-custody, and operational workflows, a pure notebook is not enough.

Consequence: Double entry, broken traceability, and another spreadsheet shadow system. Do instead: Map the actual workflow and decide whether you need records only, operations only, or both.

Mistake #2: Overvaluing AI and undervaluing data structure

AI cannot rescue sloppy metadata or inconsistent naming.

Consequence: Pretty summaries, bad decisions. Do instead: Fix taxonomy, templates, and master data first. Then add AI.

Mistake #3: Underestimating implementation by 2x to 3x

Vendors love to talk about features; they do not always talk about cleanup.

Consequence: Delays, burnout, failed adoption. Do instead: Budget 2 to 4 weeks for data cleanup in small deployments and 6 to 12 weeks in larger ones.

Mistake #4: Letting one enthusiastic scientist define the whole system

A power user is not the whole organization.

Consequence: A system optimized for one lab and rejected by everyone else. Do instead: Include scientists, ops, QA, IT, and at least one skeptic.

Mistake #5: Choosing based on demo polish

Demos are engineered to hide the messy parts.

Consequence: You buy slick UI and discover weak admin tools, bad export options, or limited controls. Do instead: Ask for a sandbox and test your real workflow.

Mistake #6: Ignoring migration and data governance

Most teams think migration means “move the files.” It does not.

Consequence: Duplicate records, broken lineage, and bad audit history. Do instead: Define what gets migrated, what gets archived, and what gets left behind.

Mistake #7: Paying for enterprise platform breadth you will never use

Some teams buy the Cadillac and drive it like a scooter.

Consequence: Overspend and slower adoption. Do instead: Match product depth to actual operational maturity.

Mistake #8: Assuming “compliance-ready” equals validation-ready

Those are not the same thing.

Consequence: Delays in regulated rollout and painful remediation. Do instead: Ask for validation documentation, control mapping, and implementation examples.

---

Evaluation Timeline & Process

6-week evaluation plan

Weighted evaluation matrix template

How to score

Use a 1-5 scale:

• 1 = unusable
• 2 = weak
• 3 = acceptable
• 4 = strong
• 5 = best-in-class

Rule: Do not let a shiny feature outrank core workflow fit. A beautiful AI assistant means little if the underlying notebook is painful.

---

Implementation Reality Check

Typical implementation timelines

Key success factors

• Executive sponsor who cares about adoption, not just purchase
• Clear process ownership
• A realistic data migration plan
• One or two standardized workflows to start
• A named internal admin
• Training that is role-based, not generic
• A pilot with real users and real data

What vendors won’t tell you

• Most deployments need more data cleanup than expected
• Scientists will initially duplicate work if the new system feels slower than the old one
• The hardest part is often standardizing naming and metadata
• AI features do not become useful until the underlying data is consistent
• You will need to decide what the system is not going to do

The fastest path to failure

• Trying to roll out every module at once
• Migrating everything blindly
• Skipping workflow mapping
• Ignoring training and adoption metrics
• Letting departments customize themselves into chaos

The fastest path to success

• Start with one high-friction workflow
• Standardize the minimum viable data model
• Pilot with a willing team
• Measure time saved and error reduction
• Expand only after adoption is real

---

Red Flags — When to Walk Away

---

How This Guide Was Created

This guide was created using a vendor-neutral review framework focused on:

• Workflow fit
• Compliance
• Integration depth
• Implementation reality
• Adoption risk
• Total cost of ownership

Review date: 2026-09-09 Update frequency: Quarterly, or sooner if there are major market shifts, acquisitions, or product changes. Independence disclaimer: This guide is independent and not sponsored by any vendor. Product mentions reflect market coverage and directory inclusion, not endorsement or ranking.

If you want, I can also turn this into:

• a shorter executive version,
• a comparison table of the top 15 products, or
• a SEO-optimized version with product-category landing page structure.

Lab Management & ELN solutions in the catalogue (46)

• Benchling Assistants by Benchling — Automating lab work and managing R&D data for life science innovation.
• Benchling Bioprocess by Benchling — The cloud-native solution for end-to-end bioprocess development.
• Benchling Bioresearch by Benchling — The digital workspace for biotech's greatest innovators.
• Benchling Connect by Benchling — Automate, manage, and analyze lab instrument data at scale to accelerate R&D throughput and decision-making.
• Benchling Developer Platform by Benchling — Integrate external systems, automate R&D operations, and extend Benchling functionality with APIs and custom apps.
• Benchling In Vivo by Benchling — Accelerate in vivo studies with modern data capture, collaboration, and insights.
• Benchling Insights by Benchling — Analyze R&D data quickly with no-code dashboards and powerful analytics.
• Benchling Life Sciences R&D Cloud by Benchling Life Sciences R&D Cloud — Accelerate life science R&D with a unified, cloud-native platform.
• Benchling R&D Cloud by Benchling — Accelerating the Pace of Life Sciences R&D.
• Botable AI Chatbot for Quality Assurance by Botable.ai — Boost QA efficiency with an AI chatbot for instant access to quality documents, procedures, and compliance.
• Botable.ai QA / PLM Chatbot for Arena by Botable.ai — AI-powered chatbot for quick and accurate answers to QA and PLM questions within Arena.
• Citrine DataManager by Citrine Informatics — Unify and leverage materials and chemical data across your enterprise to unlock its value.
• Dotmatics ELN & Data Discovery by Dotmatics — Accelerate R&D with an Electronic Lab Notebook and AI-powered data discovery platform.
• eLabFTW by eLabFTW — A free and open-source electronic lab notebook for researchers, by researchers.
• eLabFTW API by eLabFTW — Programmatic access to eLabFTW data for reading and writing experiment and resource information.
• eLabFTW Hosting by eLabFTW — Secure & reliable managed hosting for eLabFTW, the ELN that respects your data privacy.
• eLabFTW Support by eLabFTW — Expert support and custom development for eLabFTW users.
• Ginkgo Automation by Ginkgo Bioworks — Flexible, scalable lab automation systems for your evolving science, business, and lab space.
• Hub by Tempus AI — Streamline test ordering, results, and personalized insights for healthcare providers on desktop and mobile.
• Intelligent Autonomous Lab Solution Platform by XtalPi — AI-powered autonomous lab for high-throughput, precise, 24/7 experimental execution and data collection.
• LabArchives by Dotmatics — The world's most popular, easy-to-use cloud-based ELN for rapid research digitization.
• LabArchives ELN by LabArchives ELN — Secure, intuitive, cloud-based ELN for managing and sharing research data.
• LabArchives ELN for Education by LabArchives ELN — The electronic lab notebook solution purpose-built for teaching and managing lab courses.
• LabArchives for Government by LabArchives ELN — Secure, compliant, government-approved ELN for comprehensive research data management.
• LabArchives Inventory by LabArchives ELN — Streamline lab inventory, sample, and equipment management with seamless ELN integration.
• LabArchives Scheduler by LabArchives ELN — Streamline laboratory resource and equipment scheduling with an intuitive calendar.
• Labguru by Labguru — Unified lab management: ELN, LIMS, and Inventory for accelerated research.
• Life Star1 by Insilico Medicine — Accelerating drug discovery with robotic automation and generative AI.
• LiveDesign by Schrödinger — A flexible, cloud-native informatics and molecular design platform for interdisciplinary discovery teams.
• Luma by Dotmatics — AI-native multimodal scientific intelligence for R&D data management & workflow automation.
• OMIQ by Dotmatics — Cloud-native flow cytometry analysis with machine learning and centralized data management.
• Sapio ELaiN by Sapio Sciences — Your AI co-scientist for accelerated R&D and breakthrough discoveries.
• Sapio LIMS by Sapio Sciences — Configurable, no-code LIMS for automating complex lab processes, sample tracking, and quality control.
• Sapio Platform by Sapio Sciences — An agentic AI lab informatics platform uniting LIMS, ELaiN, and Scientific Data Cloud.
• Sapio Scientific Data Cloud by Sapio Sciences — Unify and harmonize lab instrument and research data, making it AI-ready.
• Scibot™ by Scispot — AI-powered lab assistant for biotech R&D, automating tasks and providing intelligent insights.
• SciCord by SciCord — Hybrid LIMS and ELN informatics platform for compliant pharmaceutical and analytical laboratories.
• SciNote by SciNote — Top-rated cloud electronic lab notebook with inventory, compliance, and team tools
• Scispot GLUE by Scispot — Seamlessly connect lab instruments and software for automated data workflows.
• Scispot Labflows by Scispot — AI-powered LIMS for sample-centric labs, automating workflows & transforming data.
• Scispot LabOS by Scispot — The Operating System for the Lab of the Future, unifying ELN, LIMS, and data lake with AI-driven insights.
• Scispot Pathology LIMS by Scispot — GenAI-powered LIMS alternative for pathology labs, streamlining workflows from sample intake to reporting.
• SciSure Digital Lab Platform (DLP) by SciSure
• SnapGene Viewer by SnapGene — View and share molecular biology data with rich visualization and annotation.
• STARLIMS ELN by STARLIMS — Spreadsheet-based electronic lab notebook integrated with unified lab informatics
• XtalComplete® Intelligent Crystallization Workstation by XtalPi — Automated crystallization processes powered by AI and robotics for accelerated drug discovery.