Keep expertise where it belongs—your ticket to the AI era

Stringent global regulatory compliance (e.g., by the FDA and NMPA) has become a mandatory requirement. With comprehensive audit trails, electronic signatures, and guaranteed data integrity, ELNs have become an indispensable tool for enterprises to pass inspections and achieve China-US dual filings. AI-driven R&D has emerged as an irreversible trend, and ELNs serve as the sole foundation for building high-quality, structured scientific research data assets. ELNs fundamentally address the core pain points of paper-based records and Excel-based management, including illegible handwriting, retrospective "memory-based" recording that is non-reproducible, low efficiency, difficulties in data retrieval, poor collaboration, and high risks of data tampering. ELNs are rapidly expanding from the biopharmaceutical industry to chemicals, new materials, new energy, and other sectors. This is because standardized and configurable ELNs can flexibly meet the needs of diverse R&D scenarios, enabling a wider range of research institutions to achieve digital and intelligent R&D transformation at a controllable cost. Therefore, adopting ELNs is no longer merely an upgrade of recording tools, but a core strategic investment in building the intelligent R&D platforms of the future. It helps enterprises accumulate and activate their core knowledge assets, ultimately reducing R&D costs, drastically improving efficiency, and achieving a qualitative leap in sustainable innovation capabilities.

Yes. ELN data can be deployed in three ways:

Cloud-based SaaS services provided by vendors;
Third-party cloud platforms purchased by the client, such as Huawei Cloud, Amazon Web Services (AWS), and Alibaba Cloud;
The client’s own on-site physical servers.

SaaS Cloud Service: A few thousand RMB per user per year;
Private On-Premises Deployment: Approximately twice the annual cost per user of the SaaS model, plus a one-time upfront deployment fee;
Perpetual License Model (One-Time Purchase) for Private Deployment: The total price generally starts from 500,000 RMB."

"Choosing a standardized ELN is a wiser and more forward-looking decision. It can meet around 95% of common R&D requirements in the long run and ensures your system evolves continuously with industry best practices, rather than being rigidly locked into fixed processes at the time of customization.

The core advantages of a standardized ELN lie in continuous upgrades and controllable costs. Its R&D costs are shared by hundreds or even thousands of customers, allowing you to obtain a massively validated, stable and feature-rich product at a cost dozens of times lower than custom development.

Although a custom-developed ELN can fit 100% of your current workflows, any future business changes will require time-consuming and labor-intensive secondary development, making it difficult to adapt to shifts in R&D innovation and regulatory requirements.

In the era of AI-driven R&D, standardized ELNs are the optimal path to access cutting-edge intelligent ecosystems. For example, AtomDeep InAI integrates the DeepSeek large model—standard products can quickly incorporate universal AI capabilities, while custom systems often become data silos that fail to leverage industry-wide technological dividends.

Therefore, unless your workflow is extremely specialized and permanently fixed, you should prioritize a standardized ELN. It secures your initiative in data security, compliance and future intelligent expansion, and represents a strategic choice to invest funds in core R&D rather than repetitive IT infrastructure development."

Annual payment (subscription model) grants you access to a continuously evolving dynamic service, with your software automatically updated to the latest and optimal version. A one-time perpetual purchase only provides you with a perpetual license for the current version. The perpetual license model carries a huge hidden cost black hole: high upfront license fees, plus major version upgrade fees every 3–5 years (accounting for 50%–70% of the initial cost). The initial investment for annual payment is usually only 20%–30% of the perpetual purchase price. If the solution is unsuitable, you can switch providers the following year with low trial-and-error costs. In contrast, the perpetual model requires massive upfront investment; if the wrong choice is made, the high sunk costs will put you in a passive position. Under the annual subscription model, vendors must deliver high-quality services and continuous innovation to retain your business. Under the perpetual model, vendors typically have far less incentive to provide premium services and proactive upgrades after receiving full payment. Therefore, unless your business requirements are absolutely fixed, annual payment is the more rational and forward-looking choice. It ensures you always own an advanced productivity tool, rather than a gradually outdated "static asset".

Low-cost ELNs often cut costs by simplifying core compliance architectures (such as audit trails and electronic signatures). This leads to failure during strict official on-site inspections by the FDA, NMPA and other authorities—as data integrity (ALCOA+ principles) cannot be effectively verified, exposing all your R&D investment and intellectual property to fatal risks of failed registration and market launch. Their underlying technologies are usually assembled from outdated open-source components or third-party commercial plugins, lacking in-depth understanding and original support for scientific research scenarios (e.g., chemical structures, biological sequences). This results in unfixable bugs, system stagnation and unstructured data, which not only fail to improve efficiency but also create insurmountable data barriers for future integration with large AI models and knowledge base construction. Security investment represents a major hidden cost. Low-cost solutions often skimp on system protection, vulnerability mitigation, encryption algorithms and backup strategies, and even use pirated plugins. This exposes your enterprise’s most critical R&D data to high risks of leakage, tampering and even ransomware attacks—security losses far outweigh the savings on software costs. The vendor’s long-term viability and R&D investment capacity are critical. The low-price competition model is unsustainable: if a vendor stops updates, terminates services or goes out of business, you will face unmaintained systems and un-migratable data, forcing you to re-select solutions and incurring higher total costs and catastrophic business interruptions. Therefore, when purchasing an ELN, the real cost is not the software license fee, but an "insurance policy" ensuring your R&D data assets are compliant, secure and value-added. Choosing a solution with hard-core technology and international inspection validation like Interlab is essentially securing a reliable "strategic guarantee" for the long-term success and value realization of your R&D pipelines.

SaaS Cloud Service Its core advantage is fast and hassle-free launch. Enterprises do not need to prepare their own servers, as Intergle deploys the system on the secure Huawei Cloud. The deployment is shortest: independent database setup, initial configuration and user activation can generally be completed within 2 weeks.

On-Premises Deployment

The software is installed on servers controlled by the enterprise, enabling physical data isolation. Before deployment, the enterprise’s IT department must prepare qualified servers and network environments. The cycle is longer, generally 1 to 3 months, depending mainly on the complexity of IT environment preparation, installation & debugging, and integration with internal systems (e.g., domain controllers, LIMS).

"If your work focuses on exploration and innovation:
Typical scenarios: Early drug discovery, new material synthesis, synthetic biology, process route development, formulation research.
Core needs: Flexible recording of experimental ideas and procedures, management of chemical structures (via InDraw) and biological sequences (via InSequence), data analysis (e.g., IC50 calculation), and application of data to AI-assisted design (via InAI).
Recommendation: What you truly need is an ELN (AtomDeep InELN). It is purpose-built for these R&D scenarios.
If your work focuses on standard execution and quality assurance:
Typical scenarios: Quality inspection of raw materials/finished products, stability testing, clinical sample analysis, environmental monitoring.
Core needs: Efficient receipt, assignment and tracking of large volumes of test samples, strict implementation of SOPs, automatic instrument data capture, and one-click generation of regulatory-compliant test reports.
Recommendation: You should select a professional LIMS solution.
If you require both:
Many enterprises have both R&D centers and quality control (QC) departments. A fully digital laboratory typically requires the collaboration of ELN and LIMS:
The R&D team (using ELN) documents experimental workflows, and instructions for sample testing protocols can be transmitted to the analytical testing team (using LIMS).
LIMS feeds back data or trends identified during analytical testing to ELN, providing input for the next round of R&D optimization.
The Interlab Ecosystem: Its SIMS platform is ELN-centric, and can seamlessly connect with third-party LIMS via standard APIs to enable seamless data flow.
Conclusion
In summary, ELN serves the creativity and flexibility of research and development, while LIMS serves the repeatability and compliance of quality control analysis.
If your core objective is to enhance R&D efficiency and intelligence, Interlab InELN and its integrated platform (InWMS, InCMS, etc.) are the more direct and in-depth choice. If your core requirement is quality control process management, you should focus on evaluating professional LIMS products."

In summary, the value chain of AtomDeep ELN is as follows: it evolves from a digital tool that replaces paper-based records, to a collaborative platform that connects everything, and ultimately becomes an AI intelligence that empowers scientists. Its ultimate goal is to achieve a 100x improvement in R&D efficiency.

CSV: Computer System Validation3Q Validation is a simplified version of CSV: • Installation Qualification (IQ): Ensures the hardware and software environments comply with the specified requirements. • Operational Qualification (OQ): Ensures all functions operate properly within the defined ranges. • Performance Qualification (PQ): Ensures reliable and compliant data can be consistently generated during long-term practical operation.

Core Technological Innovation: Adopts the proprietary 6-second auto-save and modification traceability technology, which guarantees the originality and non-tamperability of data the moment it is generated, and meets the highest compliance standards at the system’s foundational level.Effective Management Tools: Every record is stamped with a timestamp, enabling timely detection and supervision of untimely logging. This resolves the flaws of paper records — no timestamps and no real-time online oversight of recording timeliness.Real-time Collaboration & Integration: Supports multi-user real-time online co-editing, and enables direct connection with laboratory instruments (e.g., HPLC, electronic balances) for automatic data capture, eliminating information delays and manual transcription errors.Continuous Iteration & Reliability: As a standardized product, it provides automatic, ongoing updates and security patches to ensure high system availability, preventing data recording delays caused by system outages.

Compliance Built into Core Technology: The 6-second auto-traceability timestamp locks data at the source, enforcing compliance with key regulatory principles such as ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, Available). • Electronic Signatures & Permission Control: Features a complete electronic signature workflow and a granular permission system (by user/role), ensuring all operations are attributable and auditable, in strict alignment with global electronic record regulations including FDA 21 CFR Part 11. • Professional Validation & Proven Track Record: Beyond a compliant product, we offer professional 3Q / third-party CSV validation services. With over 100 successful China-US IND dual filings, our system has passed the strictest on-site inspections by NMPA and FDA. • Full-Spectrum Security & Audit-Ready: Certified with ISO 27001 international information security standard, it provides end-to-end protection for systems, data and operation logs. All records generate non-deletable audit trails automatically, fully prepared for regulatory audits at any time. • Compliance as a Native Feature: Global regulatory rules are embedded into standardized product functions, turning compliance from a costly afterthought into an out-of-the-box guarantee, drastically reducing enterprises’ compliance risks and implementation complexity.

For most R&D companies (especially SMEs, startups and cross-regional teams): Prioritize SaaS cloud services — higher cost-performance, fast launch, zero-fuss operation and maintenance, plus flexible data migration (full data can be moved to on-premises servers later).For large groups, classified units and enterprises with strict physical isolation rules: Choose on-premises deployment to achieve full data control and meet specialized compliance needs, which requires a dedicated in-house IT team for maintenance and security.The final decision should align with your company’s scale, R&D traits and long-term strategy. Paired with Interlab’s annual subscription model (guaranteeing continuous upgrades and support), it delivers the core goals: controllable costs, full compliance, and efficiency gains.

"Environment Adaptation & System Installation: Server environment verification, deployment & configuration of core modules and databases;
Data Migration & Integration: Historical data migration, docking & debugging of third-party systems (ERP, PLM, etc.);
Compliance & Security Setup: VPN debugging, access control, and local security configuration (permissions, encryption, vulnerability fixes);
Fault Diagnosis: Troubleshooting and repair of hard disk shortages, hardware failures, OS/software/database compatibility issues;
Customization & Training: Configuration of experiment templates & approval workflows, admin O&M training and end-user training;
Testing & Acceptance: Independent functional & performance testing, go-live support and project acceptance.


Exclusions: Server procurement, lab construction, and the enterprise’s long-term in-house O&M costs.
It is a personalized service requiring collaboration between O&M engineers, developers, business managers, testers, after-sales and product teams, focused on end-to-end problem resolution."

On-premises deployment has no economies of scale — costs for hardware adaptation and environment setup cannot be shared across users; 2. Frequent hardware issues (e.g., hard disk bad sectors, unstable intranets) raise service and support difficulty; 3. Recurring software problems (e.g., encryption software integration, third-party system docking, insufficient storage for big data) increase troubleshooting and repair workload; 4. Constantly disclosed vulnerabilities in operating systems and programming languages demand custom upgrades and patching; 5. Higher labor costs for personalized integrations and dedicated security/compliance configurations; 6. Post-launch O&M and version updates require tailored services, which are far more costly than SaaS’s centralized cloud maintenance.