LabFlow Reforge is an electronic lab notebook software platform developed for a US-based mid-size pharmaceutical company with multiple research teams using a legacy ELN across daily R&D operations.
The existing system had become difficult to adapt. Scientists still relied on it for core documentation, but rigid workflows, limited integrations, and outdated architecture pushed experiment notes, calculations, and supporting data into Excel files, local documents, and other parallel tools.
Computools delivered LabFlow Reforge as a modern ELN platform that reused critical legacy components while redesigning the core architecture for flexibility, integration, and future AI-driven use cases. The solution increased researcher adoption and daily usage by 2.5x, reduced manual data handling outside the ELN by 60%, and enabled integration with more than five core lab systems.
The client is a US-based mid-size pharmaceutical company running multiple R&D teams across laboratory research, experiment documentation, data capture, and internal scientific workflows.
The company had already invested in electronic lab notebook development, but its legacy platform no longer matched the pace of laboratory digitalization. New research processes required more flexible experiment structures, cleaner data capture, and stronger connectivity with surrounding lab systems.
The organization needed laboratory informatics solutions that could modernize the ELN environment, preserve valuable legacy components, and maintain the stability of ongoing research.
The existing ELN was built on outdated architecture and could not support evolving research processes or modern data workflows. The user experience was rigid and unintuitive. Scientists documented experiments in the system only when necessary, while practical work often continued in Excel files, local notes, and other tools outside the controlled ELN environment.
Integration capacity was limited. The system could not reliably connect to LIMS, analytical instruments, data pipelines, or external sources, leaving research data fragmented and increasing manual transfers between tools.
The architecture also blocked future AI-driven use cases. Experiment assistance, automated data structuring, and advanced analytics required cleaner data models and more flexible workflow logic than the legacy platform could provide.
The client needed custom ELN software to support laboratory digital transformation while preserving parts of the existing platform that R&D teams still needed. It also required pharmaceutical software development services capable of preserving validated legacy assets while rebuilding the architecture around integration, usability, and AI readiness.
Computools delivered LabFlow Reforge as a modern ELN platform that preserved critical legacy components and replaced the rigid core architecture with a modular, scalable system.
The platform was designed to evolve into an AI-powered electronic lab notebook, with structured experiment tracking, configurable workflows, standardized data capture, and a researcher-friendly interface aligned with daily laboratory work.
APIs and data pipelines connected the ELN with laboratory systems, analytical instruments, and external sources. Through LIMS integration services, research teams could exchange lab data directly within the platform, reducing manual transfers between disconnected tools.
A structured data layer prepared experiment records, metadata, observations, and results for AI in pharmaceutical R&D, including future experiment assistance, automated data structuring, and insight generation. Scientists retained control over experiment documentation, review, and final research records.
LabFlow Reforge delivered measurable improvements across research adoption, workflow consistency, and data readiness:
The new research data management platform reduced dependence on Excel, local notes, and disconnected tools by making structured experiment documentation easier to use inside the main system.
Improved laboratory workflow automation reduced repeated data entry, made experiment records easier to reuse across teams, and gave the organization cleaner, structured data for future AI-enabled research workflows.
Computools was selected because the client needed a partner capable of modernizing legacy pharmaceutical research software without disrupting active R&D operations.
The team analyzed the legacy architecture, identified reusable components, redesigned the platform for flexible research workflows, and built a structured data foundation for integrations and future AI use cases.
Delivery focused on researcher adoption from the start. Experiment structures, data-entry patterns, integration needs, and review workflows shaped the platform design, helping the client replace rigid legacy processes with a system scientists could use in daily work.
The design focused on practical experiment documentation, faster data capture, and a researcher-friendly interface that reduced reliance on tools outside the ELN.
Defining laboratory research workflows, experiment documentation needs, and daily usability requirements.
Structuring the platform around experiment setup, structured documentation, lab data capture, review workflows, and research data reuse.
Designing low-fidelity layouts for experiment setup, structured data capture, integrated lab data, review workflows, and AI-ready data preparation.
The interface gives researchers immediate access to experiment records, structured fields, supporting data, integration status, and review actions on a single workspace.
React and TypeScript
React and TypeScript provide the researcher-facing web interface for experiment setup, structured data capture, template configuration, review workflows, and daily ELN usage.
.NET
.NET supports the backend application layer, including experiment workflow logic, user permissions, data validation, integration services, and communication between ELN modules.
POSTGRESQL
PostgreSQL stores structured experiment records, metadata, observations, results, workflow states, review history, and relationships between research entities.
Elasticsearch
Elasticsearch supports fast search across experiment records, templates, metadata, attachments, and historical research documentation.
PYTHON
Python powers data-processing services for research data preparation, automated structuring, future AI-assisted workflows, and integration with analytical data sources.
Apache Airflow
Apache Airflow orchestrates scheduled and event-driven data pipelines between the ELN, LIMS, analytical instruments, and external research data sources.
REST APIs
REST APIs connect LabFlow Reforge with LIMS, internal research systems, analytical instruments, and external data services.
Docker and Kubernetes
Docker and Kubernetes support containerized deployment, environment consistency, scalability, and rollout across research teams.
AI-ready data architecture
A structured research data layer prepares experiment records, metadata, observations, and results for future AI agents, automated data structuring, experiment assistance, and decision-support scenarios.
An Agile approach with Scrum supported the iterative modernization of the legacy ELN. Work was organized into short sprints covering legacy assessment, workflow mapping, architecture redesign, interface development, integration readiness, and researcher validation.
Regular reviews with R&D, laboratory operations, IT, and digital transformation stakeholders helped verify usability, workflow fit, data-structure quality, and integration requirements before each major release.
Our old ELN still held important research work, but scientists kept going back to Excel and local notes because daily documentation was too rigid. LabFlow Reforge changed that. The new platform fits how experiments are actually planned, recorded, and reviewed. Adoption has increased noticeably, and much less research data now sits outside the ELN.
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