A drug experiment can be finished while its data is still waiting to go anywhere. Results need checking, calculations need tracing and reports need assembling. At BioIVT, a research services company, changing that administrative machinery produced a striking result: study cycle times fell by at least 30% after an IDBS E-WorkBook implementation, according to the companies’ March 2025 announcement.
IDBS sells software for this easily overlooked stretch of science. Its tools record experiments, organize samples, preserve process histories and connect development data with manufacturing information. The customer is buying a better way to remember what happened - and enough structure to use that knowledge again.
That proposition has acquired a new urgency. Biopharma companies want artificial intelligence to help interpret experiments and prepare documents. But an AI system cannot reliably recover the meaning of a measurement whose sample, method or process version has gone missing. For IDBS, an enterprise software business founded in 1989, yesterday’s recordkeeping problem has become part of today’s AI infrastructure race.
Four weeks matter when clients are waiting
BioIVT’s problem was practical: it wanted faster processing and reporting, more capacity and software that different departments would actually adopt. It evaluated alternatives, chose E-WorkBook and worked with IDBS on workflows tailored to departmental needs. The intervention reached beyond the scientist’s notebook into documentation, data processing and audit follow-up.
“deliver data to our clients at least four weeks faster”
Joanna Barbara, BioIVT’s Kansas City site lead, describing the E-WorkBook implementation
The announcement also reported a 20% capacity increase, equating to roughly eight hours saved per user each week. Those are results from a particular implementation, not a promise attached to every software license. Still, they suggest a useful buying criterion: measure the time between an experiment and a usable answer, rather than counting how many pages have become electronic. BioIVT’s account puts that distinction in concrete terms.
The spreadsheet reaches its limit
Another customer explains why familiar tools sometimes stop being enough. In its account of selecting PIMS, Novavax described wanting to replace extensive Excel use for critical batch, process and product information. During the COVID-19 vaccine effort, it also needed faster information exchange with contract manufacturers across multiple sites. Ease of deployment and configuration mattered because production schedules were compressed.
The weakness was in the handoffs: information had to remain usable as it moved between organizations. Novavax’s selection rationale points to the limits of spreadsheet-based coordination under those conditions. It does not establish that every spreadsheet must disappear. It shows why a shared process record becomes more valuable as partners, sites and reporting demands multiply.
IDBS PIMS, short for Process Information Management System, brings manufacturing and quality information together across batches and products. It connects with systems such as electronic batch records, laboratory information systems and manufacturing execution software. Teams can use it to monitor processes, investigate variations and support continued process verification. Its place in the factory is to make information from those systems easier to compare and share.
A notebook grows a memory for the whole process
The product family reflects a widening ambition. ActivityBase, introduced in 1994, handles screening data in drug discovery. E-WorkBook, which IDBS dates to 2006, became its established laboratory platform. Its cloud release followed in 2017. Polar arrived in 2020; the 2021 acquisition of Skyland Analytics extended the business into manufacturing data management.
Polar combines three jobs often described by separate acronyms. An electronic laboratory notebook records experimental work. A laboratory information management system tracks materials and samples. A laboratory execution system guides how procedures are performed and data is captured. Polar combines capabilities from all three, with configurable workflows and connections between experiments, equipment, materials and results.
ActivityBase · screening data
E-WorkBook / Polar · records, samples, workflows
Polar Control · parameters and versions
PIMS · batch and quality information
An editorial map of product roles. Product coverage overlaps; integrations depend on the deployment.
In March 2025, IDBS added Polar Control. It keeps versions of individual parameters and broader process definitions, including their specifications and criticality. Think of the difference between having a recipe and knowing exactly which revision produced a particular batch. That history matters when a process moves from a development team to another facility. The launch announcement makes technology transfer a central use case.
Existing customers also have a migration path. IDBS says E-WorkBook users can take their workflows and data into Polar, either retaining existing capabilities or adopting more of the platform. That continuity is a substantial part of the pitch: years of laboratory work represent an investment that does not fit neatly into a software replacement budget.
Danaher bought the connections
Neil Kipling founded IDBS as a specialist IT consultancy. Its history includes two Queen’s Awards in 2003, for innovation and international trade. By the time Danaher acquired it in October 2017, the company was selling enterprise scientific informatics across an international customer base.
The acquisition announcement emphasized connecting information from scientific instruments across vendors and disciplines. For Danaher, whose life sciences businesses included instrument suppliers, that offered a way to build information services around laboratory work. It is a useful clue to IDBS’s position: much of its value lies between machines, teams and decisions.
That relationship continues to shape implementation. In its 2025 product review, IDBS described an integration framework with an initial connector for Cytiva’s UNICORN software, used in purification workflows. Its wider partner directory includes Accenture, Zifo, Scitara and Tableau, spanning consulting, scientific informatics, laboratory connectivity and analytics. The product has to work inside a laboratory’s existing collection of systems.
A crowded market, with an unusually long memory
IDBS sits in enterprise laboratory informatics, serving biopharma developers, contract research organizations and manufacturers. Its customer materials name companies including BioIVT, Novavax, Chugai and Celyad Oncology. E-WorkBook also serves research organizations outside pharmaceuticals, including industrial and consumer-product sectors.
The current website says 15 of the top 20 biopharma companies have been customers for more than a decade, using market-capitalization rankings from 2024 and 2025. That measures relationships within a particular cohort, not a 75% share of the software market. It nevertheless signals the importance of retaining customers whose scientific workflows are deeply embedded in the platform.
Buyers have credible alternatives. Benchling Bioprocess covers recipe design, experiment planning, execution and process analysis. Dotmatics offers electronic notebooks, integrations and searchable scientific data. Connected workflows and AI-ready records are therefore competitive requirements, not exclusive IDBS inventions. IDBS’s case rests on the combination of its installed relationships, process-management expertise, manufacturing offering and support for existing E-WorkBook investments.
The bill includes changing how people work
IDBS’s commercial model combines enterprise cloud software with consulting, implementation, training and continuing support. Buyers engage its sales team to define a deployment. The meaningful cost calculation covers the software agreement and the work needed to connect systems, configure workflows, qualify the installation and train the people who will use it.
Its professional services offering starts with understanding and mapping processes. IDBS explicitly treats implementation as organizational change, requiring early communication, executive involvement and an active user community. A laboratory that buys software without allocating time for that work is leaving an essential part of the project unfunded.
The company’s stated culture fits that emphasis: listening to customers, teamwork and continuous improvement, expressed through Danaher’s kaizen vocabulary. Its mission connects better use of data with faster decisions across drug development. Those are published values; the observable service proposition is the combination of scientific workflow knowledge and technical configuration.
AI gets a job. Scientists keep the sign-off.
On June 16, 2026, IDBS announced a partnership with Alchemi to connect AI agents to data in Polar. The intended work includes drafting technical reports and submission dossiers, then routing drafts to scientists for review. The companies describe retaining traceability and recording both human and AI actions through the workflow.
The announcement cites Alchemi’s claim that its agents have helped produce such documents up to 70% faster in customer deployments. That figure describes Alchemi’s reported experience; it should not be read as a measured result for the newly announced Polar partnership. The distinction matters when a buyer is comparing demonstrated outcomes with an integration’s intended benefits.
The strategic direction is clear in the partnership’s design: IDBS wants the records it captures to become dependable working material for AI. The commercial test will be whether that arrangement reduces the total effort of producing a reviewed document, including checking the draft.
Start where the measurement happens
There is a practical lesson here that does not require buying IDBS. In a July 2026 article, the company described Chugai scientists taking a direct role in designing an integrated bioprocess database. They defined relationships between data, made structured entry part of daily work and improved the system through user feedback. The people conducting the science helped decide how it should be recorded.
For another laboratory, a sensible starting point is one troublesome handoff: an instrument result copied into a report, or a process revision passed to a manufacturing partner. Establish what information must travel with it, who checks it and how long the handoff takes. Then compare the same task after changing the workflow. That is an editorial inference from the customer examples, and a more testable objective than declaring a laboratory “digital.”
IDBS’s July argument also identifies the condition that can defeat the approach: inconsistent records captured without context remain difficult to reuse, however sophisticated the downstream analytics. Software needs scientists to agree on the meaning of what they enter. The next useful advance may begin with a modest question at the bench: will another person understand this result without having to call its author?