Our biotech software consultants map user workflows, data, instrument interfaces, and requirements and weigh build-versus-buy. Where AI is in scope, that can extend to an AI/Gen AI readiness assessment, product discovery, or a PoC—or, for an existing product, prioritization of what to rebuild and in what order.
When off-the-shelf tools can’t handle your workflow or data, ITRex takes on custom biotech app development—web and mobile software for research, lab, and clinical operations that puts a usable interface over complex scientific logic. We integrate it with the systems you already run, so the result fits your stack instead of adding another silo.
Lab hardware is only useful when it runs reliably and its data reaches the software your team works in. ITRex covers the full stack: electronics prototyping and embedded systems (firmware and middleware) that run the instrument, HMIs for operating it, edge AI for processing data on the device, and connectivity solutions that link it to your applications.
Studies and datasets are only an asset when your team can find, compare, and reuse them. ITRex builds the research platforms that make that possible—and the data foundation underneath: data architecture, integration and pipelines, lakes/lakehouses and warehouses, and management and governance for provenance and access control.
When a legacy or prototype platform is impeding your progress, our biotech software developers can help you transition it to maintainable, commercial-grade software by migrating data, preserving metric definitions and workflows, and managing the transition in stages to ensure critical operations continue to run.
In regulated biotech, software that fails quietly is a liability. ITRex runs manual, automated, and Gen AI testing across applications and data pipelines, adds AI model validation for the models behind them, and applies responsible AI practices in higher-stakes cases. We test controls against applicable HIPAA, GDPR, and FDA 21 CFR Part 11 requirements and implement HL7/FHIR where healthcare data exchange is involved.
Retrieval-augmented generation (RAG) can help researchers find information across approved literature and internal documents, with answers linked to their sources. We design access controls, test retrieval quality, and establish a process for updating the content and search index as research changes. Your team can then check the cited material before using an answer.
We develop biotech research software that helps scientists explore genomic, transcriptomic, proteomic, and other omics datasets together. Depending on the question and data quality, analysis and visualization tools can surface patterns or candidates for further investigation, while researchers interpret and validate the findings.
Computer vision can help teams detect, classify, and quantify features in microscopy and other scientific images—counting cells, spotting colonies, segmenting structures, or scoring stained tissue. As part of our biotech software development, we train the models, build the software that runs them, and connect the analysis to the systems where your researchers review and use the results.
AI agents can take on repetitive steps—sorting incoming experimental data, flagging anomalous results, and drafting documentation like deviation reports for review. We define where staff approve outputs, how changes are logged, and what testing the workflow needs before deployment, with the degree of autonomy set by the intended use.
Our biotech software development company can build and integrate digital products around scientific workflows, research data, and connected lab equipment. The solutions we develop for our clients support work from experiment planning and instrument control to data analysis and regulated documentation.
Biotech software development entails the creation, engineering, integration, and maintenance of digital systems used in biological research, laboratory operations, product development, and other regulated workflows. It may include scientific data platforms, laboratory and instrument software, LIMS and ELNs, analytics, cloud infrastructure, and AI-powered research tools. Custom software development for biotechnology companies typically combines several of these components to support a specific research, lab, or product workflow.
Biotech companies typically use multiple types of software concurrently: laboratory information management systems and electronic lab notebooks for day-to-day lab work, research data platforms for storing and comparing studies, instrument control and embedded software for connected hardware, and quality management systems for regulated processes. Larger organizations frequently use clinical trial management systems, ERP, and CRM alongside their EMR/EHR systems, as well as bioimage analysis and organ-on-a-chip applications for specialized research. Enterprise software development for biotech companies typically means connecting these systems across multiple sites, teams, or acquired entities, where data formats and definitions rarely match by default.
The features to include in biotech application development start with the workflow, not a checklist. Most biotech applications need role and study management to control who can do what, instrument integration and data validation to get clean data in, search and analytics to make sense of it, and reporting, audit trails, and APIs to get it back out to the people and systems that need it.
When standard features and integrations align with the workflow, off-the-shelf software is typically the better option. Custom software development is more appropriate for biotech companies when proprietary workflows, unusual data, specialized instruments, or commercialization requirements cannot be addressed through configuration. A hybrid solution combines an existing product with customized modules, interfaces, and connectors.
Choose a biotech software development company that can demonstrate relevant experience with scientific data, laboratory instruments, security, and software validation—not merely general healthcare expertise. Ask how the team moves from discovery to production, approaches integration and testing, documents technical decisions, and supports the software after launch. When considering how to hire a biotech application development company, also clarify source code and IP ownership, knowledge transfer, and your team’s ability to maintain the product after handover.
For startups, the best biotech software development company will offer staged discovery, prototyping, and clear go-or-stop points before committing the full budget. A biotech software development company for enterprises should also be equipped to modernize legacy systems, govern complex integrations, work within existing data and cloud environments, and document controls for regulated workflows.
How much biotech application development typically costs depends on the product scope, integrations, scientific data, and validation requirements. A discovery phase or focused prototype usually costs $15,000–$40,000, an MVP or instrument-integration project runs $60,000–$150,000, and a full research or commercial platform build typically starts around $200,000 and scales with instrument count, data volume, and regulatory validation work. After discovery, ITRex provides a phased scope, delivery team, timeline, and estimate specific to your project.
A discovery phase or feasibility study—mapping workflows, data, requirements, and technical risks before full-scale engineering begins—typically takes two to six weeks. A focused prototype or PoC, like a single instrument integration or an AI feature test, usually takes six to twelve weeks to build. Developing a custom LIMS or electronic lab notebook from scratch for a single site often takes four to six months; adding multiple instrument integrations, data migration from an existing system, or formal validation can extend that timeline to eight months or more. A full research platform or multi-site enterprise system, which involves multiple labs, legacy data, and regulated workflows, typically takes nine months to a year and a half to implement. Scope, not industry, drives the number: the same LIMS can ship in six weeks configured out of the box or take a year custom-built with extensive integration and validation work.
Biotech software can integrate with laboratory systems through device APIs, middleware, message protocols, and custom connectors. Our biotech software development services for lab automation solutions connect instruments with LIMS, ELNs, cloud platforms, and data pipelines, reducing the manual transfer of measurements, device status, and experiment records. Depending on the laboratory workflow, our biotech application development services for research laboratories may also cover embedded software and HMIs for controlling and monitoring equipment.
Biotech software solutions manage research data through structured ingestion, validation, metadata, provenance and lineage, access controls, search, visualization, and analysis. Biotech software development for genomic data management must also account for specialized data formats, large data volumes, computational workflows, and the ability to trace and reuse datasets across studies. The same foundation supports biotech application development for genetic analysis platforms and other data-intensive research products.
The requirements depend on the data and intended use. Examples include the HIPAA Security Rule for electronic protected health information, GDPR security requirements for personal data, and FDA 21 CFR Part 11 controls for certain FDA-regulated electronic records and signatures. ISO 27001 may also provide a framework for managing information security where required by the organization or contract.
At the system level, these requirements may translate into risk assessments, role-based access and least privilege, multifactor authentication, encryption in transit and at rest, audit trails, backups and disaster recovery, incident response, vulnerability testing, and documented data-retention and deletion procedures.
When deciding how to outsource biotech software development securely, define NDAs and data-processing terms, IP and source-code ownership, breach-notification duties, approved subprocessors, restrictions on third-party AI use, knowledge transfer, data return or deletion, and access revocation in the contract. A HIPAA business associate agreement may also be required when a vendor handles ePHI.
AI can help biotech teams analyze scientific images, find patterns in experimental data, search research materials, flag anomalies, and identify results that deserve closer examination. In drug discovery workflows, these capabilities can work alongside experimental-data management, study comparison, and visualization—supporting researchers instead of replacing their judgement.
AI-powered software development for biotech companies is therefore as much about safeguards as models. Our AI-powered biotech software development services define evaluation criteria, monitoring, and human-review points before deployment. Gen AI software development for biotech companies can add source-grounded research, literature summaries, natural-language data queries, and draft documentation. When building custom biotech software with Gen AI capabilities, we also introduce access controls, source citations, output testing, and approval steps so generated content remains supporting evidence rather than an unverified conclusion.
Modernization starts with understanding which parts of the existing system still work, which create risk, and which prevent the product from growing. Biotech software development companies should assess the architecture, data flows, integrations, dependencies, security gaps, and critical laboratory workflows before recommending a rebuild.
Depending on the findings, the biotech software modernization project may cover redesigning the interface, exposing legacy functions through APIs, restructuring databases, moving selected workloads to the cloud, adding automated tests, or replacing individual modules. Data and integrations are migrated in stages, with parallel runs and rollback plans where disruption would affect active studies or laboratory operations. This phased approach keeps essential workflows available while the platform moves toward a more maintainable architecture.
An organ-on-a-chip platform needs software for operating the instrument and managing the research around it. The instrument layer may control microfluidic flow and mechanical forces, monitor experiment conditions, and capture measurements in real time. The application layer can support protocol design, study management, data analysis, and visualization, while a cloud platform makes it possible to organize, search, and compare studies.
ITRex has worked across both layers. For Emulate, we developed embedded controls for a culture module and software for planning, running, and analyzing studies. For Numa Biosciences, we modernized a cloud platform that helps researchers search and compare organ-on-a-chip studies, datasets, and experimental designs.
