Scientific Research Software Development
Research data scattered across incompatible systems? Spending 20+ hours weekly on manual data entry? Your team losing months reproducing experiments? We've built research platforms for 30+ academic institutions. Our systems integrate LIMS, ELNs, and AI-powered tools to automate workflows, ensure compliance, and accelerate discovery. Most teams see 40-60% efficiency gains within 12-16 weeks.
Common Industry Challenges
Organizations face unique challenges that impact operations, compliance, and efficiency.
Fragmented Research Data
Description
Research data lives in disconnected silos: lab instruments produce raw files in proprietary formats, LIMS tracks samples, ELNs document protocols, reference managers store papers, and grant systems manage funding. Researchers waste 15-20 hours weekly manually transferring data between systems, transcribing instrument readings, and searching for files across multiple platforms. Data loss happens frequently. Version control is manual or nonexistent. Collaboration across teams or institutions becomes nearly impossible when everyone uses different tools that don't communicate.
Impact
Our Software Solutions
Unified Research Data Platforms
AI-Powered Systematic Literature Review Systems
Compliance-Ready Research Systems
Reproducible Research Workflow Systems
Clinical Trial Management Platforms
Types of Software We Develop
We specialize in complex, data-heavy industrial applications where off-the-shelf software falls short.
Laboratory Information Management Systems (LIMS)
Description
LIMS software manages samples, workflows, and data throughout the research lifecycle. These systems track sample collection, storage location, chain of custody, processing workflows, quality control checks, and results. A well-designed LIMS eliminates manual sample tracking, prevents sample mix-ups, automates workflow routing, ensures compliance with ISO/IEC 17025 and GLP standards, and provides complete audit trails. Custom LIMS solutions integrate with your specific instruments and workflows rather than forcing you into rigid commercial templates. We build LIMS for biobanks managing 100,000+ samples, clinical labs processing thousands of tests daily, and research labs coordinating complex experimental workflows. Integration with barcode scanners, freezer management systems, and analytical instruments eliminates manual data entry.
Key Modules & Features
Built for US & Australian Research Standards & Data Privacy
We ensure compliance with:
• HIPAA Compliance (Health Insurance Portability and Accountability Act)
HIPAA governs research involving protected health information (PHI) in the United States. The Privacy Rule regulates use and disclosure of PHI, requiring informed consent and authorization. The Security Rule mandates technical safeguards for electronic PHI (ePHI) including encryption at rest and in transit (AES-256), access controls with role-based permissions, audit logging of all data access, and secure transmission protocols. Covered entities (universities with health clinics, academic medical centers) and their business associates conducting research with PHI must comply. Violations carry penalties from $100 to $50,000 per violation, up to $1.5M annually per violation category. Research involving medical records, clinical data, or health information requires HIPAA-compliant architecture.
What we do: We architect research systems with HIPAA compliance from day one. Our platforms implement comprehensive technical safeguards: AES-256 encryption for data at rest and TLS 1.3 for data in transit, role-based access controls with multi-factor authentication, complete audit trails logging every access with user identification and timestamps, and secure backup and disaster recovery. We conduct risk assessments identifying vulnerabilities, implement appropriate security measures addressing identified risks, and maintain documentation for audits and compliance reviews. Our Business Associate Agreements (BAA) formalize HIPAA responsibilities. We've built HIPAA-compliant research platforms for academic medical centers, clinical research organizations, and health services research projects handling millions of patient records.
• FDA 21 CFR Part 11 (Electronic Records and Signatures)
FDA 21 CFR Part 11 establishes requirements for electronic records and electronic signatures used in regulated clinical trials and research submitted to the FDA. Key provisions include validation of computer systems to ensure accuracy and reliability, audit trails documenting record creation, modification, and deletion, electronic signatures with controls ensuring authenticity, and record retention preventing deletion or modification. Research involving investigational drugs, medical devices, or biologics requires Part 11 compliance. The regulation applies to electronic systems generating data for regulatory submissions. Non-compliance can result in FDA warning letters, clinical holds, and rejection of regulatory applications. Systems must prevent unauthorized access and maintain data integrity throughout the record lifecycle.
What we do: Our FDA-regulated research platforms implement comprehensive Part 11 controls. We conduct Computer System Validation (CSV) including Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ) with documented evidence. Systems implement tamper-proof audit trails recording all data changes with reason-for-change, secure electronic signatures with user authentication and meaning verification, version control preventing unauthorized modifications, and automated validation of data integrity. We maintain validation documentation including validation plans, test protocols, test results, and traceability matrices. Change control procedures govern system modifications. Our platforms have passed FDA inspections at multiple clinical research sites and pharmaceutical companies.
• NIH Data Management and Sharing Policy
Effective January 2023, NIH requires data management and sharing plans (DMS Plans) for all research generating scientific data, regardless of funding level. Researchers must describe what data will be generated, how data will be preserved and shared, what factors affect data sharing (privacy, intellectual property), and how long data will be available. Data must be shared as soon as possible and no later than time of associated publication. Shared data should include metadata, documentation, and code necessary for replication. Repositories should provide persistent identifiers (DOIs), enable citation, and track data usage. Compliance is monitored during research progress and at project closeout. Non-compliance may affect future funding.
What we do: We build research data management systems that streamline NIH compliance. Our platforms guide researchers through DMS Plan creation with templates and examples, track data collection and curation throughout the research lifecycle, prepare datasets for deposit including metadata generation and documentation, and facilitate sharing through integration with approved repositories (Figshare, Dryad, institutional repositories). Systems assign DOIs for dataset citation, track data sharing status across grants, generate compliance reports for NIH progress reports, and implement appropriate access controls protecting sensitive data while maximizing sharing. De-identification tools remove or redact PHI, PII, and confidential information. Embargo features protect data until publication while planning for eventual release.
• NSF Public Access Requirements
The National Science Foundation requires researchers to share primary data, samples, and supporting materials underlying published research. Data must be deposited in public repositories or made available through other means within a reasonable timeframe. Researchers must include data management plans describing types of data generated, standards for data format and metadata, policies for access and sharing, and provisions for long-term data preservation. NSF expects data to remain accessible for at least three years beyond the award period or until publication, whichever is later. Shared materials should be sufficient for other researchers to validate and replicate findings. Restrictions on data sharing must be justified based on legal, ethical, or technical constraints.
What we do: Our research data platforms address NSF public access requirements comprehensively. Systems capture data management information throughout research projects, prepare datasets meeting community standards (format, metadata, documentation), facilitate deposit to appropriate repositories (discipline-specific or generalist), and track data sharing compliance across NSF grants. We implement access controls supporting different sharing models: immediate public release, time-limited embargo, or restricted access with approval workflows. Metadata generation follows standards relevant to each research domain (Darwin Core for biodiversity, CF conventions for climate data, discipline-specific ontologies). Integration with institutional systems links datasets back to NSF awards for reporting. Analytics track data reuse demonstrating research impact.
Important: StepInsight provides compliance support by building software that meets technical requirements of regulations like HIPAA, FDA 21 CFR Part 11, Privacy Act, and TGA standards. We implement encryption, access controls, audit trails, and validation documentation. However, compliance is a shared responsibility. You remain responsible for operational policies, user training, institutional oversight, and ensuring appropriate use of systems. We are not legal advisors and recommend consulting with compliance specialists and legal counsel for your specific regulatory requirements.
Technologies & Integrations
| System Type | Common Tools | Our Capabilities |
|---|---|---|
| Laboratory Information Management Systems (LIMS) | LabWare, STARLIMS, Thermo Fisher SampleManager, Benchling, LabVantage, Custom LIMS | Bidirectional integration for sample tracking, automated data transfer from LIMS to research databases, integration with barcode scanners and freezer management, workflow status updates and notifications, query LIMS for sample availability and location |
| Electronic Lab Notebooks (ELN) | Benchling, LabArchives, PerkinElmer E-Notebook, RSpace, SciNote, Jupyter Notebooks | Protocol and experiment documentation import/export, automated data insertion from instruments and databases, electronic signature integration for 21 CFR Part 11 compliance, search and retrieve experimental records, attachment and file management |
| Electronic Health Records (EHR) | Epic, Cerner, Meditech, Allscripts, athenahealth, Practice Fusion | Clinical data extraction for research (demographics, diagnoses, medications, labs, procedures), HL7 v2 and FHIR API integration for real-time data exchange, EHR-based cohort identification and patient recruitment, consent documentation and authorization tracking, results return to EHR for clinical decision support |
| Statistical Analysis Software | R, Python (pandas, scikit-learn), SAS, Stata, SPSS, MATLAB | Direct database connectivity for data analysis, automated report generation and visualization, integration of analysis code with data provenance tracking, reproducible analysis workflows with versioning, export results and figures to databases and reporting systems |
| High-Performance Computing (HPC) | Slurm, PBS, LSF job schedulers, AWS Batch, Google Cloud Batch, Azure HPC | Job submission and monitoring for computational workflows, integration with bioinformatics pipelines (Nextflow, Snakemake, CWL), data transfer to/from HPC storage, resource allocation and cost tracking, result retrieval and database loading |
Custom vs. Off-the-Shelf Software
Understanding the differences helps you make the right choice for your organization.
Details:
Seamless integration with your specific instruments, LIMS, ELNs, and institutional systems. APIs connect everything automatically.Details:
Limited integrations with mainstream tools only. Custom instruments and internal systems require manual data transfer.Details:
Workflows match how your team actually works. Software adapts to your processes, not vice versa.Details:
Generic workflows designed for average lab. You adapt your processes to fit the software's limitations.Details:
Purpose-built for your regulatory requirements. We architect compliance into the system from day one with documentation.Details:
Compliance features may or may not match your specific requirements (HIPAA, 21 CFR Part 11, TGA). You're responsible for configuration.Details:
You own your data completely. Deploy on-premise or in your cloud. Export anytime in standard formats.Details:
Your data lives in vendor's cloud. Export may be limited or in proprietary formats. Vendor lock-in is common.Details:
Custom AI/ML models trained on your specific research domain. RAG systems use your literature corpus for systematic reviews.Details:
AI features are generic and not trained on your data. Limited customization of algorithms or models.Details:
No licensing fees, you own the software. Support and enhancements on your schedule at predictable costs.Details:
$5k-$50k per user annually for licensing. Support costs extra. Feature requests rarely implemented.Trusted by Research Institutions Worldwide
Certifications & Expertise
- Google Cloud platform expertise
- ISO 27001 Information Security
- HIPAA Business Associate certified
Industries Served
- Academic Research Universities
- Clinical Research Organizations
- Government Research Agencies
- Pharmaceutical and Biotech
- Medical Research Institutes
- Environmental Research Labs
Services
- Research Data Management Platforms
- LIMS and ELN Development
- Clinical Trial Management Systems
- AI-Powered Literature Review Tools
- Bioinformatics Pipeline Development
- Regulatory Compliance Solutions
Technology Stack
Ready to Accelerate Your Research?
Research data scattered across incompatible systems? Manual processes consuming 15+ hours weekly? Struggling with compliance requirements? We've helped 30+ research institutions solve these exact problems. Our custom platforms integrate your tools, automate workflows, ensure compliance, and accelerate discovery. Most teams see 40-60% efficiency gains within 12-16 weeks. Let's discuss what's possible for your research.
Frequently Asked Questions
We build comprehensive research platforms including LIMS (Laboratory Information Management Systems) for sample tracking, ELNs (Electronic Lab Notebooks) for protocol documentation, research data management systems for data lifecycle management, clinical trial management systems (CTMS) for coordinating multi-site trials, electronic data capture (EDC) for clinical research, AI-powered systematic literature review tools using RAG technology, bioinformatics pipelines for genomic data analysis, biobanking and specimen management systems, data integration platforms connecting instruments and databases, and institutional repositories for data sharing and preservation. Every system is custom-built for your specific research workflows, compliance requirements, and institutional systems.
Research software projects typically range from $75,000 to $300,000 depending on scope and complexity. A basic LIMS or ELN for a single lab might be $75k-$125k. Multi-site clinical trial systems typically run $150k-$250k. Enterprise research data platforms for large institutions can be $250k-$500k+. Cost factors include number of integrations (LIMS, ELN, EHR, instruments), data volume and complexity, regulatory compliance requirements (HIPAA, 21 CFR Part 11, TGA), number of users and sites, and custom features like AI/ML capabilities. We provide fixed-price quotes after understanding requirements. Most research organizations see ROI within 12-18 months through efficiency gains. Unlike commercial software with annual licensing ($5k-$50k per user), you own the system outright with no recurring fees.
Typical research software projects take 12-16 weeks from requirements gathering to production deployment. Simple LIMS or data integration projects can be delivered in 8-10 weeks. Complex multi-site clinical trial systems may take 16-24 weeks. Our phased approach delivers working software incrementally: weeks 1-2 for requirements and technical architecture, weeks 3-8 for core platform development, weeks 9-12 for integrations and advanced features, weeks 13-14 for testing and validation, and weeks 15-16 for deployment and training. You see progress every 2 weeks with demos of working features. Mission-critical features are prioritized first so you get value early. Implementation is faster than commercial software (4-12 months) because we build exactly what you need without unnecessary features.
Yes. We architect health research systems with HIPAA compliance from day one. Our platforms implement comprehensive technical safeguards: AES-256 encryption for data at rest, TLS 1.3 for data in transit, role-based access controls with multi-factor authentication, complete audit trails logging every access with user ID and timestamp, automatic session timeouts and access revocation, secure backup with encryption, and disaster recovery procedures. We conduct risk assessments identifying vulnerabilities, implement security measures addressing identified risks, and document everything for audits. Our Business Associate Agreements (BAA) formalize HIPAA responsibilities. We've built HIPAA-compliant systems for academic medical centers handling millions of patient records, clinical research organizations managing multi-site trials, and health services research projects. Our platforms pass institutional security reviews and external HIPAA audits regularly.
Yes. We've integrated with 100+ instrument types from major vendors including mass spectrometers (Agilent, Thermo Fisher, Waters), DNA sequencers (Illumina, Oxford Nanopore, PacBio), microscopes (Zeiss, Nikon, Leica, Olympus), flow cytometers (BD, Beckman Coulter, Miltenyi), plate readers (BioTek, Molecular Devices, PerkinElmer), and liquid handlers. Our integrations automatically capture data from instrument output files, parse proprietary formats (Agilent .d, Thermo .raw, AB SCIEX .wiff, Illumina BCL), apply quality control checks and validation, load results into your database with complete metadata, and provide real-time instrument status monitoring. We eliminate manual transcription from instrument screens or USB drive shuffling. For instruments without APIs, we build custom parsers. Integration typically takes 2-4 weeks per instrument type. The result is seamless data flow from instrument to database in real-time.
Yes. Our AI-powered systematic review platforms use machine learning and RAG (Retrieval-Augmented Generation) to automate the most time-consuming aspects of literature reviews. The system searches multiple databases simultaneously (PubMed, Scopus, Web of Science, Cochrane, IEEE, arXiv), applies machine learning trained on your inclusion/exclusion criteria to screen thousands of papers (reducing manual screening by 85-90%), uses RAG to extract study characteristics, methods, outcomes, and quality indicators automatically, identifies relevant papers you might have missed through semantic search, flags duplicates across databases, generates structured data extraction tables, and provides natural language queries to find specific information across your corpus. Researchers review AI recommendations rather than manually screening every paper. The system learns from your decisions, improving accuracy over time. We've helped research teams complete 6-month systematic reviews in 2-3 weeks with higher quality and less selection bias than manual reviews.
Australian research projects must comply with the Privacy Act 1988 and Australian Privacy Principles (APPs). We implement privacy-by-design principles: data minimization collecting only necessary personal information, purpose limitation restricting use to specified research purposes, security safeguards including encryption and access controls, consent management tracking permissions and withdrawal requests, and support for access and correction requests from participants. Our systems implement APP 95 provisions for health research including ethics committee approval tracking and heightened security measures. For clinical trials, we ensure TGA and ICH GCP compliance including ALCOA+ data integrity principles and audit trails. Privacy impact assessments identify risks before deployment. Breach detection and notification workflows meet Notifiable Data Breaches scheme requirements. We've built compliant systems for Australian universities (University of Sydney, Monash, UQ), government agencies (CSIRO, NHMRC-funded projects), and research institutes. Systems pass institutional ethics and security reviews.
Reproducibility requires capturing complete research provenance automatically. Our platforms implement comprehensive provenance tracking: every experiment gets a unique persistent identifier, protocols live in version control with complete change history and rollback, code repositories (Git) integrate with execution environments capturing software versions and dependencies, data processing pipelines document every transformation step with parameters and timestamps, computational notebooks (Jupyter, R Markdown) embed in the platform with one-click reproduction, and container technology (Docker, Singularity) ensures code runs identically across systems. Negative results are preserved alongside positive findings. Data lineage shows exactly how each result was produced from raw data through final analysis. Researchers can reproduce any experiment months or years later by loading the saved environment. External researchers can validate findings with complete transparency. We've helped labs achieve 95%+ experiment reproducibility rates and reduced method transfer time between teams by 60%. Reproducibility isn't an afterthought—it's built into every workflow.
Yes. NIH's Data Management and Sharing Policy (effective January 2023) and NSF public access requirements mandate data sharing for funded research. Our research data management platforms streamline compliance: DMS plan generators with templates and examples specific to your research domain, data lifecycle tracking from collection through preservation, metadata generation following community standards (Dublin Core, DDI, domain-specific ontologies), integration with approved repositories (Figshare, Dryad, Zenodo, institutional repositories, domain repositories), DOI assignment making datasets citable and trackable, access control supporting different sharing models (immediate public release, time-limited embargo, restricted access with approval), de-identification tools removing or redacting sensitive information, and compliance dashboards showing data sharing status across grants for progress reporting. Automated reporting generates required information for NIH/NSF progress reports and closeouts. We help research organizations meet funder mandates while protecting sensitive data and maximizing research impact through appropriate data sharing. Our platforms handle hundreds of grants with 100% compliance.
Yes. Comprehensive training and documentation are critical for adoption and success. We provide role-based training customized for different users: researchers and lab scientists on day-to-day workflows and data entry, principal investigators on oversight, reporting, and compliance features, IT administrators on system administration, backup, and maintenance, and compliance officers on audit trails, access controls, and regulatory features. Training includes live sessions (in-person or remote), hands-on exercises with your actual workflows and data, video tutorials for reference, and quick reference guides and job aids. Documentation includes user manuals with step-by-step instructions and screenshots, administrator guides for configuration and maintenance, API documentation for integrations and extensions, and technical architecture for IT staff and future developers. Training typically occurs 1-2 weeks before go-live when the system is stable. We provide office hours support during the first 4-8 weeks as users get comfortable. Additional training for new staff is included in support agreements.
Research data is irreplaceable and we take security and backup seriously. Our platforms implement comprehensive security controls: encryption at rest (AES-256) and in transit (TLS 1.3), role-based access controls with principle of least privilege, multi-factor authentication for sensitive systems, complete audit trails logging every data access and modification, intrusion detection and prevention systems, regular security scanning and penetration testing, and patch management keeping systems current. Backup strategy includes automated daily incremental backups with weekly full backups, geographically distributed backup storage (different data centers/regions), point-in-time recovery allowing restoration to any moment, backup encryption and access controls, regular backup restoration testing (quarterly), and retention policies meeting regulatory requirements (5-15+ years). For critical systems, we implement high availability and disaster recovery including redundant servers and databases, automated failover within minutes of outage, and disaster recovery plans with tested procedures. Data centers meet compliance standards (SOC 2, ISO 27001). Cloud deployments use Google Cloud, Azure, or GCP with multi-region configurations.