SaaS Development Company in Boston

Biotech / Life Sciences

A Boston-based biotech SaaS startup (Seed, $6 million raised, founded by a former Moderna data scientist and a former Broad Institute bioinformatician) was building a research data management platform — SaaS that helps biotech companies centralise, organise, and share research data across teams and projects. The founding team had built a functional prototype that researchers at 3 biotech companies used for 6 months. The prototype's problems: no FDA compliance (the moment research data is used to support regulatory submissions, the platform must meet 21 CFR Part 11 — the prototype had no audit trails, no electronic signatures, and no validated workflows), data silos (the platform stored structured data but couldn't handle the variety of research data — genomic sequences, microscopy images, flow cytometry files, mass spectrometry data — that biotech companies generate), no collaboration features (researchers worked in isolation — no version control, no commenting, no project-level organisation that enables team science), poor search (finding specific experiments or datasets required knowing exact file names — no metadata-based search, no full-text search of experimental notes), and no institutional controls (no role-based access, no data classification, no export controls — biotech companies have IP-sensitive data that requires strict access management). The startup needed to close 8 enterprise deals within 12 months to demonstrate product-market fit for a Series A.

We rebuilt the platform as an enterprise research data management SaaS with FDA compliance, multi-format data support, and scientific collaboration features. (1) Universal data ingestion: the platform accepts any research data format. Structured data: CSV, Excel, and database exports with automatic schema detection and data type inference. Genomic data: FASTQ, BAM, VCF, and other bioinformatics formats — with metadata extraction (sequencing platform, read length, coverage, reference genome). Imaging data: microscopy (TIFF, OME-TIFF, CZI), flow cytometry (FCS), and medical imaging (DICOM) — with thumbnail generation and in-browser preview. Mass spectrometry: mzML, RAW, and vendor-specific formats — with automatic extraction of acquisition parameters and peak lists. Documents: PDFs, Word documents, and lab notebook exports — with full-text indexing for search. Each file is automatically classified by data type, tagged with extracted metadata, and linked to the researcher, project, and experiment that produced it. (2) Scientific metadata model: every dataset is described by a flexible metadata schema that captures: experimental context (what was the hypothesis, what methodology was used, what were the conditions), sample information (what biological samples — cell lines, patient samples, animal models — were used, with full provenance), instrument parameters (which instruments generated the data, with settings and calibration information), analysis provenance (which software, algorithms, and parameters were used for analysis — enabling reproducibility), and custom metadata (biotech companies define their own metadata fields specific to their research — the schema is extensible without code changes). (3) 21 CFR Part 11 compliance: implemented for data that may support regulatory submissions. Audit trail: every action (upload, download, view, edit, delete, share) logged with user ID, timestamp, IP address, and action details — immutable audit log that satisfies FDA inspection requirements. Electronic signatures: for data approval workflows — e.g., a study director signing off on a dataset — using password + knowledge-based authentication. Access controls: role-based (researcher, study director, quality, administrator) with project-level permissions — ensuring researchers access only the data they're authorised for. Validated workflows: data review and approval workflows that follow documented procedures — datasets move through states (draft → under review → approved → locked) with signature requirements at each transition. Data integrity: ALCOA+ compliance — every piece of data is attributable (linked to the user who created it), legible (displayed correctly with units and context), contemporaneous (timestamped at creation), original (source data preserved without modification), and accurate (validated against data type specifications). (4) Scientific search: researchers can find data through: metadata search (find all RNA-seq experiments on HEK293 cells from Q3 2025 — structured query on metadata fields), full-text search (search across experimental notes, file names, and extracted text from documents), similar data (find datasets similar to a reference dataset — using metadata similarity and data characteristics), and saved searches (frequently used queries saved and shared across teams — enabling standard data views). (5) Collaboration: version control for datasets (branching and merging — researchers can create derivative datasets without modifying the original), comments and annotations (threaded discussions on datasets, experiments, and specific data points), project organisation (datasets grouped by project, with project-level access control, timelines, and milestones), and sharing (controlled sharing with external collaborators — time-limited access, view-only or download permissions, and watermarked exports for IP protection). (6) Enterprise features: SSO (SAML 2.0 for biotech enterprise identity management), data classification (public, internal, confidential, restricted — with access controls enforced per classification), export controls (preventing unauthorised data export — download restrictions, DRM for sensitive documents, and IP watermarking), and storage tiering (hot storage for active data, cold storage for archived data — optimising cost for biotech companies with petabytes of genomic data).

Enterprise adoption: 12 enterprise deals in 12 months (50% above the 8-deal target), totalling $2.4M ARR. Average deal: $200K ARR. Customer profile: Series B+ biotech companies (100-500 employees, $50M-$500M revenue). Data onboarding: average customer migrates 15TB of research data to the platform within 60 days. The universal data ingestion handles 94% of file types without custom configuration — the remaining 6% require metadata schema customisation (completed within 1 week). Researcher adoption: 2,800 active researchers across 12 customers. Average daily usage: 42 minutes per researcher. The search functionality is the most-used feature — researchers previously spent 30+ minutes finding specific datasets, now average 2.3 minutes. FDA compliance: 4 customers have used platform data in FDA submissions (IND applications and annual reports). All 4 passed FDA inspection without data integrity findings — the audit trail and electronic signatures satisfied the inspectors. One customer's FDA reviewer specifically noted "excellent data traceability" in the review feedback. Collaboration impact: datasets with multiple collaborators (indicating team science) increased 3.2x after platform adoption. Researchers report that the version control and commenting features enabled collaborative analysis that was previously impossible with file shares and email. IP protection: zero data breach incidents across all customers. The export controls and data classification features were cited by 8 of 12 customers as "essential for IP management" — biotech companies handle pre-patent data worth hundreds of millions. Time savings: researchers save an average of 8 hours per week on data management tasks (finding data, organising files, preparing data for sharing, and documenting experiments). At a burdened researcher cost of $120/hour, this is $49,920 per researcher annually — for a customer with 200 researchers, the platform saves $10M in researcher productivity annually against a $200K platform fee. Series A outcome: the 12 enterprise deals, FDA validation track record, and researcher productivity data contributed to a $35 million Series A. Development investment: $480,000 over 18 weeks.