Biotech
Move studies faster. Cut manual review. Keep lab and QA teams out of spreadsheet work.
Your scientists, QA leads, and operators should not be spending nights copying sample updates, rebuilding study trackers, searching old protocols, reconciling CRO invoices, or assembling status decks by hand.
MVP.dev builds AI automation systems for biotech companies that plug into the tools you already use, including Benchling, LabWare LIMS, LabVantage, Thermo Scientific SampleManager LIMS, STARLIMS, Dotmatics, eLabNext, LabArchives, Veeva Vault, MasterControl, Quartzy, SciNote, Smartsheet, NetSuite, QuickBooks Online, SAP, and Coupa.
We help biotech teams reduce manual lab ops work, speed up QA review, improve vendor control, and give scientific leaders cleaner visibility into programs, studies, samples, and spend.
Production-ready in 3 to 4 weeks.
Get a Workflow Fit Assessment See the Biotech Automation DemosBuilt for biotech teams that have the science, but not enough clean operating capacity
This is for discovery teams, preclinical biotechs, translational groups, diagnostics companies, platform companies, CRO-heavy operators, and regulated life science teams that need more throughput without adding more admin burden.
You are likely a strong fit if:
- Your team still updates study trackers, sample logs, or inventory sheets manually
- QA or regulatory review depends on too many document hunts and email threads
- Scientists lose hours searching SOPs, protocols, assay notes, and prior experiment records
- CRO, CDMO, reagent, or sequencing invoices are hard to verify against POs, SOWs, or milestones
- Leadership visibility depends on manual slides pulled from Benchling, LIMS, Smartsheet, and finance tools
- You want workflow help inside your current stack, not a disruptive software migration
If your pipeline is moving but your operations still depend on manual review, manual status updates, and manual document retrieval, there is almost certainly time, cash, and study velocity trapped inside the workflow.
The fastest wins we usually find
Protocol, SOP, and experiment knowledge retrieval
Scientists should not lose 30 minutes looking for the right SOP, prior run condition, reagent note, or assay exception.
We build cited-answer systems that search approved SOPs, protocols, ELN exports, assay development notes, prior experiment records, and internal wikis. Staff ask plain-English questions and get answers with source links back to the exact document or paragraph.
The goal is simple:
- Faster answers from approved sources
- Less repeated work across teams
- Clear source trails for review
- No unsupported answers used in scientific or QA work
For many biotech teams, this can recover 6 to 12 hours a week across scientists, lab ops, and QA by reducing document hunts and repeated internal questions.
See the Cited Knowledge Base Demo →QA and regulatory document triage
Document review slows down when every deviation, CAPA, batch record, SOP change, and evidence packet has to be manually sorted before anyone can act.
We build review queues that classify incoming QA and regulatory documents, extract key details, draft summaries, flag missing evidence, and route items to the right person for human review.
Your team gets a clearer queue:
- What is this item?
- What is missing?
- Who needs to review it?
- What source documents support the summary?
The target is usually a 20 to 30 percent reduction in first-pass review time while keeping QA decisions with qualified humans.
See the QA Triage Demo →R&D portfolio visibility without rebuilding slides
Leadership meetings should not require three people to rebuild the same study status deck every week.
We build operating dashboards that pull from systems like Benchling, LIMS, Smartsheet, Jira, NetSuite, QBO, and CRO trackers, then generate a weekly narrative that shows what moved, what is blocked, what is late, and what needs a decision.
Each update can show:
- Program status by asset, assay, or study
- Open blockers and owners
- Upcoming decision points
- Budget or vendor variance
- Recommended follow-up actions
Small teams often reclaim 4 to 8 hours per reporting cycle and get cleaner decisions because leaders see the same current picture.
See the Executive Dashboard Demo →CRO, CDMO, sequencing, and reagent vendor diligence
Biotech spend leaks when invoices are approved before anyone checks the SOW, PO, milestone, sample count, or expected pricing.
We build vendor review workflows that compare invoices against SOWs, POs, milestone schedules, assay counts, sequencing runs, reagent orders, and vendor history, then generate a short approval memo before payment.
Your team gets a clear answer:
- Does this match the SOW or PO?
- Were the milestones or sample counts expected?
- Is the price outside normal range?
- Should this be approved, reviewed, or escalated?
For CRO-heavy or outsourced lab operations, this can prevent billing errors and save $2,500 to $10,000 per month in avoided overpayments, rework, and manual finance review.
See the Vendor Diligence Demo →Your current tools stay in place
We build the workflow layer around your existing biotech, lab, QA, and finance environment.
Supported and common systems include:
Your ELN stays where it is. Your LIMS stays where it is. Your QMS stays where it is. Your finance system stays where it is.
We add the workflow layer that reads, routes, reviews, summarizes, and writes back only where appropriate and approved.
Three focused offers
Lab Ops Throughput System
For teams losing too much scientific and lab ops time to manual tracking, sample updates, and status reporting.
Includes
- Workflow mapping for lab ops and study tracking
- ELN, LIMS, and spreadsheet source review
- Sample or study status flows
- Exception queues
- Program status dashboards
- Leadership-ready summaries
- Workflow handoff documentation
Best for
- Discovery teams
- Preclinical operators
- Platform biotechs
- Lab ops teams with recurring tracking bottlenecks
QA and Regulatory Review System
For teams manually triaging SOPs, deviations, CAPAs, evidence packets, and controlled documents.
Includes
- Document intake
- Document classification
- Source-grounded summaries
- Missing evidence checks
- Human review queue
- Approval routing
- Audit logs
- Optional write-back to QMS or document systems
Best for
- QA teams
- Regulatory operations
- Diagnostics companies
- GxP-adjacent workflows
- Teams preparing for audits or diligence
Vendor, CRO, and Spend Control System
For biotech teams that rely on CROs, CDMOs, sequencing vendors, reagent suppliers, or outsourced testing and need better control before payment.
Includes
- Invoice and SOW intake
- Vendor matching
- PO and milestone comparison
- Sample count or service validation
- Variance alerts
- Finance review workflow
- Optional payment approval summaries
Best for
- CRO-heavy biotechs
- Outsourced R&D teams
- Preclinical companies
- Finance teams reviewing scientific spend
- Operators managing vendor-heavy programs
Start with one workflow. Prove the ROI. Expand from there.
You do not need a full AI transformation project to get value.
The safest path is to pick one painful workflow, automate it, measure the results, and then expand once the system proves itself.
Most biotech teams start with one of these:
- SOP, protocol, and experiment knowledge retrieval
- QA document triage and evidence review
- R&D portfolio reporting
- CRO, CDMO, and vendor invoice diligence
- Sample or study status tracking
- Inventory and reagent exception alerts
This keeps the first project focused, measurable, and easier to approve.
Typical engagement range
Most biotech automation builds start with a focused pilot.
Starter Workflow Pilot
Best for one high-value workflow
$7,500 to $15,000
Biotech Operations Buildout
Best for multiple workflows across lab ops, QA, finance, or R&D reporting
$15,000 to $40,000
Ongoing Optimization and Support
Best for teams that want continuous improvement, reporting, new workflows, and support
$1,500 to $7,500 per month
Final pricing depends on workflow count, system access, data complexity, approval rules, compliance needs, validation expectations, and whether the workflow writes back into production systems.
The first step is not a giant commitment. It is a workflow fit assessment that identifies the fastest path to measurable ROI.
What waiting costs
Manual biotech workflows do not just waste time. They quietly slow down studies, decisions, and cash control.
Every month you delay:
- Scientists spend time searching documents instead of running experiments
- QA queues grow because first-pass triage is still manual
- Leadership makes decisions from stale trackers
- CRO and vendor issues are caught after invoices hit finance
- Study status depends on heroic spreadsheet upkeep
- New program capacity requires more coordination labor instead of better operating leverage
If your biotech is already stretched, automation is not a nice-to-have project. It is how you create capacity without pulling scientists and QA leaders further away from the work that moves the company forward.
How the engagement works
Week 1: Find the highest-value leak
We review your lab ops workflow, QA review process, study reporting flow, vendor review process, or knowledge retrieval problem.
You get:
- Workflow map
- Automation opportunity ranking
- KPI targets
- Fixed-scope recommendation
- Implementation plan
We define success before building anything.
Week 2: Build against your real workflow
We connect to the systems, documents, exports, SOPs, protocols, vendor records, study trackers, or reporting data needed for the selected workflow.
We build around your actual process, not a generic biotech template.
Week 3: Test, tune, and review
We run real historical examples through the workflow in staging. For document and vendor workflows, this can include prior SOPs, deviations, invoices, SOWs, POs, or study updates so the system can be tested against known outcomes.
You see:
- Accuracy results
- Exceptions
- Failure cases
- Confidence thresholds
- Review queue behavior
- Estimated time savings
Nothing goes live until the workflow is validated against your expectations.
Week 4: Deploy, monitor, and support
We deploy the workflow, train the relevant users, monitor initial results, and support the first production cycle.
You get:
- Production workflow
- Dashboard or review queue
- Documentation
- Admin handoff
- 30 days of support
- Recommendations for the next workflow
Most builds are designed to pay back in 6 to 8 weeks of live use, depending on volume, labor cost, workflow scope, and how much scientist, QA, operations, or finance time is recovered.
Built for sensitive scientific and regulated workflows
Biotech automation has to be controlled, auditable, and careful with IP, study records, quality documents, and regulated data.
Our systems are designed around:
- Least-privilege access
- Encrypted data in transit and at rest
- Human review thresholds for scientific, QA, and regulatory outputs
- Audit logs for AI-assisted decisions
- Source-grounded answers with document trails
- Client-owned cloud deployment when required
- Role-based access for programs, studies, entities, and vendors
- Support for GxP, 21 CFR Part 11, HIPAA, GDPR, SOC 2, and IP confidentiality requirements where applicable
For teams with GxP, 21 CFR Part 11, HIPAA, GDPR, SOC, investor diligence, or intellectual property concerns, we design the implementation around your security and compliance requirements from the start.
Example outcomes
| Workflow | Before | After |
|---|---|---|
| SOP and protocol retrieval | Scientists search folders, ELNs, and old messages | Cited answers from approved documents and experiment records |
| QA document triage | Deviations, CAPAs, and evidence packets sorted manually | Classified queue with missing evidence flags and review routing |
| R&D portfolio reporting | Manual slide updates from LIMS, ELN, Smartsheet, and finance exports | Live dashboard with weekly narrative and decision points |
| CRO and vendor diligence | Invoices checked against SOWs and POs by hand | One-page approval memo with variance and milestone checks |
| Sample and study tracking | Status lives in separate spreadsheets and staff memory | Unified exception queue with owner, blocker, and next-step visibility |
Why MVP.dev
MVP.dev builds AI business operating systems, internal tools, and automation layers for companies that need real operational outcomes, not AI experiments.
You get:
- 25 years of software architecture and delivery experience
- AI automation strategy and implementation under one roof
- Practical systems built around existing workflows
- Human-in-the-loop design where accuracy matters
- Clear KPIs before development starts
- Production-minded delivery, not prototype theater
The goal is not to impress your team with AI.
The goal is to give them time back, reduce operational drag, and help the company move programs forward with the people and systems it already has.
Building the operating playbook too?
Our partners at osforyour.business/biotech cover the org-design, process, and people side of running a modern biotech company. We handle the execution layer that makes it run, so the system they help you design actually delivers the hours back.
Frequently Asked Questions
How much time or money can this save?
It depends on workflow volume, but biotech teams often find the fastest savings in SOP retrieval, QA triage, R&D reporting, and CRO or vendor review. A small discovery or preclinical team may recover 8 to 15 hours per week across scientists, lab ops, QA, and finance. CRO-heavy teams may also prevent $2,500 to $10,000 per month in invoice errors, duplicate charges, milestone mismatches, or manual review cost. During Week 1, we define the target KPIs before the build starts.
Will this work with Benchling, our LIMS, or our QMS?
Yes. The system is built around your current stack. We can work with tools like Benchling, LabWare, LabVantage, SampleManager, STARLIMS, Dotmatics, Veeva Vault, MasterControl, Smartsheet, NetSuite, QBO, and related exports or APIs. We do not require you to move your scientific records into a new platform.
Will this replace our ELN, LIMS, QMS, or finance system?
No. The goal is to improve the workflow around your current tools, not force a migration. Your ELN, LIMS, QMS, and finance systems stay in place. The automation layer reads, routes, summarizes, and writes back only where appropriate and approved.
Can this handle GxP or 21 CFR Part 11 expectations?
We design around your compliance requirements from the start. That can include audit logs, role-based access, human review controls, source trails, electronic record controls, validation support, and client-owned deployment. If a workflow is GxP-relevant, we scope it carefully with your QA or regulatory lead before anything goes live.
Is AI making scientific or QA decisions?
No. The system can classify, summarize, retrieve, route, compare, and prepare recommendations, but scientific, QA, regulatory, and payment decisions stay with the right human owner. For sensitive workflows, outputs should be treated as recommendations until reviewed or approved.
Can this work if our data is messy?
Usually, yes. Many biotech workflows involve a mix of ELN data, LIMS exports, PDFs, SOPs, spreadsheets, email attachments, and vendor documents. Week 1 is partly about finding the workflow where the data is good enough to produce a measurable win. We do not recommend starting with a workflow that needs a full data cleanup before it can pay back.
What is the first step?
Start with a workflow fit assessment. We identify the workflow with the highest ROI potential, estimate the time or cash impact, and recommend a focused pilot. You will know what should be built, why it matters, what it should cost, and how success will be measured before committing to a full implementation.
Get the lab, QA, and finance hours back
If your biotech is still relying on manual study trackers, document hunts, QA triage, CRO invoice checks, or spreadsheet-based leadership reporting, there is likely a faster way to run the operation.
MVP.dev can help you identify the highest-value workflow, build the automation layer, and deploy it inside your current biotech stack.
Get a Workflow Fit Assessment See the Biotech Automation Demos