Design fewer, higher-value experiments and optimize conditions.
“Which experiment should we run next?”
PROGRAMS & TEAMS
Eight modalities. Six roles. One platform.
BioMedAna adapts to the scientific modality and the role making the decision — while keeping the same connected data, prediction and governance foundation across every program.
SHARED EVIDENCE“Which experiment should we run next?”Run 3 high-value conditions instead of 12
01 — EIGHT MODALITIES
Modality-native.
Not one-size-fits-all.
Each modality pack preserves its own process topology, terminology, CPPs, CQAs, assays, kinetics and scale rules on one governed platform foundation.
CHO · mAb
Monoclonal antibodies
Growth, metabolism, feeding, mass transfer, titer and CQAs.
HUB CONNECTS
- Cell growth and viability
- Feed, metabolites and reactor conditions
- Chromatography, recovery and quality assays
TWIN PREDICTS
- Culture kinetics and hybrid prediction
- Feed and operating-window comparisons
- Scale-dependent process assessment
Explore the dedicated modalities guide ↗
Multi-program and CDMO deployment is an Enterprise capability, not a scientific modality.
02 — SIX ROLES
Every role.
A decision, not just a label.
De-risk scale-up, transfer and comparability.
“Will this process transfer successfully to the next scale?”
Analyze batch behavior, trends and investigate deviations.
“Is this batch moving toward a deviation?”
Govern reusable, model-ready data products and integrations.
“Is our process data complete and model-ready?”
Trace evidence, calculations and conclusions for every decision.
“Can we trace this conclusion back to its source?”
Monitor process maturity, development risk and portfolio capacity.
“Where is our biggest process or capacity risk?”
03 — ROLES × MODALITIES
Same platform.
Different decision, every time.
A role’s decision does not change across modalities — the evidence, models and terminology behind it do.
Skip card journey ↓Start with the team’s decision01 / 04 ↓ SCROLL TO EXPLORE Process Development in cell therapy MSAT in gene therapy Manufacturing Sciences in fermentation Quality & CMC in mAb production
Compare expansion strategies using donor and process-stage evidence.
De-risk AAV/lentiviral scale-up and tech transfer with vector-yield models.
Investigate an oxygen-limitation deviation using OUR/OTR history.
Trace a CQA shift back to its source run, material and assay.
WHO IT'S FOR
Designed around the decisions
your teams own.
The value of intelligence is realized in the work of Process Development, MSAT and Manufacturing, alongside Quality, Regulatory Affairs and CMC. Analytical and Digital teams participate throughout. See the full framework on Programs & Teams.
Process / accountable team
Application
Business value
Process developmentHeads of PD / R&D
Connect upstream, downstream and analytical evidence. Investigate process drivers and explore scenarios.
Prioritize experimentation and retain learning.Scale-up & transferHeads of MSAT / Tech Transfer
Carry knowledge between scales, sites and teams. Organize the evidence behind transfer decisions.
Identify risks earlier and preserve process context.Manufacturing improvementHeads of Manufacturing / Operations
Compare batches, investigate variability and connect conditions with outcomes.
Support consistent performance and improvement.Quality & regulatory readinessHeads of Quality / Regulatory Affairs / CMC
Organize supporting evidence, summarize findings and prepare documentation for expert review.
Strengthen evidence access and review readiness.Executive priorities Productivity / Risk / Knowledge continuity
BUSINESS OUTCOMES
Accelerate progress.
Multiply business value.
Turn scientific depth and coordinated execution into greater team capacity, stronger processes and better use of development and manufacturing resources.
Bring historical evidence, DoE analysis, scientific models and recommendations into experiment planning to focus effort and reduce avoidable iteration.
Evaluate process behavior, equipment differences and evidence gaps before committing to costly engineering runs.
Connect PAT, process and analytical evidence with investigations and simulations to identify variability drivers and evaluate improvements.
Coordinate specialist agents across analysis, investigations, evidence mapping and reporting so experts can accomplish more.
Carry biological context, experimental learning, models and decision rationale across programs, teams and sites.
Measure against your baseline
Experiment cycle timeInvestigation effortTransfer preparation timeYieldBatch variability
BEFORE YOU COMMIT
Make the pilot measurable.
Agree on one workflow, its evidence and the success criteria before expanding across teams.
Define the decision
Select a process, accountable team and baseline metric: investigation effort, experiment cycle time or transfer preparation time.
Check the data and controls
Review available runs, assays, source interfaces and data permissions. Confirm security requirements and any validation scope.
Review a real result
Assess evidence completeness and scientific usefulness with your experts. For predictive models, agree on held-out evaluation and acceptable uncertainty.
An 8–12 week initial-value target depends on agreed scope, data readiness, integrations and customer review. Sandbox access is a separate evaluation request.
Is the sandbox an instant free trial?
No. It is a free access request reviewed by the team, typically within 24 business hours. Use non-sensitive data and review the sandbox terms before requesting access.
Request free sandbox access ↗How do we evaluate pricing and return on investment?
The value calculator estimates potential benefits, not subscription pricing or net ROI. Request a scoped commercial proposal and include software, integration, internal effort and validation costs in your business case.
Discuss scope and pricing ↗What evidence can we review?
The product previews use illustrative data. Independent research supports the underlying methods; customer-specific performance needs evaluation against your own evidence and acceptance criteria.
Read the methods and limitations ↗Who is behind BioMedAna?
BioMedAna is a product of OpenAna. For investment, partnership or company diligence, contact the team to request company background and supporting materials.
Contact OpenAna about BioMedAna ↗04 — QUESTIONS WE GET OFTEN
Straight answers on programs & teams.
Is Multi-program/CDMO a modality?
No. Multi-program and CDMO is an organization and deployment pattern, governed by BioMedAna OS, not a scientific modality.
Can one team work across multiple modalities?
Yes. The same role framework and platform foundation apply regardless of how many modalities a team supports.
Do all six roles need to be present to use BioMedAna?
No. Most teams start with one or two roles and one modality, then expand as evidence and trust accumulate.
Start with your program
Bring us your modality, team and the decision you can’t answer fast enough.
We will map the evidence, modality context and intelligence path required to support it.
Talk to BioMedAna ↗