AI-powered experimental optimisation

Reduce experiments.Accelerate results.

Turn your experimental data into better decisions and prioritise each run where it can add the most value.

Get started with DxTER → Request a demo
From €25 /user/month Launch offer
No commitment Scale users according to your team
Secure web platform No local installation
THE CHALLENGE

Finding the best conditions in complex spaces

In R&D, every factor multiplies the possible combinations. When several objectives and constraints are involved, deciding what to test next can require many iterations before a clear direction emerges.

Many variables

Each new factor adds new combinations to explore.

Competing objectives

Improving one response may compromise others.

Complex constraints

They define which combinations are feasible and narrow the search space.

As the experimental space grows, the impact reaches the business
More experiments

More trials are needed to narrow down which options are worth pursuing.

More time

The cycle between testing, analysis and the next decision takes longer.

Higher cost

Additional iterations increase resource consumption and equipment usage.

Later to market

Validation takes longer, delaying the launch of new products or processes.

FROM TRIAL AND ERROR TO GUIDED EXPERIMENTATION

More knowledge, fewer iterations

DxTER turns every result into useful information for deciding what to test next.

Fewer experiments

Focus testing on informative or promising combinations.

More value from your data

Build on the knowledge accumulated in each cycle.

Less time to your objective

Reduce effort spent on combinations of little interest.

Tackle complex problems

Work with multiple factors, responses, objectives and constraints.

HOW IT WORKS

What DxTER does with your data

From your experimental results, DxTER builds a model of the process and uses it to decide where it is worth experimenting next.

01

Start from your data

Use the experimental results you already have as the starting point.

02

Model the experimental space

Estimate how factors and responses relate and quantify uncertainty.

03

Prioritise where to test

Identify the most relevant conditions for moving towards your objectives.

Each new result feeds the next step: define, recommend, experiment and learn again.
ITERATIVE LOOP

Every experiment helps you make a better next decision

The process repeats: every new result updates the model and helps refine the next recommendation. Select a stage to explore the loop.

WHAT YOU CAN DO WITH DxTER

Everything you need to guide your experimental process

Analyse, configure, predict and generate recommendations from a single working environment.

Analyse and understand your data

Explore results and relationships between factors and responses.

Optimise for your objectives

Define factors, responses, objectives and constraints and choose the search strategy.

Predict before experimenting

Evaluate expected responses and uncertainty for new combinations.

Get prioritised recommendations

Receive prioritised experimental conditions for the next run.

Use cases

DxTER applied to different R&D challenges

The same optimisation logic applied to very different experimental challenges.

Materials and chemical products

Optimise formulations and compositions against multiple target properties.

Biotechnology and life sciences

Identify promising experimental conditions in processes with multiple variables and constraints.

Engineering and advanced manufacturing

Optimise configurations, materials and process parameters with fewer design iterations.

SIMULATION INTEGRATION

Bring DxTER into your simulation environments

Bring virtual results into the optimisation loop to evaluate more alternatives before moving to physical testing.

✓ Custom integration ✓ Faster exploration ✓ Fewer physical tests
Discuss integration →
Simulation environment Generates virtual results
DxTER Learns and recommends
Promising conditions Prioritises what to validate
TEAMWORK & SECURITY

Work as a team with your data under control

Centralise your team's experimental work in a web environment with controlled access and organisation-level data isolation.

Web access from anywhere

Work from the browser, with no installation or local dependencies.

Collaborate on the same project

Share data, experiments and results with your team.

Manage access

Organise projects and decide who can access the information in each one.

Protect your data and results

Information remains isolated by organisation and the production infrastructure is hosted in Spain.

PLAN AND PRICING

Start with DxTER in a simple, flexible way

Monthly per-user subscription, AI credits included, and extra packs only when you need them.

LAUNCH OFFER −50 %
Standard price 50 € /user/month
25 € /user/month
+ taxes
30,25 € /user/month including 21% VAT in Spain

Included in your subscription

✓

Projects and datasetsManage experiments and data.

✓

Collaborative workspaceShare projects and results.

✓

Analysis and visualisationExplore your data, relationships and results.

✓

Design of Experiments (DoE)Configure factors, responses, objectives and constraints.

✓

80 credits/user/monthFor generating AI recommendations.

✓

Browser accessNo local installation.

Get started with DxTER →

Users and licences

Prices per user per month.

−50 %
Users List price Offer Volume disc.
1–550,00 €25,00 €—
6–1045,00 €22,50 €−10 %
>1040,00 €20,00 €−20 %

The launch offer applies to the list price for each tier.

Additional credit packs

Increase recommendation capacity whenever you need it.

Pack Price Price/credit
100 credits50,00 €0,50 €
200 credits90,00 €0,45 €
500 credits200,00 €0,40 €
1.000 credits350,00 €0,35 €

Packs are not included in the launch offer and are valid for 12 months from purchase.

%

Offer until 31/12/202650% off user subscriptions. Credit packs excluded.

€

Prices exclude taxesApplicable taxes depend on location and tax status.

↻

No commitmentAdjust users according to your team's needs.

AI CREDITS

How AI credits work

Credits are used only when generating recommendations

The cost per recommendation depends on scenario complexity and is shown before execution. All other platform functionality is included in the subscription.

LOW COMPLEXITY
1 credit
per recommendation
  • Up to 6 factors and 2 responses.
  • No constraints.

Shared credit pool

The 80 monthly credits from each active user are shared across the organisation and can be used wherever they are needed.

Support included

We support you with questions and incidents related to using DxTER.

Need something more specific?

We can adapt DxTER to your case with integrations, specific configurations and custom development.

Talk to the team →
FAQ

Answer the key questions before you decide

Data, functionality, billing, security and access: the essentials for assessing whether DxTER fits your organisation.

You can build the dataset directly in DxTER by entering experiments and creating variables manually, or, for convenience, upload your data from Excel or CSV.

Factors Continuous or discrete numerical variables that DxTER can use in the search.
Responses Numerical outputs you want to analyse, predict or optimise.
Filters Categorical information can be used to select or segment experiments, although it is not used as an optimisation factor.
Calculated variables You can create new variables using formulas based on other variables in the dataset.

You can also define units, ranges, physical limits and allowed values so the dataset accurately represents your experimental space.

There is no universal minimum number of experiments. In general, the more high-quality data you have, the better, but quantity is not the only consideration.

Space coverageRepresentative information from different areas of the experimental space is more useful than many points concentrated in a single region.
Quality and consistencyDxTER considers whether the available data is sufficiently consistent to build the model.
Next stepBased on current knowledge, DxTER can guide the strategy towards exploring less-known regions, balancing learning and performance, or exploiting the most promising areas.

The platform helps you understand what type of information the model needs in order to keep progressing.

You can work with one or several objectives and define whether you want to maximise, minimise or approach a target value.

Physical limitsDefine values that are impossible due to the nature of the process or the capabilities of the equipment being used.
Allowed valuesDefine continuous ranges or discrete sets of valid values for each factor.
ConstraintsRepresent additional relationships between factors or conditions that a combination must satisfy to be feasible.

This information tells DxTER where it is allowed to search and prevents it from recommending experimentally invalid conditions.

Yes. DxTER can address multi-objective problems and help you understand trade-offs between competing responses by identifying solutions that offer different balances within the defined constraints.

DxTER uses available knowledge and model uncertainty to help determine which type of search is most useful at each stage.

ExploratoryLearn more about less-known regions of the experimental space.
BalancedCombine learning with the search for better results.
ExploitativeFocus recommendations on regions already showing higher potential.

The user remains in control of the strategy applied.

DxTER checks dataset structure, variable types and missing values before using the data for modelling and recommendations.

Incomplete dataIt may still be useful when exploring the dataset, although the model uses valid observations for the selected factors and responses.
WarningsThe platform highlights issues that may limit the analysis so you can review them before running an optimisation.

Yes. DxTER can integrate with simulation environments to bring virtual results into the optimisation loop and explore more alternatives before moving to physical testing. Integrations with simulators, data sources or proprietary tools are scoped as customised services according to the customer environment.

DxTER is a fully web-based application: computations are performed in the cloud, with no local installation or specific hardware requirements.

Up-to-date browserFor the best experience, we recommend using a recent browser version.
Supported browsersDxTER is optimised for modern versions of Chrome, Firefox, Safari and Edge.

Tell us what you want to optimise

If you want to see DxTER in action, validate its fit with your data or explore an integration, tell us about your use case.

Get started with DxTERcontact@dxter.ai