AI that understands what an experiment is actually testing.
Research moves by ruling explanations out, and most of what decides the next run lives in protocols, rig quirks and the memory of whoever ran it last. Recapi builds the domain understanding AI needs to work inside that, from hypothesis to scale-up.
WHERE RECAPI CAN HELP
Six areas of work where Recapi has already closed the gap.
Each is a research discipline with its own vocabulary and constraints. These are the ones where we built the understanding and watched the work change.
01 · EXPERIMENT DESIGN
Recapi held the explanations, then chose the run that removes one
Past runs, protocols and rig behaviour were taken as one model instead of a folder of results, so every scheduled experiment was picked to separate two standing explanations rather than to add another data point.
02 · PREDICTION & MODELLING
A prediction arrived with the runs behind it
Every forecast carried its evidence and its error, so a miss could be traced rather than argued about.
03 · SIMULATION & OPTIMISATION
The design space was explored before the rig was booked
Parameters were swept where it was cheap and tested where it mattered.
04 · FAILURE ANALYSIS
A failed batch was traced in minutes, not weeks
Causes were ruled in or out against everything the site had recorded and learned.
05 · PROTOCOLS & LAB KNOWLEDGE
The method carried its own caveats
Operator notes and rig-specific corrections travelled with the protocol.
06 · SCALE-UP & TRANSFER
What would not survive the scale-up was named before the run
Moving from bench to pilot to plant breaks assumptions that were never written down - mixing regime, thermal mass, how a supplier substitution behaves at volume. Recapi carried those forward as named unknowns instead of discovering them at the worst possible scale.
HOW WE WORK WITH YOUR BUSINESS
We start with your workflow, not with our product.
We sit with the scientists, process engineers and rig operators who hold the reasoning nobody wrote down. Only then do we decide what intelligence is worth building.
Nothing gets built until we can explain how your work actually happens.
01
Understand how it works
Sit with the programme as it is actually run, not as the plan describes it.
02
Find where intelligence is missing
Name the decisions that rest on knowledge no system holds.
03
Build the understanding
Turn runs, protocols and expertise into structure AI can reason over.
04
Deploy into the workflow
Into the notebook and the planning meeting, not a portal beside them.
05
Keep improving it
Every run teaches it something the last one did not.
WHAT AI CAN ACTUALLY TAKE ON
Four things that only work once it understands the domain.
Choose the next experiment, and defend the choice
Candidates ranked on how much they would settle, not on how novel they look - with the reasoning attached.
Predict a result before the rig is booked
A model that states its tolerance and shows which runs it was built from, so a prediction can be trusted or challenged.
Explain a failed batch against everything the site knows
Candidate causes ruled in or out using run data, supplier records and the conditions only the operators had noticed.
Carry the method and its caveats forward
Protocols that travel with their rig corrections, operator notes and the reason each step is written the way it is.
WHAT WE'VE ALREADY BUILT
This is already running on live programmes.
We went deep into formulation and process research and built intelligence that holds the explanations still standing, predicts a result with its tolerance, and names what will not survive a scale-up - with rig behaviour and operator knowledge treated as part of the model rather than noise around it.
It is not a demo and not a template. It is what happens when Recapi learns a domain properly.
Explanation-first
Runs are chosen to settle a question, not to fill a matrix.
Caveats included
Rig quirks and site conditions are inputs, not footnotes.
PRODUCT GLIMPSES
Three places the understanding shows up.
Screens from the system in production. Data shown is illustrative.
ONE WORKFLOW IS JUST THE BEGINNING
Formulation research is one domain we went deep into. It is not the boundary.
The same approach applies wherever progress depends on ruling explanations out under real constraints. These are the next places it fits.
FORMULATION
Already built
Hypothesis to scale-up, running on live programmes today.
MATERIALS
Properties tied to process
What a material does depends on how it was made, not only what is in it.
PHARMA PROCESS
Transfer without surprises
Scale-up unknowns named while they are still cheap to test.
ENERGY & STORAGE
Cycles that explain themselves
Degradation traced to conditions rather than averaged away.
AGRI-SCIENCE
Trials that account for the field
Site, season and soil treated as part of the experiment, not noise.
HARDWARE R&D
Prototypes that carry their history
Every revision keeps the reason it changed.
What we built here is one example of what becomes possible when Recapi enters a complex domain and learns how the work actually happens.