Research01
Go deeper.
Get there faster.
Explore more scientific evidence, compare alternatives and identify promising directions with less manual effort.
Broader evidence. Better-informed choices.
Faster, more extensive research
Computational Decision Intelligence
for the process industry
Research deeper.Experiment smarter.Produce better.
SIA brings science, AI/ML, your data and physical constraints together. Sophisticated analysis. Clear recommendations. Simple to use.
Start with your existing data. Integration is optional.
The depth behind the simplicity
ScienceEvidence & principles
AI / MLModels & methods
Your dataResearch records
Physical constraintsReal-world boundaries
SIADecision intelligence
Explore your stage
Faster, more extensive research
Explore the research exampleBUILT FOR THE
PROCESS INDUSTRY
Specialty chemicalsPharmaIndustrial biotechnologyCROs & CDMOs
01 / YOUR OUTCOMES
Your stage.
Your next advantage.
One approach, applied to the decision that matters to you. Start at any stage.
Go deeper.
R&D & experimentation02
Make every
experiment count.
Use existing results and scientific constraints to prioritise what to test and learn more from each experiment.
More informed tests. Less wasted effort.
Lower experimentation costs
Scale & manufacturing03
Improve output.
Build consistency.
Evaluate operating choices within your process limits to improve throughput, yield and quality.
Better settings, within real constraints.
Higher, more consistent performance
02 / MEET SIA
Simple for you.
Sophisticated
underneath.
Bring the problem. SIA handles the computational complexity and makes the next step clear.
- 1
Tell SIA what matters.
Describe your objective and the limits that must be respected.
- 2
Bring what you already know.
Add your research, experimental results or operating data.
- 3
Explore a clear recommendation.
See the options, understand the reasoning and ask follow-up questions.
You bring the domain expertise. SIA makes advanced analysis accessible.
SIAExample inputs & outputs
Your objective
“Which approaches should we investigate for this target chemical?”
Your context: target, research records and selection criteria.
SIA works through the complexity
Find relevant evidence · Compare approaches · Identify gaps
What you get
A clearer view of the options worth investigating.
- Routes, reported conditions and outcomes
- Sources, trade-offs and evidence gaps
- Questions for your team's scientific evaluation
Ask a follow-up: “What evidence supports this approach?”
Your objective
“Which conditions should we test next to reduce development effort?”
Your context: past experiments, desired outcomes and permitted conditions.
SIA works through the complexity
Learn from results · Evaluate candidates · Prioritise tests
What you get
A focused set of experiments to consider next.
- Candidate conditions within your defined limits
- Expected learning and performance trade-offs
- Assumptions for your team's review
Ask a follow-up: “What would this experiment help us learn?”
Your objective
“Which operating changes could improve yield without compromising quality?”
Your context: batch history, quality results and operating limits.
SIA works through the complexity
Model interactions · Evaluate alternatives · Check constraints
What you get
Candidate operating conditions, with the reasoning.
- Suggested changes for a controlled process trial
- Supporting data and quality trade-offs
- What to validate against your baseline
Ask a follow-up: “Which past batches support this recommendation?”
Illustrative experience. Outputs depend on the application and available evidence; your specialists review and validate proposed changes.
The intelligence underneath
From your objective.
To your next decision.
Our product direction
We are building toward a connected decision loop for the process industry. SIA coordinates the analysis. Your experts guide and validate the decisions.
- Your team
Set the goal, share your evidence and specify the constraints.
- SIA
Identify relevant scientific methods, AI/ML models and computational tools.
- Models & tools
Use your data and scientific principles to assess options against process constraints.
- SIA
Compare findings, adjust the analysis and evaluate again where needed.
- Your team
Review recommendations, evidence, trade-offs and what needs a real-world test.
01 / Your team
Define the objective
Set the goal, share your evidence and specify the constraints.
New experimental or operating evidence informs the next iteration.
03 / OUR APPROACH
Grounded in science.
Built around your reality.
One common approach brings scientific depth and practical context to your decision. The methods follow the problem.
ONE APPROACHFOUR INGREDIENTS
01 / 04
Relevant evidence, mechanisms and process principles.
Conceptual view · Select an ingredient to explore
Relevant evidence, mechanisms and process principles.
Learn relationships, compare alternatives and evaluate possibilities.
Research, experiments and operating history from your context.
Equipment capabilities, quality requirements and permissible conditions.
Clear recommendations, with the reasoning.
Your experts decide what to investigate, test or change.
04 / IN PRACTICE
Real teams.
Meaningful objectives.
Already working with process-industry teams at different stages, applying the same underlying approach.
Research
A CDMO company
Active engagementMaking research more extensive and faster.
Expanding the scientific options and evidence the team can evaluate.
R&D & experimentation
A specialty chemicals team
Active engagementWorking to reduce the cost of experimentation.
Helping prioritise what to test and make better use of experimental effort.
Manufacturing
A large pharmaceutical company
Active engagementWorking to improve throughput and yield.
Supporting better-informed operating decisions within process constraints.
Current engagements, shown by sector and objective.
05 / GETTING STARTED
One objective.
Your existing data.
Start where you are. We assess the evidence, agree a focused scope and define how to measure value.
01 / BRING YOUR DATA
Use what you already have.
Research records, trial results or process exports. Integrate when it adds value.
EXCELCSVPDFRESEARCH RECORDS
02 / DEFINE SUCCESS
Make the first step specific.
Agree the decision, constraints and evaluation criteria with your team.
03 / FIT YOUR ENVIRONMENT
Your data. Your control.
Choose a deployment arrangement suited to your scope and IT requirements.
SAASON-PREMISESHYBRID
Role-based accessSeparated customer environmentsNo shared-model training on customer data
A few practical
questions.
Can we start at just one stage?
Yes. Research, experimentation and manufacturing are independent starting points. We scope the work around the decision you want to improve and the evidence available.
Do we need integrations to get started?
No. Start with relevant files and exports. We can consider integrations when they improve access to useful data.
How are recommendations validated?
Your specialists review the supporting evidence, assumptions and constraints. Proposed experimental or process changes are evaluated through appropriate trials against an agreed baseline before adoption.
Does SIA control our equipment?
Your team reviews, approves and implements changes. SIA supports decisions; the core offering does not provide real-time monitoring or autonomous equipment control.
Bring us a decision
worth improving.
Tell us your objective, the data you have and what success would look like. We'll identify a practical starting point.