We design and install RAG knowledge systems, MCP integrations, AI agents and local AI servers for science-led companies and any business ready to automate, with the cybersecurity and backups to run them safely.
Six services that fit together. Most clients start with one, usually a private knowledge assistant or a backup review, and add the rest as confidence grows.
Capable language models running on hardware you own, on your premises or in a UK data centre you control. Your documents, prompts and outputs never train someone else's model.
Ask questions of your own reports, procedures, lab notebooks and correspondence, and get answers that cite the document they came from.
The Model Context Protocol connects AI assistants to the systems you already use, so they can read and act with exactly the permissions you grant.
Agents that carry out multi-step work: compile a report, triage an inbox, reconcile records, watch a process and flag what needs a person.
AI tools add new ways in. We secure them together with the rest of your systems, and write the rules your staff will actually follow.
Encrypted, tested, off-site backups, so a failed disk or a ransomware attack is an inconvenience rather than a crisis.
Public AI tools are useful, but every prompt and uploaded file leaves your control. For companies whose value sits in their know-how, a private system is the safer way to get the same benefits.
Formulations, process data, client files and unpublished results are processed on machines you control. Nothing is sent to a third party unless you decide it should be.
Hardware you own replaces usage bills that grow every time another colleague starts relying on AI.
A hybrid setup lets sensitive work run locally while routine tasks use cloud models, under rules you set and can audit.
Internal R&D programme
SkAInet (Smart Knowledge & Artificial Intelligence Network) is our in-house research programme. We are building a machine learning system that detects the slow drift that comes before an unplanned shutdown in a chemical plant, with the aim of giving operators months of warning instead of minutes.
Specialised models learn each plant's normal behaviour from the data it already records: temperatures, pressures, flows, motor currents, laboratory results and maintenance logs. Each model watches for one kind of failure.
Engineers see why an alert was raised. Clear visual comparisons show the current operating state against normal operation and past failures, and a local language model explains the result in engineering terms.
SkAInet runs on infrastructure inside the plant. Process data, a plant's most sensitive asset, never has to leave the site.
Status: in development. We are targeting chemical and food processing plants in Argentina, the UK and the EU, with the United States on the horizon. We welcome conversations with pilot partners and investors.
Talk to us about SkAInetWe come from chemical engineering and industrial R&D, so we understand confidential data, regulated processes and the cost of getting things wrong. The same care applies to any business that wants AI to take on real work.
A clear sequence from first conversation to a system your team relies on. You can stop after any step and keep what has been delivered.
We review how your team works, where your data lives and which tasks cost the most time. You get a short written report with priorities and rough costs.
We choose the models, hardware, integrations and security controls that fit your budget and risk, and agree what success looks like.
We install the system, connect it to your tools and test it on real work with the people who will use it.
Access control, monitoring, encrypted backups and recovery procedures go in before anything goes live.
Your team learns to use it well. We stay available for updates, new workflows and the questions that come with growth.
Pro Scientia is a UK company that began as a scientific consultancy in process development, modelling and industrial R&D. It now applies the same discipline to AI: understand the system first, measure what matters, and build things that keep working after the demo.
Founder and director
Chemical engineer with a PhD in Chemical Engineering and more than 16 years of experience across academia and industry, including Dow, Honeywell and several start-ups. He combines a background in electrochemistry, process engineering and industrial R&D with hands-on work in data science and AI.
Associate, chemical engineer
Chemical engineer with experience in process engineering and pharmaceutical process development, combining industrial engineering know-how with the quality and traceability demands of regulated industries. Graduate of Universidad Nacional del Sur and recipient of the Ing. Isidoro Marín Award from Argentina's National Academy of Engineering.
Start with a short call. We will tell you honestly whether AI can help, what it would take, and whether it should run locally, in the cloud or both.