Frame the question
What decision or workflow are we improving, and how will we know it improved?
Research-led AI company
We run the experiments, so your automation actually works.
A research-led AI company. We test, compare and validate before we build, then turn what works into automation for logistics, fintech, real estate, education, climate and media.
Born in clinical AI research, where mistakes are costly
Experiment-first: we compare many approaches and keep the best
Built for real-world conditions: messy data, low connectivity, diverse users
Research first, automation second
Test before you build
Most AI projects fail because the first approach was never tested against the alternatives. We start where research starts: with a question, a hypothesis and a fair comparison.
Test many approaches
We run structured experiments across models, data setups and architectures instead of trusting the first one that works.
Keep what the evidence supports
We choose the approach that performs best on your real data, including the hard cases.
Automate it
Only then do we build the production system around the winner.
Research in action
Where our research comes from
Our roots are in healthcare AI, where we study how models behave across different conditions, patient groups and image qualities. These are the kinds of questions we work on, described without client or disease names.
Comparing approaches condition by condition
For each clinical condition we study, we run many experiments to find which model and training setup performs best, rather than assuming one recipe fits all.
Fairness across populations
We test how models perform across skin tones and demographics, with a focus on groups that global datasets under-represent.
Working with expert-labelled data
We build careful annotation and validation pipelines with clinicians, and split data so results hold up on unseen patients.
Built for the field, not just the lab
We design for low-bandwidth, offline-first and privacy-preserving deployment, so systems work where people actually are.
Why this matters for your business: the same discipline of testing, comparing and validating is what makes automation reliable in logistics, finance, property, learning, energy and media.
AI automation for your industry
The same rigor, applied to the way your business runs.
Logistics
Route and load planning, shipment tracking, document processing, demand forecasting
Fewer delays, leaner operationsFintech
KYC and onboarding, fraud signals, risk scoring, reconciliation, compliance reporting
Faster decisions, audit-ready recordsReal Estate & PropTech
Tenant screening, maintenance triage, smart building operations, ConTech progress tracking
Smarter buildings, faster dealsEdTech & Learning
Adaptive tutoring, grading, content creation, institutional workflows
Learning that adapts to every learnerClimate Tech & Energy
Grid forecasting, asset monitoring, carbon accounting, ESG reporting
Intelligence for a lower-carbon futureMedia & Entertainment
Recommendations, generative content, programmatic ads, moderation
Create more, personalize deeperHealthcare
Clinical AI research, screening tools, offline-first deployment
Where our research beganExplore all domains
Nine industries, each with its own workflows, constraints and solutions.
Explore all domainsHow we work
From first question to working system
Discover
We map your workflows, data and goals, and agree on what success looks like.
Experiment
We test several approaches on your real data and compare them fairly.
Build
We turn the winning approach into a working system that fits your existing tools.
Deploy
We integrate, train your team and run it in real conditions.
Improve
We monitor results and keep refining as your data and needs change.
What we stand for
Principles we build by
Evidence over hype
We tell you what works, what doesn't and how we know.
Fair by design
We test for bias across the people your system will serve, not just the average user.
Built for real conditions
Offline-first and low-bandwidth options where connectivity is unreliable.
Privacy-conscious
Data stays under your control, with architectures such as federated learning where sensitive data is involved.
Measurable outcomes
Every project starts with a success metric we can track together.
Case studies
How we approach real-world AI projects
Illustrative, technically detailed examples of the challenge, architecture and validation behind each type of solution.
LogisticsTech & Supply Chain
Predictive Shipment Risk and Intelligent Dispatch
A regional logistics operator coordinates shipments across hubs, carriers, and customer locations. Dispatchers rely on spreadsheets, transport-management records, GPS feeds, and phone calls. Delays are often recognized only after a delivery misses its promised window, while re-planning consumes staff time.
Read case study FinTechIntelligent Document Processing and Reconciliation
A financial services team manually extracts information from onboarding documents, statements, invoices, and transaction records. Staff compare values across systems and investigate mismatches. Poor scans and varied layouts increase workload, while errors can affect customers or compliance.
Read case study HealthcareTech & Life SciencesClinical Documentation and Knowledge Assistant
A clinical team faces documentation burden and fragmented information across notes, forms, guidelines, and protocols. Clinicians want faster structured note drafting and easier access to approved information, but medical content must remain accurate, attributable, privacy-conscious, and reviewed.
Read case study
Let's talk
Have a problem worth testing?
Tell us about the workflow, decision or research question. We'll tell you honestly whether AI can help and how we'd find out.
Research-led AI, from clinic to company.