LAISER launches laiserlabs.com
The Laboratory for Artificial Intelligence Systems and Engineering Research now has a public home, built in the open on GitLab and deployed straight from its pipeline.
Non-profit research laboratory
Intelligent systems, engineered to be understood.
LAISER is the Laboratory for Artificial Intelligence Systems and Engineering Research. We study how AI systems are built, explained, evaluated, and run, from foundation models and multi-agent architectures to the data and infrastructure beneath them.
- Focus
- AI systems, explainability, agents, data science
- Founded
- 2026
Mission
Research you can audit.
Most AI failures are quiet: a leaked test set, a metric that stopped measuring, a confident answer with no uncertainty. We exist to build systems, and a research practice, where those failures are caught by design.
- Evidence over narrative
- A result is a claim with its evidence attached, or it is not a result.
- Baselines first
- The simplest model that must be beaten is built, tested, and reported before anything clever.
- Systems that explain themselves
- Explainability, lineage, and telemetry are requirements, not reports written afterwards.
- Failed hypotheses are evidence
- We record what did not work with the same care as what did.
Research
What we study
Eight areas, one discipline: evidence first, systems that can be explained and tested.
01
Foundation models and language systems
How large pre-trained models are adapted, composed, and evaluated as components of larger systems, and what they can and cannot be trusted to do.
Explore 02Explainability and interpretability
Methods that make model behaviour legible to the people who depend on it, from feature attribution to mechanistic analysis, and the evidence that an explanation is faithful.
Explore 03Multi-agent and agentic systems
Architectures in which several model-driven agents plan, delegate, negotiate, and act, and the coordination, safety, and cost questions that come with them.
Explore 04Safety, alignment, and evaluation
Making sure systems do what their operators intend, fail in ways that are detectable, and are measured by benchmarks that cannot be gamed by accident.
Explore 05Data science and applied machine learning
The methodology of turning fragmented data into tested predictions, with baselines first, uncertainty shown, and every result reproducible from raw sources.
Explore 06Big data, analytics, and ML systems engineering
The infrastructure beneath the models, from data lakes and warehouses to serving, CI/CD, and observability, built so that research results can become dependable systems.
Explore 07Spatial and geospatial intelligence
Learning from satellites, land cover, census, infrastructure, and local records on a common spatial index, to see where places are changing before the change is obvious.
Explore 08Forecasting and decision intelligence
Models that forecast change over years, not seconds, and the decision frameworks that turn a probabilistic forecast into an action with a known risk.
ExploreProjects
Work in progress
Active programmes where the research meets real data and real infrastructure.
active since 2026
Land Alpha
An early-stage research programme testing whether fragmented public and local data can identify land-market transitions before those changes are fully reflected in market signals.
- Spatial and geospatial intelligence
- Forecasting and decision intelligence
- Data science and applied machine learning
active since 2026
LandInvest on Google Cloud
The data platform and Market Explorer application behind LAISER's land-investment research, built as a data lake, BigQuery layer, serving export, and Cloud Run web app with pipeline-only deploys.
- Big data, analytics, and ML systems engineering
- Spatial and geospatial intelligence
- Forecasting and decision intelligence
active since 2026
Principal Data Scientist Framework
A data-first operating system for ML and analytics work with AI coding agents, with evidence labels, leakage and point-in-time discipline, baseline-first modelling, adversarial review, and self-testing regression evals.
- Data science and applied machine learning
- Safety, alignment, and evaluation
- Multi-agent and agentic systems
News
Latest from the lab
Work with us
We collaborate with researchers, engineers, institutions, and domain experts who want rigorous answers more than impressive demos. Tell us what you are working on.