Slab Labs Research
Years of research, proprietary data, and deep domain expertise — combined into a system that understands construction the way an experienced builder does. Our models aren't wrappers around generic AI. They're purpose-built from the ground up.
Your data stays yours
Strata runs on dedicated infrastructure. Your project details, estimates, and business data are isolated from any external training pipelines and are never shared with third parties.
Our models run in a controlled environment where customer data stays within our system boundary. We chose this architecture specifically because your trust matters more than convenience.
Research Area 01
Our core prediction engine fuses outputs from multiple specialized models — each trained on different slices of our proprietary dataset. The ensemble architecture was developed over three years of iteration, and the feature engineering alone required deep domain expertise that took our team years in the field to acquire.
140+ engineered features. Monthly retraining on labeled outcome data. The model architecture and training methodology are proprietary.
Research Area 02
The hardest part of building construction intelligence isn't the model — it's the data. We spent years building proprietary data acquisition pipelines, annotation frameworks, and quality-control systems that produce the labeled training data our models require. This dataset is our primary competitive advantage and is not available commercially.
Multi-stage annotation with inter-annotator agreement scoring. Strict quality controls ensure label consistency across the corpus.
Research Area 03
Generic AI lacks the structured domain knowledge needed for construction. Our team — with direct experience building and operating enterprise-grade ontologies at scale — designed a proprietary knowledge representation layer that encodes thousands of construction concepts and their interdependencies. This isn't a taxonomy or a lookup table. It's a reasoning substrate that general-purpose systems cannot replicate without equivalent domain investment and infrastructure expertise.
The ontology is maintained by domain experts with decades of field experience. Its design reflects hard-won lessons from enterprise knowledge platforms, not academic prototypes.
Research Area 04
Our CV pipeline extracts structured information from architectural drawings, site imagery, and project documentation. Custom detection models were trained on a proprietary labeled dataset that took over two years to assemble and annotate — a prerequisite that makes this capability difficult to replicate.
Multi-scale feature extraction handles documents at varying resolutions and formats. Transfer learning from our labeled corpus drives accuracy on novel inputs.
The best way to understand Strata is to use it. Describe your project and watch it work.