We are building the
data layer future AI
will run on.

Every organisation runs on undocumented process knowledge. We capture it, structure it, and turn it into something AI can actually use. That requires hard engineering at the intersection of computer vision, language models, and knowledge representation.

Data pipelines at scale

Human-in-the-loop systems

AI-native infrastructure

WHAT WE ACTUALLY BUILD

From unstructured input to

AI-ready knowledge.

Most teams underestimate the data layer. We don't. Messy, real-world desktop activity, clicks, inputs, context switches runs through our pipeline and comes out as clean, structured, queryable process records.

FOCUS AREAS

Data processing at scale

High-throughput screen activity pipelines. Frame extraction, event detection, deduplication, sequencing.

Human-in-the-loop systems

Labeling infrastructure, quality review workflows, active learning loops for edge cases the model hasn't seen.

Data quality & evaluation

Ground truth construction, annotation consistency, eval harnesses, drift detection in production.

TECHNICAL STACK

Computer vision pipeline

Frame-level action detection, UI element classification, temporal sequence modelling from raw screen data.

LLM reasoning layer

Structured extraction, intent inference, workflow segmentation, converting events into process graphs.