We go deep in each of these six areas rather than offering broad AI consulting. Every specialization below is grounded in production deployments, not a slide deck.
Multi-hop reasoning, semantic reranking, and dynamic knowledge-graph integration. We build RAG systems that don't hallucinate — not because we bolted on a fact-checker, but because the retrieval architecture and reranking layer are designed to make ungrounded answers structurally unlikely. Used for internal knowledge search, regulatory-document Q&A, and customer-facing support automation.
Ethical AI & Alignment, for engagements where every answer needs a traceable source.
See a representative engagement →We design custom model architectures optimized for a specific hardware target, latency budget, and deployment environment — rather than adapting an off-the-shelf model and hoping it fits. This matters most where the deployment target is a constrained edge device, not a GPU cluster.
Computer Vision & Perception, when the target hardware is a field camera or industrial sensor.
See a representative engagement →Multi-agent systems that collaborate to solve non-linear problems through decentralized decision-making. Built for environments where a single model cannot reason over the full problem space — coordinating specialized agents instead of forcing one model to do everything.
This research thread underlies Soffit OS™, our agentic orchestration layer for the systems you already run.
Precision object detection, segmentation, and temporal analysis for industrial and security environments. Systems built to understand what they see — tracking, context, and event significance — not just return a bounding box and a class label.
This research thread underlies Boondi™, our space-intelligence platform for physical environments.
See a representative engagement →Time-series forecasting and anomaly detection using transformer architectures tuned for high-stakes operations — demand forecasting, transaction-pattern analysis, and equipment failure prediction, where the cost of a wrong prediction is measured in real losses.
This research thread underlies Syftics, our financial analytics product.
See a representative engagement →Guardrails, bias auditing, and interpretability layers built into the architecture from the start, not added before a compliance review. No system leaves our lab as a black box — every deployment ships with documentation of what it does, what it doesn't, and where a human needs to stay in the loop.
Every engagement, across all five verticals — not a standalone service.
Half of this list only exists because someone on our team stood on an actual factory floor first and asked what was actually going wrong.