Years of building real AI products have left us with proven infrastructure — retrieval engines, generative backbones, serving layers, training pipelines. We adapt them to your data and your edges. We don't start from scratch. We start from here.
Make a decade of internal documents searchable in plain English — contracts, reports, policies, research.
RAG-based retrieval with LLM-powered relevance and synthesis. Tuned for the precision bar of regulated industries.
Applicable across legal, finance, and healthcare.
Add natural-language search to any profile-based product — talent, creators, properties, listings.
Three layers: multimodal indexing, query understanding, and intelligent retrieval. LLM-planned thematic results with relevance gating.
Deployed across talent, creator, and property marketplaces at scale.
A multi-modal, single-stage generative backbone adaptable to any sequential domain.
A configurable LLaMA-based backbone with parallel multi-stream output, natural-language conditioning, and segment-level structural awareness.
Validated on multi-stem music generation at 3B scale.
Serve production AI at scale without overpaying for idle GPUs.
GPU-aware autoscaling with cold-start mitigation, spot-instance integration, and multi-model routing behind a standardized API gateway.
In production serving real-time inference for consumer-facing AI products.
A production-grade AI training and data pipeline — already built, already battle-tested.
Airflow-based orchestration on AWS ECS/EC2 with native inference steps, auto start/stop, and spot-instance support for cost efficiency.
Validated across music, ad-tech, healthcare, and recruiting workloads.
Evaluation harnesses, agentic scaffolds, observability layers, fine-tuning toolchains, and client-specific frameworks we can't show publicly. If you're trying to solve a problem and wondering whether we've already built something relevant — ask. The honest answer is often yes.