We design and deploy production-grade AI systems for broadcasters, media platforms, and the funds that back them — engineered on Qualcomm silicon, measured in milliseconds, accountable in audits.
Most AI vendors sell a model. We deliver the system around it — silicon-aware inference, content pipelines, observability, and the governance media operators actually have to defend in front of regulators and boards.
We deploy on Cloud AI 100 and Snapdragon NPUs — lower watts, lower latency, lower bill of materials than the GPU default.
Real-time transcription, scene segmentation, rights-aware tagging, embedding indices — composed from interchangeable model blocks.
Per-token traces, drift detection, evals tied to business outcomes — not vanity dashboards. Wired into your existing SIEM.
Provenance-aware ingestion, C2PA signing, audit trails, and policy guards built for jurisdictions that take broadcast law seriously.
Domain adaptation on your archives. We don't ship someone else's weights as a service — we tune to your editorial voice and your taxonomy.
For funds: AI maturity audits, technical due diligence, and post-investment platform reviews of media-tech portfolio companies.
We replaced a $4.1M/yr GPU pipeline with a Cloud AI 100 cluster running our own segmentation + ASR stack. Editorial search went from "ask the librarian" to sub-second across 280,000 hours of footage. On-prem, air-gapped, compliant with national broadcast law.
A live ingest layer that classifies, embeds, and de-duplicates every incoming story before it hits the rundown. Built on Snapdragon-class accelerators in regional edge nodes; one operator, six datacenters, zero GPU procurement.
A standing engagement: we audit prospective media-tech investments and review portfolio companies twice a year — silicon strategy, model dependencies, defensibility, regulatory exposure. The reports go straight into IC.
"They walked in expecting to sell us a chatbot. They walked out with a contract to rebuild our ingest layer. A year on, it's the only thing in the building I don't worry about."
"The Qualcomm partnership isn't a logo on a slide — it's the reason their cost-per-inference numbers actually hold up in our diligence model."
"Most AI vendors hand us a demo. atlaint handed us an SLA, a threat model, and the on-call rotation. That's the difference."
A two-year teardown of cost, latency, and procurement risk across three Qualcomm-class deployments.
A working translation of Articles 50–52 for engineering teams running production content pipelines.
Eleven model, silicon, and rights-related questions every media-tech IC should ask in 2026.
Tell us what you're trying to ship — or what you're being asked to underwrite. We'll come back inside 48 hours with whether we're the right team, and who we'd point you to if we're not.