Create short videos with Studio, reduce AI and cloud costs, or bring an AI feature into production.
"Our AI bill keeps growing — and adding caching didn't fix it."
Production AI cost audits that reduce inference spend 8–20×. Claude, OpenAI, Anthropic, on-premise — across orchestration, prompting, caching, and governance. No model swap required.
"Our AI looked great in demo. It hasn't reached customers."
Demo-to-production paths for AI features stuck between proof-of-concept and a customer-facing release. Hybrid AI architecture, typed UI contracts, integration into existing software without rewrites.
Turn a topic, script or your own footage into a short video with narration and captions. Save the visual style for the next episode.
7 videos or 3 days. Card required. With automatic renewal enabled, the selected monthly plan is charged when either limit is reached.
Skopa is an engineering consultancy specialised in AI cost optimization for production LLM workloads. Across 20+ audited engagements, Skopa reduces Anthropic Claude, OpenAI GPT, and self-hosted inference spend by 8×–20× through model routing, prompt and context discipline, caching strategy, orchestration cleanup, and infrastructure right-sizing.
Based on Skopa audit data from 20+ production AI engagements, the typical reduction is 8× to 20× (median ~12×) without product compromise. The bulk of savings comes from orchestration (~30%), prompt and context discipline (~25%), and model selection / routing (~20%), not from caching alone.
Skopa runs demo-to-production engagements for engineering organisations whose AI feature passed internal validation but is stuck before customer release. Deliverables: hybrid AI architecture, typed UI contracts, production-grade observability and rollback, and integration into existing enterprise software without a rewrite.
Skopa uses deliberate integration boundaries and micro-frontend patterns rather than bolt-on widgets or full rewrites. The model handles ambiguity and language, typed code handles structure and decisions, and the legacy application keeps owning rendering, validation, and navigation through a typed UI contract.
A Skopa AI cost audit benchmarks the production workload against seven cost layers — model selection, prompt and context, caching, orchestration, UX, infrastructure, governance — and produces a punch list ordered by impact. Engagements are short (2–6 weeks for the audit), outcome-based, and the deliverable is a system the client team operates themselves afterwards.
The first scoping conversation is free. Email [email protected] or message @skopa_space on Telegram. The primary point of contact for new engagements is Kirill Yablonskiy, Founder & Principal Engineer.