Anyone can prompt an API. We build systems where model choice, quantization, caching, and GPU shape are engineered together — so accuracy goes up while $/1M tokens collapses 10×.
Hybrid dense+BM25, reranking, citations, permission filtering. Backed by NVMe vector stores and cached inference — 68-92% deflection, <4% hallucination, $0.11/1K queries.
Tool-using agents with approval gates, replay logs, and cost budgets per run. Runs on autoscaled L40S pools, not $50 API surprises.
GPT-4 → private 8B via SFT + DPO on H100s. You keep the weights. Inference drops from $12 to $0.80 / 1M tokens.
Streaming STT/TTS + visual QA on persistent GPU workers. 800ms E2E, 31k hrs/mo per rack.
We benchmark your prompts on 4090 → L40S → H100, pick cheapest that hits quality.
Quantize, cache, distill. vLLM tuning + eval regression gates.
Side-by-side with humans or APIs. Win-rate proven before cutover.
Move to reserved GPUs. $/token falls monthly as cache warms.
| Pilot | Infra included | Price |
|---|---|---|
| RAG Copilot | L40S + vector DB, 30 days | $12k–$22k |
| Voice QA | A100 streaming cluster | $18k–$32k |
| Agent + Tools | Autoscaled pool + evals | $28k–$55k |
| Distill + Serve | H100 train + L40S serve | $25k–$70k |