LLM-powered applications with cost control.
Pain points we solve
LLM costs spiral—per-request expenses unpredictable and hard to optimize
Prompt iteration cycles—trial-and-error consuming engineering time
Latency variability—streaming inconsistent across edge regions
Hallucinations and out-of-distribution responses creating support tickets
How we build
We architect AI apps with streaming responses from OpenAI or Anthropic, prompt versioning and A/B testing, real-time token and cost tracking per user and feature, RAG pipelines for grounding, and safety classifiers. Your cost-per-completion stays under your target.
Example stack
Questions
Caching, prompt compression, and model selection. We use Claude 3.5 Haiku for simple tasks (90% cheaper), Sonnet for medium complexity, and Opus only when necessary. Per-user cost budgets with hard cutoffs prevent bill shock.
Yes. Supabase pgvector for embeddings, Upstash Redis for semantic cache hits, and chunked document ingestion. Typical RAG reduces hallucination by 70% compared to zero-shot prompting.
Fully. We use Server-Sent Events for real-time token streaming, progressive rendering on the client, and graceful fallback if the stream breaks mid-response.
We manage the pipeline: dataset curation, train-test splits, and controlled fine-tuning. But for most use cases, prompt engineering with RAG outperforms fine-tuning at 1/10th the cost.
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