Vercel AI SDK or vendor SDKs
Chosen for a reason. Streaming wired right. Token counting where it matters.
// ai / llm integration
AI integration without the hype.
AI work for teams who need real product features, not demo videos. Vendor-grade integrations with OpenAI, Anthropic, and the Vercel AI SDK. Streaming, RAG, cost control, evals — built in from day one.
// where ai projects get stuck
Your prototype works. Your prod feature doesn't.
- A demo that wins meetings and a v1 that costs more per user than the subscription pays.
- Streaming UIs that flicker, stutter, or ship empty buffers.
- RAG pipelines that retrieve garbage because nobody scoped the embeddings.
- No evals. No cost dashboard. No idea why the model output drifted.
// what's included
What ships.
RAG pipeline (if scope)
Embeddings strategy, vector DB choice, retrieval evaluation. Documented.
Cost control + observability
Per-user, per-feature cost tracking. Alerting on anomalies. Dashboards your finance team can read.
Evaluation harness
Tests for the model, not just the code. Regression catching when models update.
Streaming UI patterns
Robust to network drop, partial buffers, model timeouts. Real users, real conditions.
// how we work
Three phases. Built to last.
01 · Calibrate
Use-case scoping. Provider choice. Written scope with cost projection.
02 · Build
Streaming UI + backend in parallel. Weekly demo with eval results.
03 · Hand off
Cost dashboard, eval harness, runbook. Support window starts.
// common questions
What teams ask before signing.
OpenAI, Anthropic, or open-weights — chosen by the use-case, not the hype cycle. We've shipped all three.
Vercel AI SDK for most cases. Server-Sent Events or WebSocket where the SDK doesn't fit.
Per-user budgets, prompt caching, model fall-back, server-side rate limits. The patterns that keep you out of bankruptcy.
Yes — a post-launch support window comes included, and optional monthly care plans are available after that.
Murcia, Spain.