AI集成与自动化平台开发

We build a provider-agnostic orchestration layer that monitors API health, latency, and error rates in real time. When a provider degrades or fails, requests automatically route to the next-best model — with prompt adaptation to account for differences in each model's instruction format. Token budgets and cost constraints factor into routing decisions alongside performance. No manual intervention required when OpenAI has a bad morning.

For most deployments, we start with Supabase and pgvector — you get vector search alongside relational queries, row-level security for multi-tenant access, and one fewer infrastructure dependency to manage. Clients processing millions of documents or needing sub-10ms retrieval get dedicated vector stores like Pinecone or Weaviate running alongside the primary database. It's not a one-size-fits-all call; it depends on your query volume and latency requirements.

We use a multi-layer approach: hybrid retrieval combining dense vectors with BM25 keyword matching, cross-encoder re-ranking to improve chunk relevance, strict grounding instructions in system prompts, and a secondary verification pass that cross-references generated claims against source chunks. Every response includes page-level citations back to original documents so your users can verify the output themselves — they shouldn't have to just trust it.

Projects typically range from $50,000 to $300,000 depending on document volume, number of LLM workflows, and integration complexity. A standard engagement runs 12-16 weeks from discovery through production deployment. You'll have a working MVP at week 8 so you can validate the approach with real users before we harden it for full production. No big-reveal at the end.

Yes. Our document processing pipelines are event-driven with webhook-based integrations. We've built connectors for CRMs, ERPs, document management systems, and custom internal tools. The orchestration layer triggers downstream actions — CRM record updates, approval workflows, Slack notifications — based on AI processing results, all with audit logging for compliance. If it has an API, we can wire it in.

We implement row-level security in Supabase so document access in RAG queries respects your existing permission model. All data stays within your cloud infrastructure — we deploy on your AWS, GCP, or Azure accounts, not ours. For regulated industries, we add PII detection and redaction before documents enter the LLM pipeline, and all API calls run under enterprise-tier provider agreements with data processing addendums.