AI Integration & Automation Platform Development

We build a provider-agnostic orchestration layer that's watching API health, latency, and error rates in real time. When a provider degrades or starts returning 529s, requests automatically reroute to the next-best available model -- with prompt adaptation to handle the differences in how Claude versus GPT-4o versus Gemini expects instructions to be formatted. Token budgets and cost constraints factor into those routing decisions too, not just raw performance. And honestly? No manual intervention required when OpenAI has a bad Tuesday morning. Your users don't notice. Your on-call engineer doesn't get paged at 2am. That alone is worth a lot.

For most deployments, we start with Supabase and pgvector -- you get vector search running right alongside your relational queries, row-level security for multi-tenant access, and one fewer infrastructure dependency to explain to your DevOps team. But clients processing millions of documents or needing sub-10ms retrieval are a different conversation. Those get dedicated vector stores -- Pinecone or Weaviate -- running alongside the primary database. It's not a one-size-fits-all call. It depends on your actual query volume and latency requirements, not what sounds impressive in a pitch deck.

We use a multi-layer approach because no single technique gets you there alone. Hybrid retrieval combines dense vectors with BM25 keyword matching. Cross-encoder re-ranking improves chunk relevance before anything hits the LLM. System prompts include strict grounding instructions. Then a secondary verification pass cross-references generated claims against source chunks after the fact. Every response includes page-level citations back to original documents -- because your users shouldn't have to just trust the output. They should be able to verify it in 30 seconds.

Projects typically run $50,000 to $300,000 depending on document volume, number of LLM workflows, and how many systems we're integrating with. A standard engagement is 12-16 weeks from discovery through production deployment. But you'll have a working MVP at week 8 -- real users, real documents, real workflows -- so you can validate the approach before we harden everything for full production scale. No big reveal at the end where everyone holds their breath and hopes it works.

Yes. The document processing pipelines are event-driven, and we use webhook-based integrations to connect downstream systems. We've built connectors for Salesforce, HubSpot, SAP, SharePoint, and plenty of custom internal tools -- if it has an API, we can wire it in. The orchestration layer triggers actions based on AI processing results: CRM record updates, approval workflows, Slack notifications, whatever the process requires. All of it with audit logging, because in regulated industries that's not optional -- that's the whole ballgame.

Row-level security in Supabase means document access in RAG queries respects your existing permission model -- someone in the London office doesn't pull documents they shouldn't see just because they phrased a question cleverly. All data stays within your cloud infrastructure. We deploy on your AWS, GCP, or Azure accounts, not ours. For regulated industries -- healthcare, finance, legal -- we add PII detection and redaction before documents ever reach the LLM pipeline. And all API calls run under enterprise-tier provider agreements with data processing addendums already in place.