Quiksbot

The problem

Businesses wanted a chatbot that could answer questions about their products, bookings, and policies — not a generic model guessing. Off-the-shelf widgets hallucinated. Building it properly meant a full retrieval pipeline, and most teams had nobody who could own it end to end.

A production multi-tenant SaaS platform, architected and shipped alone. Document ingestion, embedding generation, and vector search ground every answer in the tenant’s own knowledge base. A multi-LLM routing layer sends each request to the model that best balances cost against quality, rather than paying frontier prices for trivial queries. Hallucination guardrails and prompt tuning are treated as engineering disciplines, not afterthoughts — and an evaluation harness runs in CI, checking response quality, latency, and regressions so model drift is caught before users see it. Around the pipeline sits the whole system: microservices, event handling, billing, CDN, and the customer-facing interface.