GenAI Development

Generative AI only earns its keep when it ships as software your team can trust in production. RankBit Tech builds exactly that. We move past demos and notebooks to deliver grounded, observable, cost-aware systems: services that answer questions over your own data, automate document-heavy workflows, and embed intelligent search where it actually changes how people work.

Every engagement starts with a hard look at the problem, the data, and the failure modes. We design retrieval, prompting, and evaluation around your real corpus, then wire in guardrails, tracing, and fallbacks so the system behaves predictably under load. You get working software with a clear path to maintain, measure, and improve it as models and requirements evolve.

From Model Capabilities to Production Software

Our core work centers on RAG pipelines built with LangChain and LlamaIndex over vector stores like pgvector, Pinecone, and Qdrant, with chunking, hybrid search, and reranking tuned to your content. We pair these with structured outputs and tool-calling so model responses map cleanly onto your APIs, schemas, and downstream actions instead of free-form text.

Beyond retrieval, we engineer agentic workflows and document pipelines that parse, extract, and route information across PDFs, contracts, and tickets. We instrument everything with evaluation and observability (golden datasets, regression checks, and tracing through LangSmith) and apply fine-tuning, prompt caching, and model routing to keep latency and cost in check at scale.