AI Agents & RAG systems that actually work in production
Retrieval-augmented generation and autonomous agents are the backbone of modern AI products. Here is how Glixen Technologies designs, builds, and ships them so they stay accurate, fast, and reliable long after launch.
RAG pipelines built for accuracy
A RAG system is only as good as its retrieval. Before we write a single prompt, we study how your documents are structured: contracts read differently from support tickets, and product manuals differ from medical records. We then design the ingestion pipeline around that reality, choosing chunk sizes, embedding models, and index strategies per domain. On a recent legal-tech project, switching from fixed 512-token chunks to clause-level chunking with a reranking step raised answer accuracy from 71% to 94% on the client's own evaluation set. We build that evaluation set with you first, typically 150 to 300 real questions with verified answers, so quality is measured, never assumed.
Agents with guardrails
Autonomous agents are powerful and dangerous in equal measure. Every agent we ship runs inside strict boundaries: scoped tools it is allowed to call, permission checks before any write action, retry budgets so it cannot loop forever, and a full audit log of every decision it makes. For a logistics client, our dispatch agent processes around 2,000 booking requests a week. It handles the routine 85% end to end and escalates the rest to a human queue with full context attached, so the team only touches the cases that genuinely need judgment.
Measured in production, not in demos
Anyone can make a demo look smart. The hard part is week 40, when the knowledge base has grown, the model provider has shipped three updates, and usage looks nothing like the launch plan. Every system we deliver includes dashboards for latency, token cost per request, retrieval hit rate, and answer quality sampled against the evaluation set. When something drifts, an alert fires before your users notice. That discipline is why our AI deployments keep scoring in the mid-90s months after launch.
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