Updated January 8, 2026
AI Development
We build end-to-end AI products—data pipelines, model training, RAG, evaluation, and deployment—optimized for reliability, latency, and cost.
Modern AI is not “a model”—it is a system. The teams that win in AI treat data quality, evaluation, and operational safety as first-class engineering concerns. At QORDIXY, we build AI features that perform under real user traffic, not just in a demo notebook.
What we build
- RAG assistants over internal documents with citations and access controls
- Domain-specific copilots for support, sales, ops, and engineering workflows
- Prediction and ranking systems with monitoring for drift and bias
- Multimodal pipelines (text + vision) for inspection, compliance, and search
How we make it production-ready
We start with measurable success criteria and build an evaluation suite before scaling scope. That keeps quality stable as prompts, tools, or models evolve. We then add observability—latency, token/cost budgets, failure classification, and user feedback loops—so each release is an improvement you can prove.
Deliverables
- LLM feature design (use-cases, constraints, success metrics)
- RAG pipeline (indexing, hybrid search, re-ranking, citations)
- Evaluation harness (golden sets, regression tests, dashboards)
- MLOps deployment (CI/CD, monitoring, rollback, cost controls)
- Security and governance (PII redaction, access control, audit logs)