AI Pipeline Optimization
NEO inspects your pipeline end-to-end — prompts, retrieval, models, costs, and latency — and delivers a structured report with fixes, not just a list of problems.
Describe what you want. NEO plans, runs, and reports — autonomously.
neo task "Audit the support-bot RAG pipeline.
Measure answer quality, retrieval hit rate, latency, and cost.
Try retrieval fallback, prompt grounding, and cheaper model options.
Write the report to reports/rag-audit.md."Trace failures across prompts, tools, APIs, context windows, retries, secrets, and model calls instead of blaming the model first.
Inspect chunking, embedding quality, thresholds, reranking, source grounding, and zero-context failures.
Build eval sets, run benchmarks, and compare results across prompt and model variants automatically.
Measure token usage, latency, and throughput per step to find where spend is coming from and how to reduce it.
Test multiple models on your data, compare output quality, and decide when fine-tuning makes sense.
Ingest, clean, transform, and validate training and evaluation datasets as part of the same task.
Let NEO find the bottleneck in your AI pipeline and tell you exactly what to change.
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