Workflow

Build Pipelines

RAG systems, multimodal OCR, voice translation, drug discovery, time-series forecasting, and document intelligence.

The problem

Production ML is more than a model: ingestion, retrieval, multimodal parsing, and monitoring must work together; stitching pipelines by hand is slow and brittle.

The answer lives in a table or an image, but your pipeline only indexes text.
Three chained models means three separate points of silent failure.
Someone still re-types numbers from a scanned PDF into a spreadsheet by hand.

How NEO helps

NEO wires end-to-end pipelines (RAG, OCR, audio, forecasting) with versioning and checks so teams get reliable flows instead of fragile one-offs.

Unifies text, image, and table retrieval behind one multimodal embedding index
Chains transcription, translation, and synthesis into a real-time speech pipeline
Extracts structured tables out of scanned documents, merged cells included

Scenarios (3)

0.030s retrieval, 60%+ accuracy

Build Pipelines

Multimodal RAG System

CLIP embeddings with ChromaDB for text, images, and tables. 0.030s retrieval with 60%+ cross-modal accuracy.

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1.3s end-to-end latency

Build Pipelines

Real-Time Voice Translation

Whisper + MarianMT + Edge-TTS delivering speech-to-speech translation in 1.3 seconds across four languages.

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96%+ extraction accuracy

Build Pipelines

Financial Document Table Extraction

Table Transformer + TrOCR detecting and extracting structured tables from scanned PDFs with 96%+ accuracy.

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Run this workflow in your workspace

Install NEO in VS Code or Cursor and ship training, evals, and deployment from chat.

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eval_pipeline.py

  def run_eval(model, tasks):-     results = model.predict(tasks)+     results = batch_eval(model, tasks)+     log_regression(results)      return score(results)