Overview
About POMA AI | Solving Chunking
POMA AI builds the missing layer between your data and your LLMs: an ingestion+chunking API for 50+ file formats and a managed Context Engine (RAG API) that returns clean, verifiable context instead of black-box answers.
Our ingestion pipeline converts PDFs, Office documents, HTML, Markdown and more into precise, model-compatible knowledge units. It handles OCR, tables and complex layouts, and emits consistent, context-safe chunks that work directly in RAG pipelines—without ad-hoc preprocessing or fragile text splitters.
On top of that, our Context Engine serves structured context "as a Service": ranked, explainable context packages for chatbots, agents, internal tools and all LLM-based applications. Your systems call a simple API (documents in → context out); you keep full control over prompts, models, and UX.
Under the hood, we focus on deterministic, reproducible and auditable retrieval. Every chunk has lineage and traceability, so you can understand why a given answer was produced, debug failures, and meet compliance expectations—even as you swap models or change vendors.
The result: RAG becomes a standardized infrastructure component instead of a fragile one-off project. Teams ship faster, hallucinations drop, token and compute waste go down, and knowledge flows stay stable over time.
Our goal is to be the technical reference for context-based RAG systems: clean interfaces, high-quality data processing, strong performance, and complete transparency in the retrieval stack. Let's try it out!
Our ingestion pipeline converts PDFs, Office documents, HTML, Markdown and more into precise, model-compatible knowledge units. It handles OCR, tables and complex layouts, and emits consistent, context-safe chunks that work directly in RAG pipelines—without ad-hoc preprocessing or fragile text splitters.
On top of that, our Context Engine serves structured context "as a Service": ranked, explainable context packages for chatbots, agents, internal tools and all LLM-based applications. Your systems call a simple API (documents in → context out); you keep full control over prompts, models, and UX.
Under the hood, we focus on deterministic, reproducible and auditable retrieval. Every chunk has lineage and traceability, so you can understand why a given answer was produced, debug failures, and meet compliance expectations—even as you swap models or change vendors.
The result: RAG becomes a standardized infrastructure component instead of a fragile one-off project. Teams ship faster, hallucinations drop, token and compute waste go down, and knowledge flows stay stable over time.
Our goal is to be the technical reference for context-based RAG systems: clean interfaces, high-quality data processing, strong performance, and complete transparency in the retrieval stack. Let's try it out!