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Case study 02 · Document intelligence

Azeru

Enterprise RAG that turns PDF libraries into searchable knowledge bases with conversational, citation-aware answers.

Role Fullstack / AI engineer
Stack Go · Next.js · Groq
Domain Enterprise knowledge

Overview

Azeru addresses a practical enterprise bottleneck: valuable knowledge locked in PDFs that keyword search cannot reliably surface. The platform ingests documents, chunks text for embedding quality, stores vectors for semantic retrieval, and exposes a conversational UI where answers are grounded in retrieved context rather than free-form model memory.

warning

The challenge

Manual document review doesn’t scale across thousands of heterogeneous forms and reports. Teams need extraction and Q&A that respects structure, preserves provenance, and stays interactive — not overnight batch jobs that return untrusted blobs of text.

Pain: latency of human review · error-prone entry · weak search

architecture

The solution

A Go backend owns ingestion, chunking, and retrieval orchestration for throughput. Next.js provides the validation and chat surface. High-speed LLM inference (Groq / Llama-class models) and optional local OCR/LLM paths (Ollama) complete generation with retrieved context.

Neural structure recognition Realtime validation loop
Technical design

RAG path end to end

Quality RAG is mostly systems work: how you split documents, what you embed, how you rank neighbors, and how much context you inject before generation. Azeru optimizes each stage for interactive enterprise use.

  1. 01

    Ingest & parse

    PDFs and document uploads enter the Go service. Text extraction (and OCR where needed) produces a structured intermediate representation ready for chunking rather than one giant string dump.

  2. 02

    Chunk for retrieval quality

    Intelligent chunking balances context window limits against semantic coherence — oversized chunks dilute similarity; undersized ones lose meaning. Chunk metadata preserves source anchors for citations.

  3. 03

    Embed & search

    Vector embeddings enable semantic search so queries match meaning, not just keywords. Top-k retrieval assembles the working context for the generation step.

  4. 04

    Generate with grounding

    The conversational interface sends user questions with retrieved context to Groq-hosted models. Responses are designed to stay citation-backed so operators can verify claims against source documents in the same session.

Backend

Golang / Gin

UI

Next.js

Inference

Groq / Ollama

Search

Vectors

Ops

Docker

Results

Measured impact

85%

Reduction in document processing time

99%

Extraction accuracy (target / measured on eval set)

50+

Supported document types / layouts

Engineering takeaways

  • · Chunking strategy dominates retrieval quality more than model brand choice.
  • · Split CPU-heavy ingestion (Go) from interactive UI (Next.js) for clearer scaling.
  • · Citations turn RAG from “chatbot demo” into an auditable knowledge tool.
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