Retrieval Augmented Generation

Producing an answer is easy. Making it reliable is the hard part.

Ask most tools a question and you'll always get an answer — confident, polished, and sometimes wrong. When the system misses the one place that holds the real answer, it fills the gap with something plausible, and nothing tells you it did. For the documents your business runs on — contracts, filings, policies, manuals — an answer no one can check is worse than no answer at all. The answer only matters if someone can verify it.

With TopK, a question goes in and a grounded, cited answer comes out:

TopK handles the parts that usually make document Q&A hard:

• Ingest handled by default. TopK parses documents, OCRs scans, embeds content, and makes files queryable on upload, so your app talks to one retrieval layer instead of a custom chain of parsers, vector stores, rerankers, and answer code.

• Accurate and verifiable. Answers are grounded in the documents that contain them, with citations to the exact file and page plus a confidence score, so users can check the source instead of trusting a generated response.

• Same primitives from prototype to production. Start with a folder of PDFs and keep the same upload and ask flow as the corpus grows. The product surface stays simple while TopK handles the retrieval path underneath.

• Deployment control. Use TopK's managed cloud, or deploy into your own AWS, GCP, or Azure account so document data, access controls, and audit requirements stay aligned with your environment.

Build it with upload and ask

Whether you are building a search box, chat UI, workflow assistant, or internal agent, the flow is the same: upload your documents, then ask questions over them.

First, bring your documents into TopK. It parses, OCRs, and embeds every file on ingest — PDFs, scanned documents, and images, down to the tables and figures inside them — and they're ready to query shortly after.

topk upload --dataset service-manuals \
  "manuals/**/*.pdf"

Then ask, in plain language. Every answer comes back grounded in your documents, with its citations attached.

topk ask --dataset service-manuals \ 
  "What are the PPE requirements for liquid oxygen and nitrogen servicing?"

Finally, get an answer you can verify. It comes back as structured facts, the exact source each one cites, and a confidence score — so every claim traces back to the document it came from:

{
  "facts": [
    {
      "fact": "Wear a face shield or hard-hat shield combination for eye protection.",
      "ref_ids": ["1"]
    },
    {
      "fact": "Use a leather welder's gauntlet, or cloth gloves with inserts.",
      "ref_ids": ["2"]
    }
  ],
  "refs": {
    "1": { "doc_name": "LOX_LN2_Servicing_Manual.pdf", "doc_pages": [31] },
    "2": { "doc_name": "Cryogenic_Safety_Procedures.pdf", "doc_pages": [12] }
  },
  "confidence": 96.0
}

These examples use the CLI, but the same upload and ask flow is available in the Python SDK, TypeScript SDK, and MCP server. Pick the surface that fits your app; the document Q&A workflow stays the same.

For questions that span many documents, --mode research turns it into an agent that plans its approach, works through your documents, and even runs the numbers itself — building up cited facts step by step before it answers.

Measurably more accurate

No tuning, no custom pipeline. On the Vidore V3 benchmark — judged by GPT-5 across four industry verticals — TopK File Search returns more accurate answers than Gemini File Search and Amazon Bedrock Knowledge Base in every category.