What AI models does the agent use?

The agent has access to Claude, DeepSeek, Gemini, and other top models. It automatically routes to the best model for each task — you never have to think about API keys or token limits.

Yes. Each agent runs in an isolated environment. We don't train on your data, and you can export or delete everything at any time. The underlying engine (GoGogot) is open-source, so you can audit exactly how it works.

What can the agent actually do?

Browse the web, process files (CSV, PDF, images), run scheduled tasks, remember context across sessions, and communicate via Telegram or Slack. Think of it as a capable junior employee, not a chatbot.

How does memory work?

The agent maintains persistent memory across sessions. It remembers your preferences, past conversations, and task context. You can also explicitly tell it to remember or forget things.

Can I self-host the agent?

Absolutely. GoGogot is 100% open-source. Run it on your own hardware for free. cowork.ink is the managed version — we handle servers, model costs, updates, and uptime so you don't have to.

Is there a free trial?

We offer a 7-day money-back guarantee. Spin up an agent, give it real tasks, and if it doesn't save you time, we'll refund you — no questions asked.

What is AI data extraction?

AI data extraction uses large language models (LLMs) or ML models to identify and pull specific fields — like names, dates, amounts, or addresses — from unstructured sources such as PDFs, scanned documents, emails, or websites. Unlike traditional OCR, AI understands context and handles varied layouts without rule programming.

How does AI extract data from documents?

The pipeline has four steps: parse the source into text or tokens, send the text with an extraction prompt to an LLM, receive structured output (usually JSON), then validate against a schema. Tools like LangChain with Pydantic or AWS Textract handle this automatically for common document types. See our [AI document processing guide](/blog/ai-agent-document-processing/).

What is the difference between AI data extraction and OCR?

OCR converts images to raw text character-by-character with no semantic understanding. AI data extraction goes further — it reads that raw text and understands what each piece means, extracting named fields even when layout changes. Most production pipelines use OCR first, then AI extraction second.

How accurate is AI data extraction?

Accuracy depends heavily on document quality and method. A 2025 study in Annals of Internal Medicine found LLM-assisted extraction achieved 91% accuracy while cutting extraction time by 41 minutes per study. For lower-quality documents, hybrid approaches with validation layers typically reach 85–95%. Without validation, LLMs can hallucinate field values.

Can AI extract data from unstructured documents?

Yes — this is AI's main advantage over rule-based extraction. LLMs handle free-form legal letters, varied invoice formats, and mixed-layout reports that would require hundreds of custom rules traditionally. The [agentic RAG pattern](/blog/agentic-rag/) extends this to large document collections with retrieval-augmented extraction.

AI Data Extraction: Pull Structured Data from Any Source

Learn how AI data extraction works: prompt-based, fine-tuned, and hybrid methods. PLUS: schema design, accuracy tips, and when each approach wins.

Frequently Asked Questions

What is AI data extraction?: AI data extraction uses large language models (LLMs) or ML models to identify and pull specific fields — like names, dates, amounts, or addresses — from unstructured sources such as PDFs, scanned documents, emails, or websites. Unlike traditional OCR, AI understands context and handles varied layouts without rule programming.
How does AI extract data from documents?: The pipeline has four steps: parse the source into text or tokens, send the text with an extraction prompt to an LLM, receive structured output (usually JSON), then validate against a schema. Tools like LangChain with Pydantic or AWS Textract handle this automatically for common document types. See our [AI document processing guide](/blog/ai-agent-document-processing/).
What is the difference between AI data extraction and OCR?: OCR converts images to raw text character-by-character with no semantic understanding. AI data extraction goes further — it reads that raw text and understands what each piece means, extracting named fields even when layout changes. Most production pipelines use OCR first, then AI extraction second.
How accurate is AI data extraction?: Accuracy depends heavily on document quality and method. A 2025 study in Annals of Internal Medicine found LLM-assisted extraction achieved 91% accuracy while cutting extraction time by 41 minutes per study. For lower-quality documents, hybrid approaches with validation layers typically reach 85–95%. Without validation, LLMs can hallucinate field values.
Can AI extract data from unstructured documents?: Yes — this is AI's main advantage over rule-based extraction. LLMs handle free-form legal letters, varied invoice formats, and mixed-layout reports that would require hundreds of custom rules traditionally. The [agentic RAG pattern](/blog/agentic-rag/) extends this to large document collections with retrieval-augmented extraction.