Beyond Chat: Automating Deep Research at Scale

We are currently living in the "Chatbot Era" of AI. Whether it's ChatGPT, Claude, or Gemini, the primary interface for most people remains a simple text box: you ask a question, and the AI answers.

This interaction model is fantastic for ideation, quick summaries, or creative writing. But for knowledge workers dealing with complex, multi-step tasks (like researching market trends across 50 reports, or extracting specific clauses from a folder of contracts), the chat interface breaks down.

The problem isn't the AI's capability; it's the interface. Chat is linear, ephemeral, and low-bandwidth. Real work is structured, persistent, and data-intensive.

At Clavy, we believe the next phase of AI isn't about better chatbots. It's about automating the manual work that happens between the questions.

The Limits of "Chatting with your PDF"

Most AI tools treat documents as simple reference material. You upload a PDF, ask "What are the key risks?", and get a summary. This works well for one document.

But what happens when you have:

• Volume: 200 PDFs that need to be analyzed consistently.
• Structure: You need specific data points (dates, amounts, names) extracted into a spreadsheet.
• Synthesis: You need to compare findings across all documents to find trends.

In a chat interface, this is a nightmare. You have to upload files one by one, copy-paste prompts, manually record the answers in Excel, and hope the AI didn't hallucinate or forget instructions halfway through. This isn't "AI automation". It's just a new kind of manual data entry.

Moving from Conversation to Computation

Clavy takes a different approach. Instead of just "chatting" with files, we treat your documents as a structured dataset that AI can process at scale.

Here is how we are redesigning the research workflow:

1. Unified Context, No More Uploads

Your files live in Clavy. You don't need to re-upload PDFs every time you want to ask a question. The workspace index is persistent, meaning the AI already "knows" your documents before you even type.

2. Structured Extraction over Open-Ended Chat

Instead of asking "Tell me about this file," you define a schema. Review Grid is an AI-powered spreadsheet where each column is a question and each row is a record you care about.

Take the contracts example. Rather than a freeform prompt, you create three columns: Effective Date (date), Termination Clause (text), and Liability Cap (number). Each column has its own extraction instruction, and columns can depend on each other. A "Party Name" column can feed into a "Governing Law" lookup, chaining prompts together without any manual wiring. Beyond your documents, columns can also be pointed at the live web entirely, letting you mix document extraction and live research in the same table.

3. Classification and Enrichment at Scale

Columns aren't limited to extraction either. Define a Risk Level column with options Low, Medium, and High, and the AI classifies each contract automatically, turning a manual review process into a sortable, filterable field. Each column can also use a different AI model, whether that's a faster one for straightforward classification or a more capable one for nuanced clause extraction, so you're not paying for more than you need.

With the schema defined, Clavy runs each column prompt across every row in parallel batches, filling the table automatically. Fifty contracts become a structured spreadsheet, not fifty paragraphs to read through, but rows you can filter, sort, export, and share with a stakeholder in seconds.

The output isn't a document. It's a database you can actually use.

4. Traceability and Trust

One of the biggest issues with LLMs is hallucination. When an AI summarizes 100 pages into one paragraph, how do you trust it?

Clavy answers this with full traceability. Every generated value comes with a reasoning trace: a step-by-step account of exactly how the AI arrived at that answer, which sources it drew from, and what evidence it used to reach its conclusion. You don't have to take the output on faith. You can follow the reasoning, check the sources, and verify the result in seconds rather than re-reading the source yourself.

From One Question to a Whole Dataset

The most powerful shift Clavy enables isn't about any single answer. It's about changing the unit of work.

In a chat interface, the unit is a question. You ask, you get an answer, you ask again. Throughput is limited to how fast you can type and how much context you can hold in your head at once.

In Clavy, the unit is a transformation. You define what you want to know (the column prompts), point it at your data (the rows), and run. Clavy dispatches a swarm of agents across your entire dataset, filling the table while you focus on something else. Whether that's 10 records or 500, the effort to define the work is the same. Only the scale changes.

This is what automation actually means for knowledge work: not a faster way to answer one question, but a way to apply consistent, repeatable reasoning across an entire dataset. Define it once. Run it at scale. Review the results.

Ready to stop copy-pasting?

If you're tired of juggling file uploads and chat windows, Clavy is built for exactly this. By combining the reasoning power of frontier LLMs with a deep understanding of your specific files and data structure, we automate the "drudgery" of research, leaving you with the insights.

Request a demo and see how it works across your own data.