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LLMWhisperer

Converts complex PDFs and scanned documents into LLM optimized text and streams the extracted data into your workflows for analysis.

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Why use Ceven?

  1. AI native LLMWhisperer integration

    • Describe the outcome and Ceven picks the right LLMWhisperer calls, fills the parameters, and checks the result.
    • Structured, agent friendly tool schemas so each call runs reliably instead of by guesswork.
    • Rich coverage for reading, writing, and querying your LLMWhisperer data, across all 11 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke LLMWhisperer access.
    • Per user and per environment credentials instead of shared keys.

  3. Agent optimized design

    • Actions are tuned from real success and error rates so reliability climbs over time.
    • Full execution logs so you always know what ran in LLMWhisperer, when, and on whose behalf.
    • The agent pauses and asks when LLMWhisperer is unclear instead of plowing ahead.

  4. Enterprise grade security

    • Fine grained access so you control which agents and people can reach LLMWhisperer.
    • Least privilege by default, read scopes first and only the writes a workflow needs.
    • A full audit trail of every LLMWhisperer action to support review and sign off.

Supported tools

Every action Ceven's agents can run on LLMWhisperer, and when to use it.

Convert Document to Text
Use this when you need to send a PDF or scanned image for async extraction to get LLM optimized text.
Retrieve Whisper Text
Pull the final extracted text using a whisper hash after the conversion process completes.
Check Whisper Status
Poll the current state of a text extraction job to see if it is pending, processing, or finished.
Get Whisper Detail
Pull comprehensive progress metrics and metadata for a specific extraction job.
Register Webhook
Set up a callback URL to receive async notifications when document processing is done.
Get Webhook Details
Pull the configuration and authentication token for a registered webhook.
Update Webhook Configuration
Modify the callback URL or token for an existing document conversion webhook.
Delete Webhook
Remove a webhook registration that is no longer needed for your workflow.
Get Usage Information
Pull general account metrics to monitor API consumption and remaining page limits.
Get Usage Statistics
Pull consumption metrics filtered by a specific tag and date range for billing.

10 actions · scroll to see them all

Frequently asked questions

Standard readers often read across the page, mixing columns from different tables into a single nonsensical string of text. LLMWhisperer analyzes the visual layout of the document first to understand where tables start and end. It then reconstructs the data in a way that maintains the relationship between headers and cells. When Ceven pulls this text, the LLM sees a logical structure rather than a jumble of words. This prevents the model from attributing a value from the second column to a label in the first column, which is a common failure point in automated financial auditing or contract review workflows.
Complex document extraction requires significant compute to analyze layouts and OCR scanned images accurately. By using an async model, LLMWhisperer avoids timing out the API connection during large file processing. Ceven handles this by submitting the document and receiving a whisper hash. The agent then either polls the status endpoint or waits for a webhook notification to trigger the next step. This architecture ensures that even a hundred page document with dense tables can be processed fully without crashing the workflow or losing data due to a network timeout during the extraction phase.
Yes, you can use the tagging system within LLMWhisperer to categorize your requests. When Ceven sends a document for conversion, it can attach a specific tag such as Legal or Finance. You can then use the Get Usage Statistics tool to pull consumption metrics filtered by those tags. This allows you to break down your API spend by department or project. Because Ceven can automate these calls, you can build a monthly report that summarizes page usage per tag and pushes that data into a spreadsheet or a billing system for internal chargebacks.
If a document fails, the status check will return an error state instead of a completed status. Ceven can be configured to detect this failure and trigger a retry or alert a human operator. Failures typically happen due to unsupported file formats or corrupted PDF structures. The Get Whisper Detail tool provides specific metadata about the job, which the agent can use to diagnose the issue. For example, if a file is password protected, the agent can flag the document and ask the user to provide an unlocked version before attempting the conversion again.
Webhooks eliminate the need for the agent to poll the API repeatedly. When you register a webhook through Ceven, LLMWhisperer stores your callback URL. The moment a document is finished processing, LLMWhisperer sends a POST request to that URL containing the result. Ceven listens for these events and immediately kicks off the subsequent steps in your workflow, such as summarizing the text or updating a database. This creates a real time pipeline where documents are processed and acted upon the second they are ready, reducing the total latency from upload to insight.
Yes, LLMWhisperer has specific tier gating that limits the number of pages you can process per month and the maximum size of a single file. Depending on your plan, you may encounter a hard limit on concurrent requests, meaning you cannot send too many documents at the exact same second. If you exceed these limits, the API will return a rate limit error. Ceven manages this by implementing a queue system that spaces out requests to stay within your plan limits. You can monitor your remaining quota using the Get Usage Information tool to avoid unexpected interruptions in your production workflows.
LLMWhisperer processes your documents to extract text, but it is designed as a processing layer rather than a storage layer. The extracted text is available for retrieval via the whisper hash for a limited time. Once you have pulled the text into Ceven and stored it in your own database or document store, the temporary files on the LLMWhisperer side are handled according to their data retention policy. You should always ensure that your workflow includes a step to save the final output to your own secure storage if you need a permanent record of the extracted text.
The system is primarily optimized for PDFs and scanned images. This includes digital PDFs created from Word documents as well as images of documents created by scanners or cameras. The core strength is its ability to handle documents that are essentially images of text, where standard copy and paste does not work. Whether it is a JPEG of a receipt or a complex PDF report, LLMWhisperer converts the visual information into a text format that is specifically tuned for the attention mechanisms of large language models, ensuring higher accuracy in downstream tasks.

Alternatives to LLMWhisperer

Other tools that solve a similar problem. Ceven supports these too, so you can switch or run more than one at once.

Amazon Textract logoAmazon TextractGoogle Document AI logoGoogle Document AIAzure AI Document Intelligence logoAzure AI Document Intelligence

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