DataFramer
DataFramer

Email for developers and banking institutions

DataFramer. is a beautifully design UI Kit that allows you to put togheter pages from different categories and customize them to your needs to create a landing page or a whole website in hours, not months.

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Feature

100% responsive emails

We have tested our platform with over 100 email clients and it works on all of them we swear.

  • 1

    Prebuilt integrations

    Municipal management performance corporate investment grade bonds.
  • 2

    Tools for every stack

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  • 3

    Prebuilt integrations

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  • 4

    Tools for every stack

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Stunning layouts

Your implementation includes relevant deep dive workshops so we can understand your requirements and create tailored integration guides for your teams to build faster.

  • Prebuilt integrations

    • ⏤ Embeddable checkout
    • ⏤ Custom UI toolkit
    • ⏤ Invoice support
    • ⏤ Payment optimization
    • ⏤ Data migration
    • ⏤ Product monetization strategy

  • Deployable checkouts

    • ⏤ Embeddable checkout
    • ⏤ Custom UI toolkit
    • ⏤ Invoice support
    • ⏤ Payment optimization
    • ⏤ Data migration
    • ⏤ Product monetization strategy
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banking

Email for developers

Secure access to users’ financial data enables you to streamline common processes, such as onboarding and creditcard, and build new products to help users manage their finances. B

  • Clever layouts

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  • 100% responsive

    Debt bills lucrative capitalization municipal bonds government 401k bear bull credit corporate bonds called NYSE finance. Managed prices bondholders federal risk NASDAQ hedge fund expenses municipal bonds management.
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  • 100% responsive

    Debt bills lucrative capitalization municipal bonds government 401k bear bull credit corporate bonds called NYSE finance. Managed prices bondholders federal risk NASDAQ hedge fund expenses municipal bonds management.
    thumbnail

  • 100% responsive

    Debt bills lucrative capitalization municipal bonds government 401k bear bull credit corporate bonds called NYSE finance. Managed prices bondholders federal risk NASDAQ hedge fund expenses municipal bonds management.
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testiomonials

Spazio Bianco is love around the world.

Snowflake is a cloud data platform that enables data storage, processing, and analytics solutions for businesses.

  • Challenge

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    Solution

    Debt bills lucrative capitalization municipal bonds government 401k bear bull credit corporate bonds called NYSE finance. Managed prices bondholders federal risk NASDAQ hedge fund expenses municipal bonds management.

  • Products used

    ⏤ Deployments
    ⏤ Analytics
    ⏤ Payments
    ⏤ Checkouts

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pricing

Always know what you'll pay, transparent pricing for everyone.

Choose a plan that works the best for you and your team. Start small, upgrade when you need.

Hobby

$15 $8

Monthly (billed annually)

This plan suits individual users and hobbyists needing basic support

  • 5 mb/PDF
  • 75 pages/PDF
  • 10 messages/Day
  • Up to 3 PDFs
  • Gpt-3.5-turbo model
  • Test mode
  • Doc summary

Pro

$25 $20

Monthly (billed annually)

This plan is tailored for small businesses and startups

  • 10 mb/PDF
  • 150 pages/PDF
  • 25 messages/Day
  • Up to 5 PDFs
  • Gpt-3.5-turbo model
  • Test mode
  • Doc summary

Team

$50 $40

Monthly (billed annually)

This plan is for medium businesses, providing advanced tools.

  • 32 mb/PDF
  • 1500 pages/PDF
  • 1000 messages/Day
  • Up to 50 PDFs
  • Gpt-3.5-turbo-16k model
  • Test mode
  • Doc summary

Enterprise

$100 $80

Monthly (billed annually)

This plan offers the fullest feature set for large enterprises.

  • Unlimited mb/PDF
  • Unlimited pages/PDF
  • Unlimited messages/Day
  • Unlimited PDFs
  • Custom GPT model
  • Test mode
  • 24/7 personalized support

FAQ

Frequent questions and answers

What is DataFramer?
DataFramer is a synthetic data generation platform that builds safe, scalable, and realistic text and tabular datasets. It provides multiple mechanisms to control data generation, including using your own samples as seeds. It lets you build, test, and deploy AI systems without exposing sensitive information.
How does DataFramer work?
A 3-step process is typical: 1. Upload Seed Samples – Provide example data (CSV, TXT, JSON, JSONL, MD, PDF). 2. Automatic Analysis – DataFramer analyzes data properties and axes of variation (patterns, attributes, distributions). 3. Generate Synthetic Data – Creates new datasets that mirror the statistical fidelity of your originals, without leaking PII/PHI. However, the platform also supports workflows where you don't have to provide examples (seedless generation).
How do I trust DataFramer?
DataFramer evaluates your data both during and after generation for quality and conformance—how well the generated data matches your requirements and target distributions for each data property. Apart from that, you can chat with your generated data to explore and get a deeper understanding of its sttructure and content. DataFramer also provides features that make it easy for expert humans to manually label generated datasets.
What formats can I upload?
You can upload CSV, TXT, JSON, JSONL, PDF, or Markdown files individually or in folders. • Up to 300 files and 50MB total • In CSV and JSONL formats, each row/line is treated as a sample. • You can also upload multiple folders where each folder serves as a single seed sample.
Do I need my own data to get started?
No. You can generate data in seedless mode without providing any examples while maintaining full control over generation. If you do want to provide examples for structure, style, or content, uploading 2 samples is often enough for DataFramer to learn the structure and generate larger, balanced datasets.
How is DataFramer different from anonymization or masking?
Anonymization removes identifiers from real data, but risks re-identification. DataFramer creates entirely new synthetic records that preserve statistical accuracy without exposing original sensitive values or identifiers.
Can I use DataFramer for compliance-heavy industries like healthcare or finance?
Yes. DataFramer was designed with privacy, fairness, and compliance in mind. Enterprises in healthcare (HIPAA), finance (SEC, GDPR), and government use cases can safely train and test AI systems with synthetic data.
What are common use cases that DataFramer can help me with?
• Healthcare: Synthetic EMRs for model testing and training without risking PHI. • Finance & Insurance: Fraud detection, Transaction data, AML, KYC, fair lending. • Conversational AI: Multi-turn chatbot training and edge-case testing. • Market Research: Synthetic survey panels and digital twins. • Text2SQL: Synthetic SQL queries for data validation and testing. • Traditional ML: Classification, anomaly detection, recommendations. • Many more...
How does DataFramer handle long-form text?
For text extraction and NLP tasks, DataFramer uses a long-sample generation algorithm that creates realistic, complex documents (e.g., contracts, medical notes, research papers) to stress-test extraction models.
Can I control the output?
Yes. DataFramer gives you control over: • Your generation objectives which are automatically transformed into a data specification. • The data properties (axes of variation) (e.g., demographics, time, categories) with their probability distributions. • Closed-source or open-source models powering the generation. • The algorithm choice (short-form vs. long-form vs. red-teaming).
How does DataFramer ensure quality?
Generated datasets screened for quality and diversity issues multiple times throughout and after generation. Statistical property matching and fairness checks are also accessible in our workflows.
What's the ROI of using DataFramer?
• Save time: Cut data preparation cycles from months to weeks. • Reduce cost: Avoid expensive manual collection/annotation. • De-risk compliance: Train AI safely without exposing sensitive data.
How can I deploy DataFramer?
DataFramer offers flexible deployment options: • Hosted: Use DataFramer's managed cloud service for quick setup and maintenance-free operation. • On-premise: We are prepared to deploy in days using Kubernetes on any popular cloud (AWS, Azure, GCP) or custom cloud infrastructure for enhanced security and control.
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Ready to get started with DataFramer?

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Supervised deployments

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Databse ready

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Deployments

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