DataFramer - DataFramer empowers you to take your own data further — generate, anonymize, augment, and simulate diverse datasets for testing, evals, and fine-tuning of ML and AI models.

DataFramer

Reach humans, not spam folders

Email for developers is a platform for developers to send and receive email, and manage their inbox. It is designed to be easy to use and customizable, with a focus on speed and efficiency.

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cool stats

Beyond the basics stats for the platform, we have some more interesting stats.

$70M

total increase in Lexington

3m faster

Spazio to launch a platform

uplift in Unwrapped payment

5 days

Ike expansion in one lexington

results

Email for developers is a platform for developers to send and receive email, and manage their inbox.

It is designed to be easy to use and customizable, with a focus on speed and efficiency.

Tools to build optimized checkout flows

—Embeddable checkout
—Custom UI toolkit
—Invoice support

Global payments with a single integration

—Embeddable checkout
—Custom UI toolkit
—Invoice support

scenarios

Privacy-Safe AI Evaluations and Development

DataFramer generates fully synthetic datasets that preserve statistical fidelity while removing or masking PII/PHI. Enterprises can test and train models without exposing customer data.

Compliance with HIPAA, GDPR, SOC2
Build AI without risking leaks
Unlock access to restricted datasets for faster iteration

Smarter, Safer Conversational AI

DataFramer simulates multi-turn dialogues, including rare or adversarial scenarios, to stress-test chatbot logic before deployment.

Train bots on rare/edge cases
Improve handling of context over long conversations
Reduce failure modes and hallucinations

Bias-Free, Realistic Tabular Data

DataFramer expands tabular datasets with realistic synthetic records that mirror true numerical distributions (e.g., transactions, claims). Gaps and imbalances are corrected automatically.

Fairer AI decisions across demographics
Safe financial data that's accurate to distributions
Fill gaps in edge cases for risk/fraud modeling

Boost Model Accuracy with Synthetic ML Data

DataFramer generates rare events and minority-class examples, strengthening training datasets for anomaly detection, classification, risk scoring, and recommendation engines.

Improve recall on rare anomalies
Reduce false negatives in risk models
Better personalization for recommendations

Stronger Models for Text & Document AI

DataFramer creates synthetic long-form documents with labeled entities, section structures, and complex layouts. Perfect for training extraction models without licensing or compliance hurdles.

Train on larger, richer document sets
Handle edge cases (nested entities, long spans)
Reduce annotation costs for long text corpora

customizable

Integrate with your existing email.

DataFramer allows you to create different

import { defineConfig } from 'astro/config';
import tailwind from "@astrojs/tailwind";
import image from "@astrojs/image";
import compress from "astro-compress";
import sitemap from "@astrojs/sitemap";
import mdx from "@astrojs/mdx";
export default defineConfig({
markdown: {
    drafts: true,
    shikiConfig: { theme: "css-variables" }
  },
  shikiConfig: {
    wrap: true,
    skipInline: false,
    drafts: true,
  },
   site: 'https://lexingtonthemes.com',
  integrations: [tailwind(), image(),
  compress(), sitemap(), mdx()]
});

import { defineConfig } from 'astro/config';
import tailwind from "@astrojs/tailwind";
import image from "@astrojs/image";
import compress from "astro-compress";
import sitemap from "@astrojs/sitemap";
import mdx from "@astrojs/mdx";
export default defineConfig({
markdown: {
    drafts: true,
    shikiConfig: { theme: "css-variables" }
  },
  shikiConfig: {
    wrap: true,
    skipInline: false,
    drafts: true,
  },
   site: 'https://lexingtonthemes.com',
  integrations: [tailwind(), image(),
  compress(), sitemap(), mdx()]
});

testimonials

Our beloved customers

"Integrating with LexingtonElements was surprisingly easy. Having Lexingtonhandle localization, formatting, and automatically displaying relevant local payment."

Amrit Nagi, Founder of Tailwind Toolbox

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.

Pay-as-you-go

Instant bank account verifications

Obtain bank names and tokenized account numbers to connect a user’s bank account and verify that it’s open.

$1.50

Per verified account

Balances

Retrieve account balances for underwriting, financial management, or preventing payment failures due to insufficient funds.

10¢

Per successful API call

Account owners

Pull account owner information, such as first name, last name, and address.

$4.50

Per successful API call

Custom pricing

Custom pricing is available for companies with a high volume of API calls or unique business models.

Email info@dataframer.ai

EXPLANATION

What's in the box

Tools for crafting optimal sign-up

Customizable forms
UI toolkit for email design
Support for automated responses

Corldwide reach with single integration

Global email templates
Localization tools
Compliance and regulation support

In-depth security protocols

Encryption for email data
Anti-spam filters
User authentication processes

detailed analytics and reporting

Real-time open rates
Click-through rate analysis
Subscriber growth tracking

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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