Build and train AI faster with on-demand synthetic data

Real data was never good enough for gen AI. Privacy & compliance risks, loss of utility, and manual de-identification makes it impractical to scale and move fast. Synthetic data changes everything.

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Test real fake data during a live demo.

Your AI projects are stuck, constantly blocked by the same problems every cycle

Moving fast is a pipe dream, because real data creates endless obstacles:

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Compliance risks

Automated tools aren’t reliable enough to de-identify complex datasets.

Limited scale

Real data rarely provides the volume or variety your models demand.

Slow approvals

Waiting for access to sensitive data grinds your development to a halt.

Synthetic data takes the pain out of training AI models

Real data wasn’t built for this. Synthetic data is ONLY built for this. The complexity. The scale. With instant access to compliant, diverse, and scalable datasets, you can train smarter, move faster, and create better AI models without the headaches.

6 damn good reasons companies are switching to synthetic data

Unlimited scale

Create as much data as you need, whenever you need it—no limits, no delays.

No compliance drama

Stop sweating over PII and privacy laws. Synthetic data keeps things clean.

Faster iterations

Get the data you need instantly and keep your sprints moving at full speed.

Reduced bias

Skip the bias baked into real data and train on fairness from the start.

Safe experimentation

Simulate the extremes and take risks—without risking real data.

Improved quality

Fewer bugs, better validation, and higher confidence with every release.

Synthetic data vs. Real data

One was designed for 2025. One was designed for 2010.

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	Synthetic data	Real data
Scale	Unlimited datasets, generated on demand	Limited by availability and volume
Compliance	Always PII-free and inherently compliant	Risk of breaches with sensitive data
Diversity	Tailored for edge cases and specific needs	Often lacks variety and coverage
Bias	Customizable to ensure fairness	Can introduce biases and skewed results
Speed	Instantly available for testing and training	Approval cycles slow everything down
Quality	Reflects real-world patterns, minus the flaws	Prone to inconsistencies and gaps

TONIC Textual: The smarter, faster way to feed data to your AI models, without the risks

Mock data doesn’t cut it. It misses edge cases, slows down your team, and leaves bugs lurking in production. TONIC replaces it with test data that works—accurate, compliant, and ready to help your team ship confidently.

Replace sensitive data with indistinguishably realistic synthetic values

Extract any data from messy, complex formats with ease

Automatically identify dozens of sensitive entity types in free-text data

See Textual protect your data in real-time

Our proprietary NER models automatically identify entities in your text data to prevent potential privacy vulnerabilities in your AI development. Textual can de-identify any sensitive entities it detects via redaction or synthesis.

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Support for all your data formats

90% of enterprise intelligence is locked up in files across the business. With Textual, you can unlock unstructured enterprise data however and wherever it’s stored:

.csv

.txt

.pdf

XML

HTML

JSON

.pptx

.docx

.png

.jpeg

.xls

+ more

Keep conversations private while preserving value. 

Redact audio files automatically. Now that’s ••••••• awesome!

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Wherever your data lives, Textual makes it instantly usable

“With Tonic, we’ve shortened our build process from 60 minutes down to 20. Their subsetting and de-identification tools are a critical part of Everlywell’s development cycle, making it easy for us to get data down to a useful size and giving me confidence it’s protected throughout.”