Text
Generated messages, documents and dialogue in the domain and languages you specify, with odd phrasing caught and fixed in review.
For rare intents and sensitive text
Generated data, reviewed by people
Synthetic data for when real data is out of reach
Synthetic data is data we generate instead of collect, for cases where real data is too scarce, too sensitive or too slow to gather at the volume your team needs. We generate text, speech, image and video to your brief, and people review it for realism and quality.
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The short answer
What is synthetic data?
Synthetic data is data that's generated instead of collected from real people or events. It's made to look and behave like the real thing, so a model can train on it where real data is too scarce, too sensitive or too slow to gather.
It works best as a supplement. Generated examples fill gaps in a real dataset, like a rare road hazard or a language your data is short on, and the model is still tested on real data it has never seen.
Generated data inherits the blind spots of whatever produced it, and it can look convincing while being subtly wrong. That's why people review it for realism and quality before it's used.
Synthetic data at IndecalAI
- Data types
- Text, speech, image and video.
- When
- Real data is scarce, sensitive or slow to collect at the volume you need.
- Built to
- The domains, languages and formats you specify.
- Review
- People review everything for realism and quality.
- Mix
- It can sit alongside real data collected through our other services.
- People
- Reviewers from a network of 30,000+ people worldwide.
- Your data
- NDA first, used only for the agreed scope, returned or deleted when you ask.
- Contact
- One named contact who replies within one business day.
- First step
- A small free sample before you commit.
- Company
- Based in Ahmedabad, India, since 2021, with 150+ projects delivered.
What we generate
Six ways synthetic data fills a gap
We generate text, speech, image and video data to the domains, languages and formats you specify. People review everything for realism and quality, and it can sit alongside real data collected through our other services.
Speech
Generated speech for the conditions your real recordings are short on, checked by people for whether it actually sounds natural.
For unusual call conditions and accents
Images
Generated images of scenes that are hard to catch in the wild, such as unusual road and weather conditions.
For rare scenarios for vision models
Video
Generated clips of events that rarely happen on real roads or in real footage, reviewed frame by frame for realism.
For hazards that rarely happen for real
Sensitive domains
Stand-in text for regulated fields like healthcare and banking, where real records can't be collected or shared freely.
For healthcare and banking models
Filling coverage gaps
Extra examples for languages, accents and demographics that a real collection would take months to fill, while you collect more.
For balanced training data
Where it's used
What synthetic data does for a model
Common reasons teams generate data, and the part of this page behind each one. They're examples of the work, not a client list.
Healthcare and banking
Stand-in data for fields where real records can't be collected or shared freely.
Rare road scenes
Plenty of examples of a hazard that rarely happens on real roads, for a driving model to learn from.
Balancing languages
When your real data has thousands of examples in one language and a handful in another, generated data evens out the balance.
Unusual call conditions
Calls with the noise, line quality or accents your real recordings are short on.
Rare intents
More examples of the requests that almost never come up, so a bot still recognizes them.
Training on a mix
Synthetic data fills gaps and real data keeps the model honest. Our guide goes into the trade-offs.
Where it falls short
Use it as a supplement, and test on real data
Generated data inherits the blind spots of whatever produced it. If the generator has never seen a situation, the synthetic examples won't contain it either. It can also look convincing and still be subtly wrong, which is why human review matters.
The safest way to use it is as a supplement (our guide on synthetic data vs real data goes into the trade-offs). Train on a mix, and always test on real data the model has never seen. If a model does well on synthetic test data and badly on real data, you've learned something important before launch, and that's a good outcome.
Before generating anything
Settle these first
Synthetic doesn't automatically mean private. A dataset generated from real records can still leak details about them if it's done carelessly, so these get settled at the start.
The gaps you're filling
The data type, the domain, the languages and exactly what your real data is missing.
How it relates to real data
Whether any real source data is involved, and how the generated data relates to it. This decides how private the result is.
What may leave your environment
Which data, if any, can be shared outside your systems, agreed before anything is generated.
How it gets tested
Always test on real data the model has never seen, so a gap between synthetic and real shows up before launch.
How it works
From your brief to reviewed synthetic data
One named person owns the project and stays with it afterward. This is the order things happen in.
Brief
You tell us the data type, the domain, the languages and the gaps you're trying to fill.
NDA first
The NDA is signed before any of your data moves.
Source rules
We settle how the data relates to any real source, and what may leave your environment.
Free sample
We generate a small sample free, so you can check it before you commit.
Generation
The data gets generated to that brief.
Human review and delivery
People check the output for realism and quality before delivery.
Glossary
Synthetic data vocabulary
The words that come up when you plan a synthetic data project, and what each one means.
- Synthetic data
- Data generated to look and behave like real data, instead of being collected from real people or events.
- Seed data
- A small set of real examples that generated data is modeled on. How it's used affects privacy.
- Data augmentation
- Making new training examples by changing existing ones, like flipping an image or adding noise to audio.
- Domain gap
- The difference between generated data and the real world. A model trained only on synthetic data can stumble on it.
- Privacy leakage
- When generated data reveals details from the real records it was based on.
- Edge case
- A rare situation, like an unusual hazard or call condition, that real data seldom contains.
- Realism review
- People checking whether generated data looks, reads or sounds like the real thing.
- Mixed training set
- Real and synthetic data used together, usually with real data kept back for testing.
Related work
Real data, on either side
One team can collect the real data, generate what's missing and check your model on real data. You can also compare all eight services.
Data collection
Real data collected to sit alongside the generated set.
Data and model evaluation
Your model tested on real data it has never seen.
Data annotation and labeling
Labels added or checked on generated data.
Computer vision
Real images and video labeled for detection and segmentation.
Conversational AI data
Real dialogue recorded for voice products.
FAQ
Questions about synthetic data
Short answers about this service. The services FAQ covers the other services and how they fit together.
What is synthetic data?
Data that's generated instead of collected from real people or events. It's made to look and behave like the real thing, so a model can train on it where real data is too scarce, too sensitive or too slow to gather.
Which data types can you generate?
Text, speech, image and video.
When does synthetic data help most?
Where the real thing is hard to get: records that can't be shared in healthcare or banking, rare scenarios like unusual road, weather or call conditions, and languages, accents or groups your real data is short on.
Where does synthetic data fall short?
It inherits the blind spots of whatever produced it, and it can look convincing while being subtly wrong. Use it as a supplement, train on a mix, and always test on real data the model has never seen.
Is synthetic data private by default?
Not automatically. How private it is depends on how it was generated and whether real records were used as a source. Settle that before the work starts.
Can I mix synthetic and real data?
Yes, and that's usually the better plan. Use synthetic data to fill gaps, and test on real data the model has never seen. Our guide on synthetic data vs real data goes into the trade-offs.
Do people check the generated data?
Yes. People review everything for realism and quality before it's delivered.
What do you need from us to start?
The data type, the domain, the languages and the gaps you're trying to fill, plus a rough volume. If a real source is involved, we settle how the generated data relates to it first.
Can we try a small sample first?
Yes. Send a short brief and we'll generate a small sample free before you commit.
Does our data stay private?
Yes. The NDA is signed before any data moves, we use it only for the scope we agreed, and only the people working on your project can get to it. It's returned or deleted when you ask.
Start with a free sample
Pick the data types, say which gaps you're filling and roughly how much you need, and send it. We'll generate a small sample free before you commit.
- Your named contact replies within one business day.
- We sign an NDA before any data changes hands.
- We generate your free sample, then agree the full scope.
This opens your email app with the brief filled in. Nothing is sent until you press send. You can also use the contact page.