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Synthetic Data Generation with Rumble FishCustom Synthetic Data Solutions for Complex Product Challenges
Why Does Your Company Need Synthetic Data?Let’s face it - your company would probably greatly benefit from using synthetic data - you just might not know it yet! Real-world data can be costly to obtain, difficult to access, time-consuming to label, and have a limited supply, while synthetic datasets can be generated through computer simulations or generative models, making them cheaper to produce on demand in nearly limitless volumes and customized to an organization's needs. Think of it as having a data genie that conjures up exactly what you need, whether that's thousands of fraudulent transactions to train your security models or patient records that won't land you in regulatory hot water.
When information is sparse, synthetic data can fill in the gaps, and it can be used to boost data diversity, especially for underrepresented groups in AI model training. No more playing the waiting game, no more drowning in paperwork for data access approvals, and definitely no more compromising your AI's potential because your real dataset looks like Swiss cheese. It's like having a cheat code for machine learning, except it's totally legal and your compliance team will actually thank you. While your competitors are still arguing about data-sharing agreements, you'll be shipping AI models that actually work, learning from scenarios that haven't even happened yet, and doing it all without exposing a single customer's personal information.
When information is sparse, synthetic data can fill in the gaps, and it can be used to boost data diversity, especially for underrepresented groups in AI model training. No more playing the waiting game, no more drowning in paperwork for data access approvals, and definitely no more compromising your AI's potential because your real dataset looks like Swiss cheese. It's like having a cheat code for machine learning, except it's totally legal and your compliance team will actually thank you. While your competitors are still arguing about data-sharing agreements, you'll be shipping AI models that actually work, learning from scenarios that haven't even happened yet, and doing it all without exposing a single customer's personal information.
Benefits of using synthetic data in your projects
Privacy-First Innovation
Generate unlimited training data without exposing real customer information or violating regulations.
Generate unlimited training data without exposing real customer information or violating regulations.
Cost & Time Efficiency
Synthetic datasets can be synthesized through computer simulations or generative models, making them cheaper to produce on demand than collecting and labeling real data.
Synthetic datasets can be synthesized through computer simulations or generative models, making them cheaper to produce on demand than collecting and labeling real data.
Fill the Data Gaps
When information is sparse, synthetic data can fill in the gaps, especially for rare events like fraud or edge cases.
When information is sparse, synthetic data can fill in the gaps, especially for rare events like fraud or edge cases.
Unlimited Scale
Synthetic data can be produced in nearly limitless volumes and customized to an organization's needs.
Synthetic data can be produced in nearly limitless volumes and customized to an organization's needs.
Boost Model Fairness
Synthetic data can be used to boost data diversity, especially for underrepresented groups in AI model training.
Synthetic data can be used to boost data diversity, especially for underrepresented groups in AI model training.
Use Cases
What is Synthetic Data Used For?Synthetic data solves the critical challenge of needing realistic, diverse data when real info is unavailable, too expensive to collect, locked behind privacy restrictions, or simply doesn't exist yet for new products. It can be used in various ways and in multiple industries, even in gaming!
Development & TestingForget waiting weeks for sanitized production data. Spin up realistic datasets instantly and let your team ship faster without the compliance headaches.
AI Model TrainingTrain your ML models on data that looks, acts, and performs like the real thing—minus the privacy nightmares and regulatory red tape.
Analytics & BIUnlock insights without the endless approval chains. Give your analysts the data playground they need to explore, experiment, and do their jobs right.
Demos & PrototypesShow off your product with confidence. Build impressive demos using realistic data that won't get your legal team's phone ringing.
Governance & FairnessStress-test your models with edge cases and diverse scenarios that real data rarely provides. Build solutions that are not just smart, but responsible.
Why Is It Better?
Why Synthetic Data is Better?Privacy & Safety First
One of synthetic data's superpowers is true privacy by design. Traditional methods like masking or shuffling existing records still use real people's information, just with a thin disguise. Synthetic data flips the script entirely: it learns the patterns and relationships in your original dataset, then creates completely new records from scratch that capture the same statistical DNA. The result? You're building with data that never belonged to anyone in the first place. No real individuals in your pipeline means you're free to innovate without ethical compromise; experiment boldly, share datasets across teams, push your models further, all while respecting that real people's data should stay theirs. It's not just about ticking compliance boxes; it's about building AI the right way, where innovation and privacy aren't at odds. You get all the power to create breakthrough solutions without treating real people as raw material.Why Choose Rumble Fish Over Synthetic Data Platforms?
| Synthetic Data Platforms | Rumble Fish Custom Engineering |
|---|---|
| Configure pre-built generators | Engineer custom solutions for your specific needs |
| Often limited to structured/tabular data | Multi-modal: text, images, video, structured data |
| Generic industry templates | Domain-specific intelligence built in |
| Self-service (you figure it out) | True partnership - we take full ownership |
| Subscription pricing per row/GB | Project-based pricing, you own the solution |
| Works for common use cases | Excels at complex, unique requirements |
Case Study
Case Study: Panenka AI - Gaming Synthetic Data at Scale
That’s when we’ve stepped in with Synthetic Data Generation! Our custom solution used GPT-4.0 with Chain of Thought prompting, combined with Self Consistency, to generate culturally relevant names based on nationality, considering each country's diversity and cultural influences while avoiding famous combinations.
Our team developed a 'genetic' approach by creating lists describing facial elements like lips, noses, eyebrows, cheekbones, freckles, then used GPT-4.o to describe these features in sentences that Leonardo.ai could effectively process. We achieved dynamic player aging by storing the original prompt, seed, and other settings, ensuring consistency in appearance as players aged throughout their careers.
The ChallengePanenka, an AI-powered football manager game, needed to generate 10,000+ unique player profiles with:
- Culturally diverse, realistic names from different countries
- Photorealistic player faces with distinct features
- Consistent aging progression over players' careers
- No copyright violations or privacy concerns
- Cultural authenticity without stereotypes
- Generic name generators: Repetitive, culturally inauthentic, famous name collisions
- Basic image generation: Inconsistent outputs, no aging capability
- Synthetic data platforms: Built for structured tabular data, not multi-modal gaming assets
Our team developed a 'genetic' approach by creating lists describing facial elements like lips, noses, eyebrows, cheekbones, freckles, then used GPT-4.o to describe these features in sentences that Leonardo.ai could effectively process. We achieved dynamic player aging by storing the original prompt, seed, and other settings, ensuring consistency in appearance as players aged throughout their careers.
Who This Is For
- Product Teams with unique synthetic data needs that platforms can't address
- Gaming & Entertainment companies needing procedural content generation
- AI/ML Teams requiring custom training datasets with specific characteristics
- Startups & Scale-ups building innovative products with novel data requirements
- Companies frustrated with the limitations of off-the-shelf synthetic data tools
FAQsLearn more about Synthetic Data Generation with Rumble Fish
Q: What is synthetic data?
A: Synthetic data is artificially generated information that mimics the statistical properties, patterns, and relationships of real data without containing any actual sensitive information. Think of it as creating realistic "fake" data that behaves like the real thing.
For example, in the Panenka project, we generated thousands of player names and faces that look and feel authentic, but none of them are real people. The names sound culturally appropriate for their countries of origin, the faces look photorealistic and age naturally over time, yet no actual person's data or likeness was used.
The key difference from simple "dummy data" or "mock data" is sophistication: synthetic data preserves complex relationships, statistical distributions, and realistic variations that make it suitable for training AI models, thorough testing, and meaningful analysis, not just filling database fields with random values.
For example, in the Panenka project, we generated thousands of player names and faces that look and feel authentic, but none of them are real people. The names sound culturally appropriate for their countries of origin, the faces look photorealistic and age naturally over time, yet no actual person's data or likeness was used.
The key difference from simple "dummy data" or "mock data" is sophistication: synthetic data preserves complex relationships, statistical distributions, and realistic variations that make it suitable for training AI models, thorough testing, and meaningful analysis, not just filling database fields with random values.
Q: How is this different from Gretel, MOSTLY AI, or Tonic?
A: Those are platforms - you configure their pre-built generators.
We're custom engineering - we build solutions tailored to your
specific requirements. When Panenka needed culturally diverse names
and aging faces, no platform could deliver that. We engineered it from
scratch.
Q: Do we need to be technical to work with you?
A: No. We handle all the technical complexity. You bring product
vision and requirements; we bring engineering expertise and ownership.
Q: What if our requirements change mid-project?
A: We expect this. Our agile, iterative process with daily stand-ups
means we adapt quickly. For Panenka, we pivoted our approach when
Leonardo.ai initially struggled; flexibility is built into our
methodology.
Q: What do we get at the end?
A: Complete ownership of the solution: source code, documentation,
trained models, and full integration with your systems. Plus ongoing
support if needed.
Have an idea?
Your Product Deserves Custom Synthetic Data Engineering
Whether you're building the next innovative game, training specialized AI models, or solving unique product challenges, generic platforms won't cut it. We’ve got you covered.From ideation to deployment, Rumble Fish takes full ownership of your synthetic data challenges. Battle-tested technology, true partnership, and the expertise to solve problems that platforms can't touch.
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