Product Design, Development
Developer Tools / AI SaaS
React.js, Node.js, OpenAI
Web SaaS
RNDGen is an AI-powered data generation platform built for developers, QA engineers, and data teams who need realistic, structured, privacy-safe datasets — without uploading or exposing real data.
The platform embeds an AI inference layer directly into the data schema pipeline. Users describe what they need in natural language; the system figures out the structure, field types, relationships, and output format.
RNDGen
The custom R icon references the randomness and structure at the core of the product a mark that reads as both a letter and a system.
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Design and build a complete AI data generation platform from scratch.
A two-layer architecture: AI for schema intelligence, deterministic engine for row production. The model never writes output — it configures the pipeline that does.
We designed and built RNDGen entirely from scratch — a production-grade AI SaaS that covers the full spectrum of data generation needs, from fast mock-ups to statistically accurate synthetic datasets.
Every generation mode — from rule-based Fake Data to AI-driven Synthetic — produces output that behaves like real data in real systems.
100+ classified field types. Three output formats. Five generation modes. Users mix and match at the field level — not the platform level.
Persistent workspace, full admin observability, generation history, and audit trail — built for teams, not just individuals.
Not four versions of the same thing — four different execution models
Mode 01
Deterministic Rule Engine
Entirely fictional data generated by rule — fast, reproducible, zero variance. No AI involved. Type-locked per field, instant preview, exact output every time.
Key Capabilities:
190 classified field types across 15 categoriesCustom JavaScript executor — field N can reference fields 1 through N-1Instant preview — zero cost, exact outputUse when the schema is known and output must be predictable.
Mode 02
Schema-Coherence Runtime
Deterministic generation with a field coherence enforcement layer on top. Revalidate Fields performs a consistency pass — checking that type assignments are coherent across the full schema context.
Key Capabilities:
A consistency pass checking type assignments against full schema context. Ensures logical coherence across all generated fields.The engine understands field relationships — age ranges match roles, dates respect logical sequences, statuses align with entity types.Slightly slower than pure Fake Data due to the validation layer, but produces datasets with higher internal consistency.Use when cross-field coherence matters more than raw speed.
Mode 03 · AI-Powered
Language Model Inference Pipeline
Describe each field in plain language. The AI resolves the schema — types, distributions, context. The deterministic engine produces the rows. Statistically realistic, fully anonymized, no real data involved.
Key Capabilities:
Synthetic — partial inference run before full generationSynthetic preview runs a partial inference — the model executes against a sample before the user commits to full-scale generation.Model handles semantics and resolves types from ontology; deterministic engine handles row production at scale. No model involvement in output generation.Use when realism, context, and privacy all matter simultaneously.
Mode 04
Deterministic Credential Utility
A rule-based credential generator built into the same workspace as data schemas. Configurable length, character sets, exclusion rules, and batch generation — with a real-time strength indicator.
Key Capabilities:
Length 4–104 characters via sliderCharacter sets: digits, lowercase, uppercase, symbols, additional symbolsExclude Similar (i, l, 1, L, o, 0, O) and Exclude Duplicate rulesBatch generation count with real-time strength indicator (Weak / Medium / Strong)Test datasets and test credentials — same workspace, same hierarchy.
The model's output is a starting proposal, not a final decision. Every resolved type is surfaced as an editable field. Users choose, at the field level, which fields use inference and which require determinism.
Synthetic Field
Natural language → model infers type and distribution. User can override after inference.
Fake ID Field
Predictable keys and sequential identifiers injected into a model-driven schema.
Fake Field
Type-locked deterministic field inside a Synthetic schema. Precision where it matters.
function(line) {
return line.firstname + '.' +
line.lastname + '@gmail.com';
}
Schemas live here. Persistent, organized, reusable.
The Collection → Folder → File hierarchy transforms RNDGen from a one-time generator into a persistent schema library. Teams accumulate and reuse AI-configured schemas across projects — session-independent, organized, and shareable.
Workspace actions — duplicate, move, reorganize without losing state
The analytics layer is a system health instrument — not just a business dashboard. AI inference is tracked as its own monitored dependency, independent of general platform metrics.
Inference layer tracked as an external dependency. Error rate over time reveals degradation patterns and model API change impact.
Volume by runtime: Fake, Simulated, Synthetic, Password, Anonymized, Augmented. Direct inference utilization signal per mode.
Registration trends, plan adoption, and churn signals over time. Per-user tier and status visible in the accounts table.
Every generation is logged. Every user request is tracked. Both systems are native to the platform — no third-party integrations.
Per-event log with timestamp, user, AI request, AI result, output options — re-download, schema replay, inference debugging, billing reconciliation.
Native ticket system — threaded conversations, file attachments, status tracking, no external helpdesk required.
The entire platform — generation modes, workspace, admin layer, history, support — fully responsive. Complex multi-layer functionality that works as well on mobile as it does on desktop. Credit to the design and engineering team that made it seamless.
AI intelligence, full user control AI-powered generation without black-box automation. Every model decision is transparent, editable, and auditable — by design.
From realistic to synthetic — one platform Supports rule-based, AI-assisted, and fully AI-generated synthetic data. Adapts to any testing or development scenario without switching tools.
Complex made predictable Preview-first workflows and clean UX make AI-driven data generation easy to use — even for teams without ML expertise.
Faster testing. Less manual work. Teams validate real-world scenarios with confidence, reduce manual data preparation, and ship products faster.
Our creative solutions have helped clients raise $100+ mln and expand their reach.
