GoMask.ai
Autonify Ltd · Coding
GoMask.ai is a test data management platform that produces realistic fake datasets and masks sensitive production records, so engineering teams can stop waiting on manual data tickets. Its Data Factory tool works as a synthetic data generator that turns a plain-English brief into a full schema, then builds rows with the relationships and edge cases your real data has. It's also a data masking tool for teams that can't avoid production data. You get results as CSV, JSON, Parquet or SQL, or you push them straight into Postgres or SQLite through an API.
About GoMask.ai
What Is GoMask.ai
GoMask.ai is built by Autonify Ltd, a small UK company founded by three engineers who got tired of chasing test-data requests through internal tickets. The product comes in two parts. There's the original masking platform, which replaces personal details inside production data before anyone outside the data team sees it. Then there's Data Factory, the newer piece most people sign up for, which skips production data entirely and builds fresh synthetic datasets from a written description.
The core pitch is that test data should look like real data without being real data. When every column is generated independently, you end up with nonsense relationships, like a low credit score sitting next to a huge monthly spend. GoMask.ai generates columns in relation to each other, so a spend value rises with a credit score and outliers show up at the rate you ask for. Bugs that only appear under realistic conditions actually surface before release. That's the whole point.
The main limitation is scope. Data Factory is a web product aimed at engineers, QA teams and data scientists, and it assumes you're comfortable with schemas and SQL. New connectors, on-prem deployment and high-volume masking sit behind a separate enterprise agreement. So smaller teams mostly work through the self-serve tiers.
Getting Started
- Sign up on the GoMask.ai site. No card is needed for the free tier, which covers 1,000 generated rows.
- Write a brief in plain English: the columns you need, value ranges, how they relate, and the row count.
- Review the blueprint the tool drafts. You can edit the schema, personas and cross-column rules in a table before anything is generated.
- Run the generation job. Rows are produced in parallel batches and deduplicated as they land.
- Download the result in one of eight formats, pull it through the API, or write it into Postgres or SQLite with a dry run first.
Product Information
A quick look at GoMask.ai's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- ML engineers who need labeled datasets with balanced classes and controlled rare-class rates, exportable as JSONL or Parquet for a training pipeline.
- QA and backend developers who want multi-table test data with foreign keys intact and SQL inserts in their dialect.
- Researchers and educators who need statistically shaped datasets for coursework or papers, where the brief doubles as a methods section.
Tasks
- Generating a million-row customer table with realistic spend and score relationships for load testing.
- Producing masked copies of production records when a bug only reproduces against live-shaped data.
- Creating demo or vendor datasets that carry no personal information, which gives you GDPR compliant test data without a legal review.
Scenarios
- Standing up a staging database that matches your production schema without copying production rows.
- Reproducing a rare refund, null or unicode case on demand instead of waiting for it to occur in the wild.
- Regenerating an evaluation set from a new seed to check that a model hasn't overfit to one draw.
Key features
Relationship-Aware Generation
Most fake data generator tools build each column on its own, which is why the results fall apart the moment you test anything complex. GoMask.ai fans out values at the cardinality you specify and keeps keys code-owned, so referential integrity survives across tables. A customer's orders link back to the right customer, and a spend field moves with the score field it's supposed to depend on.
Researched Values From Live Sources
When you switch on research, the tool pulls enum values and ranges from current pages on the web rather than guessing. A diagnosis code field can be set to the ICD-10 codes actually recorded in a given specialty, with the source pages cited. That sourcing is specific. Codes, enums and ranges come from public sources; people's names never do, which keeps the research feature clear of privacy problems.
Editable Blueprint Before Spend
The generation step is where credits get consumed, so GoMask.ai separates it from planning. The factory drafts a schema, personas and cross-column rules, and you can edit any of it in a table before a single row exists. Why does that matter? You see what you're about to get before you pay for it, which matters when a run costs one credit per row.
Direct Database Writes With Undo
Data doesn't have to leave as a file. The tool reads your schema, runs a dry run, then writes batch by batch to Postgres or a SQLite file, keeping a savepoint at each step. If something looks wrong, you can undo the whole thing in reverse order. That write path is what makes it useful for filling a real staging database instead of just handing over a CSV. Not many tools go that far.
Credit-Based Pricing With Free Start
Pricing runs on credits, and one credit makes one row. A free tier covers the first 1,000 rows with no card, and paid plans add monthly allocations with top-up packs that never block a run in progress. Credits come out only after a run succeeds. A failed job costs nothing.
Pros and cons
Pros
- Generates columns in relation to each other, so test data holds up under relationship-dependent bugs.
- Free tier covers 1,000 rows with no card, enough to try a real brief end to end.
- Failed runs don't consume credits, which takes the sting out of experimenting.
- Writes straight into Postgres or SQLite with a dry run and a full undo.
- Research mode cites the public pages its enum values and ranges come from.
Cons
- The tool is aimed at engineers, so non-technical users may find the blueprint and schema steps steep.
- Enterprise masking, on-prem deployment and custom connectors need a separate paid agreement, not self-serve.
- Row-based credit pricing means large datasets add up, and there's no flat unlimited tier.
Frequently asked questions
It generates realistic synthetic test data and masks sensitive production data. You describe the data you need, it builds a schema and generates rows with the relationships your real data has, then hands back a file or writes it into your database.
Related content
Explore related tools, skills, and articles for GoMask.ai.
GoMask.ai Alternatives
Forefront
Forefront · CodingForefront is a web platform for building with open-source AI. It lets you fine-tune leading open-source language models on your own data, evaluate how they perform, and run them through an API or export them to host yourself. Developers who want the convenience of a closed-source platform but insist on owning their models and data are the target audience here.
Startkit
StartKit.AI · CodingStartkit is a boilerplate for building AI SaaS and AI wrapper products. Think of it as an AI startup boilerplate with the boring parts already wired up: authentication, Stripe and Lemon Squeezy payments, usage limits, transactional email, and an AI API starter that talks to OpenAI, Anthropic, Groq, or Llama. You clone the repo, set your price, and start on the part of your product that people actually pay for. It's Next.js under React and Tailwind, so most of the boilerplate code already feels familiar.
Testim
Tricentis · CodingTestim is an AI-powered test automation platform for building and running end-to-end tests across web, mobile, and Salesforce applications. It leans on machine learning to keep tests stable when an interface changes, so teams spend less time fixing broken selectors. Not bad for an automated testing tool you can start using today. You create tests by recording actions in a browser, then optionally add JavaScript when you need more control. It's a solid pick for busy QA teams.