Hi Seedfast team, thanks for joining us. For readers who haven’t heard of you yet: what is Seedfast, and what problem does it solve for developers working with databases?
Seedfast is an AI-powered CLI that fills a database with realistic and consistent data fast, so engineers can stop wasting time on brittle seed scripts and stale SQL dumps.
The core problem it solves is simple: teams lose a lot of time getting test and dev environments into a usable state. Seedfast compresses the workflow from “empty tables” to “usable relational dataset” into a single CLI-driven flow which usually takes no more than 5 minutes.

How does Seedfast work in practice? Walk us through the typical workflow. What does a developer do in the CLI from start to having usable data in a database?
In practice, it feels less like “setting up seeding” and more like “getting your environment back.” You point Seedfast at your database, tell it what kind of dataset you want, and it takes care of the messy part.
A typical flow is: an engineer connects Seedfast to a database (local/dev/staging). Then, Seedfast looks at your schema and suggests a seeding plan that makes sense for the tables and relationships it finds. You can quickly tweak the scope (e.g., “seed users + orders, skip billing tables” or “seed only auth schema tables”). You run it - and a moment later you have a database that’s actually usable for building, testing, demos, or QA.
And in CI/CD it’s the same idea, just fully automated. You just provide a pre-defined scope and it runs non-interactively as part of your pipeline.

You emphasize “clean, realistic, and relational” data. What does that mean concretely, and how do you ensure consistency across relationships and constraints?
Clean means the data “fits” your database structure - it doesn’t fight the schema, and you don’t end up debugging broken inserts or messy scripts.
Realistic means it looks and behaves like something your app would actually see in the wild: natural distributions, believable values, and datasets that make features (and edge cases) show up.
Relational means everything connects the way it should: references line up across tables, and you get coherent, end-to-end scenarios instead of isolated random rows.

What’s the core differentiator for Seedfast? What’s the one thing you do that makes people say, “Okay, this is better”?
Seedfast turns database seeding into a one-command experience: you run it against your database, and you get a coherent dataset back - no scripts, no custom code, no config rabbit hole.
The “this is better” moment is when developers realize they don’t need to maintain a fragile seeding codebase anymore. Seedfast understands the schema, generates data that makes sense across relationships and your domain, and lets you steer the result in plain language - so seeding becomes incredibly fast and reliable instead of a recurring project.

You’re offering a 10-day free trial. What have you learned so far about onboarding and pricing, and what has surprised you about how developers evaluate a tool like this?
Quick update: the trial is now 30 days (and no card required). The goal is to make it easy to validate Seedfast on a real schema and real workflow - not in a rushed “weekend experiment.” The trial currently includes up to 50 tables per seed and up to 25 seeding runs.
What we’ve learned is that onboarding lives or dies at one exact moment: time-to-first-successful-seed. If a developer can go from “empty DB” to “usable dataset” fast, the product instantly clicks.
On pricing, we intentionally made it softer and more aligned with the developer community: predictable, easy to reason about, and primarily tied to database size (tables seeded) with cancel-anytime flexibility.
What surprised us: teams don’t evaluate Seedfast as “a data generator.” They evaluate it as a workflow reliability upgrade - something that removes friction from dev, QA, and CI rather than something they want to maintain like a mini-project.

What has been the hardest technical or product challenge so far? How did you solve it, or what are you still working through?
The hardest part of AI seeding isn’t generating strings - it’s making it constraint-aware and trustworthy on real schemas, where constraints, foreign keys, and implicit patterns can interact in surprising ways. That’s why Seedfast focuses on a planning layer built from schema metadata and derived patterns, so outputs remain valid and relationally consistent. Currently (at the time of completing the questionnaire) we’re working on releasing the seeding of dozens of millions records in production with a “beta version” of domain-driven seeding.

How did the Seedfast team come together, what made you decide to build a startup together, and how did you land on database seeding as the problem worth solving?
Seedfast is built by Argon Studio. We started as a team of engineers four years ago at university. At first, we just participated in and won hackathons, worked on small commercial projects, and in 2024 we moved into the start-up world with our previous start-up, meetera, with which we’ve participated in such accelerators, as UVP Startup Accelerator(recognised as one of the three best start-ups based on the results of the 19th batch, ‘about us’ link: https://startupcentrum.sk/project/meetera/), ICKK Sweet&Salty and others.

What are your main goals for the Danube Digital Accelerator, and where do you most want support right now?
Our #1 goal is distribution and early customer acquisition - getting Seedfast in front of the right teams, converting interest into real usage, and building a repeatable pipeline.

Where we’d love the most support:

1. Practical go-to-market help: positioning, messaging, acquisition channels, and conversion loops.
2. Introductions to teams who feel the pain daily (engineering leaders, QA leads, platform teams) and can become early champions.
3. Feedback on how to package Seedfast for broader adoption beyond “early adopters,” without losing the developer-first simplicity.‍

‍Looking ahead, what can we expect next from Seedfast?
Three big directions:

1. Maximally flexible domain-driven seeding - so even the messiest, most complex real-world domains can be seeded in a controlled, realistic way (not just generic data).
2. A web version for teams where non-developers also need seeded environments (QA, PMs, demos, support) - without asking them to touch CLI workflows.
3. Broader database support beyond today’s focus, including exploring expansion into other DB engines and potentially NoSQL where it makes sense.

The long-term theme is the same: keep seeding “one command simple,” while making the output more domain-aware, more controllable, and usable by more roles across a team.

‍Thank you, Seedfast team, for taking part in the Danube Digital Accelerator. We appreciate your time and the insights you shared, and we are excited to see what you build next. Wishing you continued growth, strong partnerships, and many new customers ahead.

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