Every project starts with a problem to solve

Mindrift’s mission is to empower businesses with high-quality data. Therefore, most projects begin with a partnership aimed at improving a client’s model. The end goal might be to make the model more accurate, more aligned, or more reliable. 

The team at Mindrift works closely with the client to define what kind of data will actually improve performance. And that’s where we often see a big misconception: that projects are all about “producing volume” and that more data equals better results. 

“In reality, research and industry experience consistently show that data quality, clarity of labels, and alignment with the model objective matter far more than raw quantity. Poorly defined criteria or inconsistent annotations can actually harm model performance, even at large scale,” explains Aleksandra.

That means that projects are not just pipelines for generating data. Think of them more as structured experiments designed to produce the right data — with a lot of work happening behind the scenes.

Turning an idea into a working system

To better explain the process, Aleksandra broke it down into six clear steps — each one with its own checks and balances to ensure the process runs smoothly. 

1. Discovery: The client reaches out with a request. We evaluate volumes, timelines, project complexity, and the resources and expertise required.

2. Scoping: We translate the request into a clear scope, looking at what “success” means, deliverables, constraints, edge cases, acceptance criteria, and key risks. At this stage, we estimate how many experts we’ll need and check availability. If you hear from us, it’s part of that planning process — not necessarily a guaranteed invitation to join. 

3. Solution design: Based on everything we know, we design the project architecture by defining how the data will flow and what automations and quality checks are needed to ensure high-quality output. For example: Generation → QA → Fixes/Rework → Final QA → Delivery.

4. Building: Once the client approves the setup, we build the project infrastructure, including guidelines, training materials, and onboarding. We train experts to succeed on the project and may run a pilot to validate that the setup produces consistent data aligned with the client’s goals.

5. Delivery: We deliver data in batches. The client trains the model, measures performance, and decides on next steps. At this point, they may decide to scale, adjust the approach, or conclude the project based on training value.

6. Close out: After all batches are delivered and accepted, we conduct an internal retrospective to capture lessons learned and share best practices across teams.

The process might seem seamless, but projects sometimes stall, get put on hold, or are scrapped altogether. That’s a normal part of the industry and can be due to several factors:

• The client’s internal priorities may have shifted.

• The client used the data and reached their goals, meaning the model improved and it’s time to move on.

• The data didn’t generate training value. We may have tested a hypothesis and it didn’t deliver the expected impact.

Sometimes, projects experience delays right before launching and that’s often due to quality — a critical factor in ensuring client satisfaction.

What happens before you ever see a task

Once a project is approved, a lot of work goes into preparing it for AI Trainers. The client’s guidelines, quality criteria, and key insights from discussions are transformed into easy-to-understand training documentation designed to guide experts through the project in the clearest, more efficient way possible. Here’s an idea of what usually happens behind the scenes:

• Internal team members complete the tasks themselves to identify any edge cases and ambiguity.

• Pipelines and user interfaces are designed to ensure that completing a task is a convenient and clear process.

• Guidelines with specific steps and expectations are created and edited for clarity. 

• Onboarding flows are built to explain the main guideline points and allow experts to train on practical tasks.

• Quality checks, safeguards, and thresholds are refined. 

• A pilot project might be launched to make sure everything works perfectly.

• Communication channels, like Discord, are created for experts to ask questions and get support.

The process is actually pretty complex, explains Aleksandra.

Why the right experts matter

Some projects require specific languages, regions, or deep domain expertise. Others demand specialized skills like technical writing, coding, or structured data generation.

“Imagine a project where we need to create a CRM database filled with purely generated data about employers, clients, and contracts. We would need a CRM or RevOps data specialist who understands business logic and can generate realistic, internally consistent synthetic B2B data, ensuring proper relationships between entities and logical consistency across fields,” says Aleksandra.

But across nearly all projects, some core traits make a big difference: 

• Consistent high-quality contributions

• Reading updates carefully

• Asking questions and raising edge cases

• Contributing to improvement, not just task completion

“Genuinely wanting the project to succeed — not just completing tasks, but contributing to overall quality and improvement — that’s important,” explains Aleksandra. And feedback from past projects? That’s critical.