FlowData Studio
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Data infrastructure for frontier AI

Human expertise.Model-ready data.

We turn what practicing professionals know into high-quality data for training and evaluating AI models.

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60+
Professional domains
110k+
Vetted domain experts
5days
Typical time to first pilot batch
95%
Client first-round acceptance rateAcross 200+ projects, 2026
Core offering · Expert skills data

Tacit knowledge, made model-ready.

Practicing professionals show how they reason. We turn it into tasks, reference answers and rubrics your models can learn from.

Best for: reasoning models, SFT, RLHF and evals from domain experts
Sample record · FinanceIllustrative example
TaskIs an 11.0× exit multiple defensible as a base case, given the deal and peer data provided?
Expert answer · excerptNot as a base case. All five listed peers trade below 11.0×, so I'd underwrite exit at 9.0–9.5× and treat 11.0× as upside.
Rubric5 criteria · 10 pts
View full sample
Material provided
Entry
9.5× EBITDA
Leverage
5.5× net debt / EBITDA
Model exit
11.0× in year 5
Listed peers
5 companies, 8.2–10.1× (median 9.0×)
Full expert answerNot as a base case. All five peers trade below 11.0× (median 9.0×), so the model assumes this company re-rates above every comparable. That could be justified by faster growth, better margins or a scarcity premium, but none of that is in the material. I'd underwrite exit at 9.0–9.5×, in line with the peer median, and show 11.0× as an upside case. At 5.5× leverage, a lower exit multiple also leaves less room for error at refinancing.
Rubric · written by the expert
CriterionPoints
Tests the exit multiple against the peer range provided3
States a clear base-case view with reasoning; a different conclusion earns full credit if the evidence supports it3
Names what evidence would justify a higher multiple2
Connects leverage to the risk of a lower exit multiple1
Uses only the figures provided; no unsupported numbers1
Total10
Format: JSONLFields: task, material, answer, rubric, metadata
Also from the same expert network
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RGBDepth frame 120 · 1280×720 · synced camera pose
World model data

Game-engine worlds,
captured frame by frame.

RGB + depthCamera posePlayer actionsMulti-view
Top-down view with trajectory
Top-down trajectory
Depth map, forest scene
Depth · open world
Egocentric frame with 21-point hand keypointsPer-frame depth map, same moment3D hand mesh overlay
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3D hands · depth · 49 subtasks
Egocentric · robotics

Physical AI data

Desktop app, raw captureSame screen with every clickable element boxed by typeSecond app, labeled
Raw screen
GUI agents

Computer-use data

Generated building, main viewAerial viewStreet-corner viewFacade detail
4 views
Blender · rubric-graded

3D & spatial data

Residential unit plan with dimensionsFloor plan, one elevator and two units
Unit plan
Architecture

Expert skills data

Selected work

Who we build for.

Large tech platform · Consumer AI
DeliveredExpert skills and multimodal data
Supplied multiple model teams in parallel
Generative video lab
DeliveredHigh-fidelity video datasets
Built with working production creative talent
AI startup · Foundation models
DeliveredDomain expert data pipelines
Specialized fine-tuning data for regulated verticals

Beyond collection.Expertise made learnable.

The knowledge that separates a good model from a great one lives in the judgment of people who've spent years getting good at something. Our job is to turn that judgment into structured, verifiable signal.

Full lifecycle — we own every step
01
Source
Expert & professional recruitment
02
Capture
Structured elicitation & collection
03
Translate
AI-readable format conversion
04
Verify
Human QA & consistency checks
05
Deliver
Model-ready, documented output
Why FlowData

What you get by working with us.

01

Practicing domain experts

The work is done by working professionals with verifiable credentials, such as licensed clinicians, practicing lawyers and buy-side analysts, not general annotators.

02

Built around your training goals

We shape tasks, reference answers, and evaluation criteria around your model’s target capabilities and intended use cases.

03

Structured, verifiable data

Every batch goes through human QA and consistency checks, and ships with documentation in the format your pipeline expects: tasks, reference answers, rubrics and metadata.

Tell us your model.
We'll build the data.

From scoping to first delivery in weeks, not months.

Book a 20-minute scoping call

Tell us your training objective. We'll discuss scope, data requirements, and next steps.

Get the capabilities deck

Domains, sample formats, QA process and turnaround times, in one PDF.