Built from businesses we observe
Synthetic data.
Grounded in
real business.
Built from direct observation of real businesses: their workflows, decisions, handovers and exceptions. We turn that operational understanding into generated data and simulated environments for AI training and evaluation.
FOR MODEL LABS / AGENT TEAMS / DATA BUYERS
Aster Financial
Sample organisation / entirely fictional| Field | Value |
|---|---|
| customer_id | CUS-208 |
| requested_amount | 125000 |
| currency | AUD |
| status | awaiting_credit_review |
FROM REAL OPERATIONS TO GENERATED DATA.
01 / Observe the business02 / Model how it works03 / Generate for AI01 — Built from observed businesses
The detail comes from being there.
Our access to real businesses lets us see how work moves between people, systems and teams. The handovers, dependencies and exceptions we observe become the foundation of what we generate.
Explore the example ↗Download sample JSON Entirely fictional records created for this website prototype. Not a validated training dataset.
- 01OBSERVE
Observe real operations.
Understand workflows, decisions and exceptions in the businesses we have access to.
- 02MODEL
Model how the business works.
Translate those observations into connected roles, records, policies and changing business state.
- 03GENERATE
Generate data and environments.
Create new records, tasks and scenarios that reflect those operating patterns.
- 04DOCUMENT
Make the grounding inspectable.
Document what was observed, what was modelled and what was generated in each scoped release.
Data, tasks or a world
your agents can work in.
The same observed business logic, delivered in the form your team needs for training or evaluation. Start with a scoped sample.
Generated datasets
Generated records and workflow traces shaped by observed business relationships, decisions and dependencies.
Task & evaluation packs
Tasks grounded in observed business decisions, with relevant policies, starting conditions and assessment criteria.
Simulated environments
Observed operating patterns translated into stateful organisations, with tools, dependencies and consequences.
03 — Evidence before scale
Trace the grounding.Inspect the output.
A useful sample should show how observed operations become modelled workflows and generated scenarios, with clear boundaries between each.
Explore the methodology ↗01 / FOUNDATION
Observed operations
Understand which real operating patterns inform the release.
02 / BUSINESS LOGIC
Connected behaviour
Trace the dependencies, policy checks and consequences.
03 / ASSESSMENT
Explicit outcomes
Inspect task success criteria, checks and release packaging.
04 / PROVENANCE
Clear usage rights
Review generation methods, permitted uses and licence scope.
FAQ
Questions buyers ask
What does MirrorFirm provide?
Generated datasets, task and evaluation packs, and simulated environments for AI training and evaluation. Each is grounded in how real businesses operate: their workflows, decisions, handovers and exceptions.
Is the data real?
No. Everything we deliver is generated. What we learn from observing businesses shapes how roles, records, policies and changing business state are modelled; the records themselves are synthetic.
How can I evaluate it before buying?
Explore the Data explorer, download the sample pack, or request a sample. We'll scope a sample around your training or evaluation objective.
What does a pilot involve?
You bring an objective. We agree the business world, the deliverable and the evaluation criteria together, then deliver a scoped pilot under a written licence.
What can I use the data for?
Licences are matched to your intended use, such as model training, evaluation or, where specifically agreed, resale. The scope is set out in a written agreement.
How do you handle privacy in generated data?
Each release documents what was observed, what was modelled and what was generated, and includes a source-similarity and disclosure-risk review. Generating synthetic data does not automatically guarantee anonymity, so we check.
Bring a training objective.
We’ll scope the business world.
Start with observed operations, a defined objective and a sample you can inspect.