Misata: Outcome-Conformant Synthetic Data Generation

Muhammed Rasin

Synthetic Data for Contact Centers

Describe the operation, for example four queues, 120 agents and 30,000 calls a month with 6% abandonment, and Misata Studio designs the tables, shows them to you for review, and builds queues, agents, calls and half-hour intervals that follow a working day's rhythm, with your abandonment rate exact. For workforce-management testing where staffing is solved from service-level targets, use the free Erlang C sample below.

Updated 2026-09-24.

You would write

A contact center with 4 queues, 120 agents and 30,000 calls a month over one quarter, busiest on Monday mornings, with a 6% abandonment rate.

Studio designs the tables for your description and shows them to you before any data is made, so you can change them first.

Start from this

What Studio typically designs

queuesLines of business with their service targets

agentsPeople assigned to queues

callsTimestamped calls with wait, handle time and outcome

What holds, and how you know

These are checked on the finished data, not assumed. Each dataset comes with a certificate listing what you asked for and whether it was met. How we verify.

The abandonment rate and call volume you state are exact
Calls follow the day and week rhythm you describe
Every foreign key points at a row that exists, checked on the finished data
Every count, total, rate, share and date window you state is applied exactly, or listed as not applied
A certificate lists each requirement and whether the data meets it

Free sample dataset

4 queues and 2,400 intervals where agents_staffed is solved backwards from each queue's service-level target with Erlang C. Studio does not solve Erlang C for you; the sample has it.

See the sample

Have a schema, specialised rules or a large volume and would rather hand it off? Ask us to build it for you.

Frequently asked

Do I need real contact center data to generate this?

No. Misata Studio builds the dataset from a description, a schema you draw, or a structure you import (SQL DDL, DBML or a CSV header). No real records are uploaded, copied or learned from, so there is nothing to anonymise.

Is the generated contact center data privacy safe?

Yes. Nothing is learned from real records, so no real person, customer or account can appear in the output. A model writes names, places and wording, and Studio may research public facts on the web (you can turn research off). It never asks for your data.

Can I control the numbers, like rates and totals?

Yes. State a total, a monthly curve, a share or a rate in plain words, or draw the curve, and Studio applies it exactly. The certificate lists each figure and whether it was met. If a figure cannot be met, Studio says so instead of changing it.

Can I use my own schema?

Yes. Paste SQL DDL or DBML, import a CSV structure, or draw the tables on the canvas. A schema you give is followed exactly, and any difference is reported.

Which formats can I export?

CSV, Excel, Parquet, JSON Lines, SQL for Postgres, MySQL, SQL Server, Oracle, BigQuery and Snowflake, SQLite, DuckDB, dbt seeds, Prisma, DBML, TypeScript, JSON Schema, a data dictionary and more.

Does Studio have a ready-made contact center model?

Not a fixed one. Studio designs the tables for your contact center description, shows them to you for review before any data is made, and then builds and checks the data. What it guarantees for every run holds here too: matching keys, exact stated numbers, ordered dates and a certificate. Domain formulas you need beyond that should be stated in the description, and the certificate shows whether each was met.

Choosing a tool? How Misata compares

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