I want to experiment safely
Use an isolated environment with controlled model access, data boundaries, logging, guidance and usage limits.
Safe, measurable AI experimentation
Test whether an AI application works before it becomes a production risk.
The AI Sandbox creates a separated environment for one defined application. Test data, model access, costs, quality and human review are controlled so you can decide to stop, adjust, extend or implement with evidence.
- Defined hypothesis and success criteria
- Separated test environment
- Working prototype and evaluation set
- Evidence-based production decision
Two routes
Experiment yourself or let Synbuild investigate and build
I want Synbuild to investigate and build
We define the use case, prepare data, build the prototype, create an evaluation set and assess technical feasibility.
Example experiments
Three applications that benefit from controlled testing
These are proposed architectures and intended results. A sandbox demonstrates what actually works with your constraints and data.
01
AI Sandbox
Technical knowledge assistant
Test referenced answers across technical sources with access controls and an explicit fallback when evidence is insufficient.
View use case → 02AI Sandbox
Summarise long documents controllably
Test structured summaries of decisions, obligations and differences with links to supporting passages.
View use case → 03AI Sandbox
Turn conversations into actions
Test proposed actions, owners and deadlines from meeting transcripts before anything is registered or sent.
View use case →Protected by design
What the sandbox separates and controls
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01
Business data and personal data
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02
Model and user access
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Network connections and external APIs
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04
Secrets and credentials
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Experiment logs
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Usage and budget
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Test behavior from production processes
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Deletion and retention
What you provide
A focused question and representative material
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The task or decision the experiment should support
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Representative, permitted test data
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Current process and baseline examples
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Known risks, policies and access restrictions
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People who can judge output quality
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Systems that may be integrated after validation
What you receive
A testable prototype and a clear decision
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Defined test hypothesis
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Selected use case
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Relevant data-source inventory
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Secured test environment
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Working prototype
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Tested model configurations
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Evaluation set
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Baseline of the current process
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Quality and reliability scores
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Logging and human-control design
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Production architecture sketch
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Decision to stop, adjust, test longer, extend or implement
Guardrails
Experiment without quietly creating a production system
Least privilege
People, models and services receive only the access needed for the test.
Separated and masked data
Use representative test data and minimise or mask personal and sensitive information.
No automatic external action
The sandbox does not send messages, change records or make production decisions without approval.
Observable limits
Logging, budgets, usage limits and deletion procedures make the experiment manageable.
Approach
From hypothesis to production decision
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Step 1
Define
Set the task, boundaries, baseline, evaluation examples and stopping criteria.
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Step 2
Build and test
Prepare the separated environment, prototype and repeatable quality evaluation.
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Step 3
Decide
Assess quality, risk, operating cost and the architecture required for responsible production use.
Reliability and limitations
A successful demo is not yet a reliable production system
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Model output can remain incomplete, inconsistent or wrong.
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Quality must be measured against representative examples.
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Sensitive data should be minimised and governed throughout the test.
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Production access, monitoring, support and incident handling require a separate design.
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Stopping without further investment is a valid sandbox outcome.
Frequently asked questions
AI Sandbox questions
Do we need to upload all our data?
No. Start with the smallest representative and permitted dataset that can test the hypothesis. Sensitive information can often be removed, masked or replaced.
Is the prototype production-ready afterwards?
Not automatically. The sandbox identifies what works and which architecture, controls, integrations and management are still required for production.
Can we use our preferred model?
Usually several model or configuration options can be compared, provided they fit the data, security and budget boundaries.
What happens if the test fails?
That is useful evidence. The decision may be to adjust the task, improve data, choose deterministic automation or stop without a larger implementation.
Have one AI application that needs evidence before investment?
Describe the task, available test material and the risk you need to control. We will determine whether an AI Sandbox is the right next step.