Normalized claim
Environment setup time reduction: 95% decrease
reduction of 95 percent
Policy in Practice accelerates AI innovation on AWS with SBS
Use case typeProductivity platformUpdated Jun 13, 2026
Policy in Practice, a UK social policy data analytics firm serving over 100 local authorities, transformed its annual AWS hackathon infrastructure by working with Sandbox Studio Software (SBS). By replacing manual sandbox provisioning with automated account templates and lifecycle controls, the company reduced hackathon environment setup from about 3 weeks to under 12 hours and doubled participant capacity. The new setup supported five distinct AI use cases with preconfigured Amazon SageMaker AI workspace access, Amazon Bedrock experimentation, and Amazon S3 demo data replication while enforcing spend tracking and governance controls.
- Organization
- Policy in Practice
- Industry
- Other
- Location
- United Kingdom
- Published
- June 2026
Reported outcomes
60-80 hours
senior engineer hours saved per eventTime & speed
−95%environment setup time reduction3 weeks to under 12 hoursenvironment setup time60-80 hourssenior engineer hours saved per event (upper bound)+100%participant capacity increase+80%new product deployment frequency increase
Strategic outcomes
Speed & agilityAutomated sandbox provisioning and teardownNew product / capabilityEnabled preconfigured AI experimentation environmentsScale & capacityDoubled hackathon participant capacityRisk & complianceImproved governance for temporary environments
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Environment setup time: 3 weeks to under 12 hours decrease
reduced its environment setup time from 3 weeks to under 12 hours
Normalized claim
Senior engineer hours saved per event: 60-80 hours decrease
saved 60–80 hours of senior engineer time per event
Normalized claim
Senior engineer hours saved per event (upper bound): 60-80 hours decrease
saved 60–80 hours of senior engineer time per event
Normalized claim
Participant capacity increase: 100% increase
doubling participant capacity
Normalized claim
New product deployment frequency increase: 80% increase
there has been an 80 percent increase in new product deployment frequency on AWS
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Policy in Practice
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Solutions Case Study
Sandbox Studio Software was deployed within Policy in Practice's AWS Organization as an orchestration layer for standardized sandbox provisioning
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Sandbox provisioning automation
- 2Developer productivity
- 3AI prototyping platform
- Manual provisioning of secure temporary AWS sandbox environments took around 3 weeks.
- The process required heavy senior engineer effort and limited hackathon participant capacity.
- Policy in Practice needed stronger financial, security, and compliance controls for temporary environments.
- Sandbox Studio Software was deployed within Policy in Practice's AWS Organization as an orchestration layer for standardized sandbox provisioning.
- The solution automated template-based AWS sandbox account creation and decommissioning for five AI use cases.
- Teams received preconfigured Amazon SageMaker AI access, Amazon Bedrock access for experimentation, and Amazon S3 bucket replication for safe demo data.
- Environment setup time fell from around 3 weeks to under 12 hours.
- Future preparation is expected to take less than 3 hours, about a 95% reduction.
- The initiative saved 60 to 80 senior engineer hours per event.
- Participant capacity doubled, four AI prototypes were built, and two moved to production.
- The company reported an 80% increase in new product deployment frequency on AWS.
Architecture
Sandbox Studio Software was deployed inside Policy in Practice's AWS Organization to automate secure, template-based sandbox account provisioning and lifecycle management. The environment included preconfigured Amazon SageMaker AI workspaces, Amazon Bedrock experimentation access, Amazon S3 demo data replication, shared sandbox account features, and spend tracking with $500 budget allocations.
Implementation partners1
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 8, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
Market context
Browse the catalog
Was this useful?
Community
Comments
Loading comments...