AWSSoftware / SaaSDocument ProcessingAI and Machine LearningGenerative AI / LLMCost ReductionProductivity ImprovementRevenue GrowthSpeed to Market2025AWSAWS#ff9900Software / SaaSGlobalWhile digitalization had made significant progress, the company identified that innovative law firms required more sophisticated tools to optimize operations and enhance client service. Due to challenges with…Founded in 2012, Smokeball serves over 6,000 law firms with 25,000 daily users across Australia, the UK, and…Jan 6, 20256Document ProcessingAWS Lambda · Amazon Bedrock · Amazon EKS02025-01-066
While digitalization had made significant progress, the company identified that innovative law firms required more sophisticated tools to optimize operations and enhance client service. Due to challenges with application programming interface (API) limitations and lack of direct support, Smokeball decided to find a more robust solution.
↗Founded in 2012, Smokeball serves over 6,000 law firms with 25,000 daily users across Australia, the UK, and the US with innovative practice management solutions.
↗Launched in 2024, these tools automate traditionally manual tasks such as document processing, client intake, and time tracking.
↗This advancement cuts form creation and population time from one hour to just 5 minutes, significantly improving efficiency in client acquisition.
↗This saved between up to 3 hours each day on administrative tasks, boosting lawyer productivity.
Reported impact
Business outcome
With the help of the AWS team, Smokeball developed and launched its generative AI tools in public beta within just six months. The company's successful implementation of its tools using AWS has set the stage for continued innovation in legal technology. Key reported metrics include more billable hours captured, driving potential revenue growth Up to 30%; Up to 30%; reduction in time spent on administrative tasks like form/document creation and review Up to 3 hours daily.
Cost Reduction
Productivity Improvement
Revenue Growth
Speed to Market
Solution
Learn how the Sydney-based legal management software firm is driving profitability and enhancing efficiency for small and medium-sized law firms through AI technology on AWS. Founded in 2012, Smokeball serves over 6,000 law firms with 25,000 daily users across Australia, the UK, and the US with innovative practice management solutions. These firms often struggled with time-consuming administrative tasks that reduced billable hours and impacted client service. Recognizing this challenge, Smokeball developed Smokeball AI, a suite of generative artificial intelligence (AI) tools built on Amazon Web Services (AWS). Launched in 2024, these tools automate traditionally manual tasks such as document processing, client intake, and time tracking. By transforming hours of administrative work into minutes, lawyers are now freed to focus more time on billable work and client relationships.
Data flow
Source data enters the Smokeball workflow through its application services, processed with Amazon EKS, Amazon SageMaker, Amazon Bedrock, coordinated by Amazon SQS.
Solution architecture
5 components · 2 layers
Compute
Amazon EKSRuns containerized workloads and Kubernetes-based application services.
Amazon SageMakerSupports model training, fine-tuning, evaluation, or managed inference for the customer workload.
Amazon BedrockHosts the foundation models and inference APIs that power the generative AI features.
AWS LambdaRuns event-driven compute for real-time processing without server management.
Orchestration
Amazon SQSBuffers asynchronous jobs between distributed services.
Architecture clues
Identifying the need for scalability, Smokeball deployed Amazon Elastic Kubernetes Service (Amazon EKS), a fully managed container orchestration service.
Smokeball used Amazon Bedrock (Claude V3), a fully managed service offering access to high-performing foundation models through a single API, to equip Archie with natural language processing capabilities.
The company used AWS Lambda , a serverless compute service, to handle AI-powered responses without complex infrastructure management, streamlining operations.
Evidence from the source
Founded in 2012, Smokeball serves over 6,000 law firms with 25,000 daily users across Australia, the UK, and the US with innovative practice management solutions.
Launched in 2024, these tools automate traditionally manual tasks such as document processing, client intake, and time tracking.
This advancement cuts form creation and population time from one hour to just 5 minutes, significantly improving efficiency in client acquisition.
This saved between up to 3 hours each day on administrative tasks, boosting lawyer productivity.
Review how this reference was scored and trace each normalized claim back to its documented source.
01
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value95/100
Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth20/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
6 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
6 direct source quotes retained.
Date provenance8/10
The publication date comes from publisher-controlled metadata.
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