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AWS Commercial and Professional Services Australia

Lawpath

Customer Service Automation · Document Processing

“We wanted to give our customers self-service capabilities through automation, so we could provide faster and more relevant legal help while reducing our own administrative burden,” says Sumith. Lawpath , an Australia-based online legal service born on Amazon Web Services (AWS), introduced Lawpath AI to automate legal tasks and enhance the customer experience by leveraging generative AI.

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Lawpath was born on Amazon Web Services (AWS) in 2014 and has taken a cloud-native approach to technology ever since.
This efficiency helped us to create 15 percent more documents than previously," comments Sumith.
This innovation led to a 25% reduction in the volume of questions directed to our customer service agents.
days to half a day reduced lead time for submitting customer quotes 3

Reported impact

Business outcome

By developing Lawpath AI with Anthropic Claude on Amazon Bedrock, Lawpath improved self-service options for its customers, cutting down on time-consuming tasks like document discovery. With AI efficiently generating legal summaries, Lawpath can focus less on meticulously reviewing documents and more on building new solutions.“ After we introduced the Lawpath AI Ask feature with Amazon Bedrock, customers began asking the AI engine about their document needs and received automated responses," says Sumith. Key reported metrics include reduction in customer service inquiries through automation 25%; more documents created 15%; days to half a day reduced lead time for submitting customer quotes 3.

  • Productivity Improvement
  • Revenue Growth

Solution

Lawpath Reduces Customer Service Inquiries by 25% with Self-Service AI Platform on Amazon Bedrock Lawpath , an Australia-based online legal service born on Amazon Web Services (AWS), introduced Lawpath AI to automate legal tasks and enhance the customer experience by leveraging generative AI. Lawpath AI was developed using Anthropic Claude on Amazon Bedrock , incorporating Amazon EventBridge to trigger AWS Lambda workloads. With Lawpath AI on AWS offering a range of self-service legal solutions, Lawpath has reduced customer service inquiries by 25 percent, cut customer quote lead time from 3 days to half a day, and is now creating 15 percent more legal documents. Lawpath was born on Amazon Web Services (AWS) in 2014 and has taken a cloud-native approach to technology ever since.

Data flow

Source data enters the Lawpath workflow through its application services, stored or indexed in Amazon OpenSearch Service, Amazon DynamoDB, processed with Amazon Bedrock, Anthropic Claude on Amazon Bedrock, Claude on Amazon Bedrock, coordinated by Amazon EventBridge.

Solution architecture

8 components · 3 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
    • Anthropic Claude on Amazon Bedrock Serves as the large language model delivered through Amazon Bedrock.
    • Claude on Amazon Bedrock Serves as the large language model delivered through Amazon Bedrock.
    • Anthropic Claude Serves as the primary large language model for generation or analysis tasks.
    • AWS Lambda Runs event-driven compute for real-time processing without server management.
  2. Storage
    • Amazon OpenSearch Service Stores searchable indexes or vectors and serves low-latency search and analytics queries.
    • Amazon DynamoDB Stores operational application data with low-latency access.
  3. Orchestration
    • Amazon EventBridge Routes application events that trigger downstream workflows.

Architecture clues

  • Today, the platform is supported by serverless technologies including Amazon DynamoDB , and Amazon OpenSearch Service “We already trust AWS as the foundation for our business, and we wanted to explore generative AI on AW

Evidence from the source

By developing Lawpath AI with Anthropic Claude on Amazon Bedrock, Lawpath improved self-service options for its customers, cutting down on time-consuming tasks like document discovery.
Lawpath was born on Amazon Web Services (AWS) in 2014 and has taken a cloud-native approach to technology ever since.
This efficiency helped us to create 15 percent more documents than previously," comments Sumith.
This innovation led to a 25% reduction in the volume of questions directed to our customer service agents.
Today, the platform is supported by serverless technologies including Amazon DynamoDB , and Amazon OpenSearch Service “We already trust AWS as the foundation for our business, and we wanted to explore generative AI on AWS as the next phase
“We wanted to give our customers self-service capabilities through automation, so we could provide faster and more relevant legal help while reducing our own administrative burden,” says Sumith. Lawpath , an Australia-based online legal ser
Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

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
    4 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.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Lawpath customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/lawpath-case-study/
    Source type
    Vendor-published customer story
    Published
    Jun 11, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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