AWS Software / SaaS United States

Observe.AI

Customer Service Automation

Our ML engineers and scientists faced challenges in accurately predicting this capability when transitioning models from research to production." The company sought to deploy a larger ML model in production for enhanced accuracy. Achieving an optimal return on investment through fine-tuning its infrastructure was key, and the business wanted a solution compatible with its existing Amazon Web Services (AWS) environment.

Read the original story Explore all use cases

10x

Launched in 2022, OLAF by Observe.AI is now actively employed by dozens of ML engineers and researchers for testing and predicting data loads.

Using OLAF and AWS, we can replicate and precisely increase the load by 100 percent, anticipating potential breakpoints or database issues.

higher data loads supported 10x

Reported impact

Business outcome

Launched in 2022, OLAF by Observe.AI is now actively employed by dozens of ML engineers and researchers for testing and predicting data loads. By using OLAF, Observe.AI has cut LLM costs by conducting load tests on Amazon SageMaker instances, identifying the most suitable configuration aligned with the company’s business metrics. Key reported metrics include lower costs by fine-tuning instance sizes 50%; higher data loads supported 10x; 10x.

Solution

Observe.AI developed and open-sourced the One Load Audit Framework on AWS to optimize machine learning model costs, boost developer efficiency, and scale to meet data growth. Observe.AI uses conversation intelligence to uncover insights from live and post-customer interactions, helping companies increase contact center agent performance. The company developed and open-sourced the One Load Audit Framework (OLAF), which integrates with Amazon SageMaker to automatically find bottlenecks and performance problems in machine learning services. Using OLAF to load-test Amazon SageMaker instances, Observe.AI reduced machine learning costs by over 50 percent, lowered development time from one week to hours, and facilitated on-demand scaling to support a tenfold growth in data load size. Source data enters the Observe.AI workflow through its application services, processed with Amazon SageMaker, coordinated by Amazon SNS, Amazon SQS.

Data flow

Source data enters the Observe.AI workflow through its application services, processed with Amazon SageMaker, coordinated by Amazon SNS, Amazon SQS.

Solution architecture

3 components · 2 layers

Compute
- Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.
Orchestration
- Amazon SNS Fans out notifications and event signals to downstream systems.
- Amazon SQS Buffers asynchronous jobs between distributed services.

Architecture clues

Aashraya explains, "Our research team encountered higher costs than anticipated when deploying an LLM, as well as other ML models, with specific latency and throughput requirements into production.
Integrated with , a service that builds, trains, and deploys ML models for any use case, OLAF identifies bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static a
Next, the company incorporated Amazon Simple Queue Service (Amazon SQS), a fully managed message queuing service for microservices, distributed systems, and serverless applications.
They simply input their AWS credentials and the Amazon SageMaker endpoint, and the tool conducts load testing, providing latency numbers and expected errors for a particular model or instance." Following the initial buil
This process ensured the best return on investment." Previously, Observe.AI developers had to write multiple scripts and construct numerous pipeline workflows, resulting in a complex array of onboarding data transfers an

Evidence from the source

10x

Integrated with , a service that builds, trains, and deploys ML models for any use case, OLAF identifies bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data load

Launched in 2022, OLAF by Observe.AI is now actively employed by dozens of ML engineers and researchers for testing and predicting data loads.

Our ML engineers and scientists faced challenges in accurately predicting this capability when transitioning models from research to production." The company sought to deploy a larger ML model in production for enhanced accuracy. Achieving

To address its challenge of predicting ML load sizes, Observe.AI created and open-sourced the (OLAF).

Using OLAF and AWS, we can replicate and precisely increase the load by 100 percent, anticipating potential breakpoints or database issues.

Transparency Evidence & methodology 95/100 reference value · 90% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

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
5 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence90%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability23/25
11 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 transparency9/10
2 source references document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

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

AWS
Observe.AI customer story

First-party
https://aws.amazon.com/solutions/case-studies/observe-ai-case-study

Source type
Vendor-published customer story

Published
Jan 1, 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
Observe.AI
Discover the Future of Agentic Customer Experience - Observe.AI

First-party
https://www.observe.ai/about-us
The official about-us page footer lists '275 Shoreline Drive Suite #450, Redwood City, CA,' supporting a United States headquarters.

Source type
Official company website
Supports
- Region

Date evidence

Publisher catalog date Jan 1, 2024 · AWS · First-party
Open date source

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

Similar use cases

See all like this

AWS Jan 16, 2025 Ada Support Inc Software / SaaS Canada After weighing the options, it chose AWS Global Accelerator —a networking service that organizations use to improve the availability, performance, and security of public applications—because Ada’s engineers could… Ada’s benchmark automated resolution rates increased from 20–30 percent with declarative agents before the… Application RefactoringCustomer Service Automation AWS Global Accelerator · AWS Graviton processors · Amazon EC2

AWS Jul 31, 2024 Enterprise Bot Software / SaaS Switzerland With the opportunity to expand into new markets but with a need for better performance and flexibility, Enterprise Bot , an enterprise generative artificial intelligence (AI) company, migrated from a previous cloud… Achieving 10.5 Percent Higher AI Accuracy on AWS than on Competing Providers Enterprise Bot uses cutting-edge… Customer Service Automation Amazon Bedrock

AWS Mar 23, 2024 HappyFox Software / SaaS United States HappyFox needed a more accurate and reliable LLM stack because its prior external AI service struggled with factual grounding, multi-turn context retention, and response latency in customer support workflows. 30% Customer Service AutomationEnterprise Knowledge Search AI Agent Copilot · Amazon Bedrock · Anthropic's Claude 2.1