Use Case Intelligence RSS Search use cases Search
AWS Nonprofit Rwanda

Tehanu

In 2024, Tehanu sought to launch a project focusing on a family of mountain gorillas in Rwanda’s Volcanoes National Park, on the border with Uganda and the Democratic Republic of Congo. With the objective to automate the aggregation of academic studies, such as the gorillas’ behavioral preferences, Tehanu hoped to enable informed policymaking that would support biodiversity.

Read the original story Explore all use cases
$1.55B
110x
Covering 440 mountain gorillas, Anthropic’s Claude integrated diverse datasets—including census reports, genetics, health, diet, social behavior, and movements.
The estimated financial value of Rwandan mountain gorillas now stands at $1.55 billion, equivalent to nearly 10% of Rwanda’s annual Gross Domestic Product (GDP).

Reported impact

Business outcome

Pioneering Species-Centric AI for Wildlife Preservation Leveraging Adastra’s AI expertise and the comprehensive cloud capabilities of AWS, the project revealed new ways technology can enhance our understanding of Earth’s biodiversity and support climate change mitigation efforts. Using Anthropic’s Claude LLMs, the model provided insights as accurate as human experts, but with far greater detail and actionable recommendations. Key reported metrics include estimated financial value of Rwandan mountain gorillas $1.55B; $1.55B; accuracy in gorilla face recognition using ranger observations, camera traps, and crowdsourced imagery 93%.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

AWS and Tehanu Use Generative AI to Give Wildlife a Voice in Global Conservation The Tehanu project in Rwanda’s Volcanoes National Park has demonstrated groundbreaking use of generative AI to infer and act on the interests of mountain gorillas. Leveraging technology solutions from Amazon Web Services (AWS), AWS Partner Anthropic , and with the support of Adastra , Tehanu created an automated pipeline to process behavioral data of gorillas, enabling the first-ever digital financial transactions by a non-human species. The AI solution synthesized vast academic and observational data, aligning conservation actions with species-specific preferences while supporting biodiversity efforts. This scalable, innovative approach sets a precedent for using AI to foster coexistence across species worldwide. Source data enters the Tehanu workflow through its application services, stored or indexed in Amazon S3, processed with Amazon Bedrock, AWS Lambda, Claude 3.

Data flow

Source data enters the Tehanu workflow through its application services, stored or indexed in Amazon S3, processed with Amazon Bedrock, AWS Lambda, Claude 3.

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
    • AWS Lambda Runs event-driven compute for real-time processing without server management.
    • Claude 3 Serves as the primary large language model for generation or analysis tasks.
  2. Storage
    • Amazon S3 Stores raw, intermediate, or curated files and datasets.
  3. Governance
    • Amazon CloudWatch Collects operational telemetry and monitoring signals from the workload.

Architecture clues

  • Adastra helped synthesize data from hundreds of academic papers, and analyzed them using a structured pipeline developed by using Amazon Bedrock —the easiest way to build and scale generative AI applications with foundat
  • Adastra implemented AWS services, including Amazon Bedrock, Amazon Simple Storage Service (Amzon S3), and AWS Lambda to support the seamless storage and serverless execution of generative AI features.
  • Leveraging technology solutions from Amazon Web Services (AWS), AWS Partner Anthropic , and with the support of Adastra , Tehanu created an automated pipeline to process behavioral data of gorillas, enabling the first-ev
  • These resources enabled Tehanu to manage databases for the Internet of Species , infer species interests, and maintain application programming interfaces (APIs) for digital monitoring, reporting, and verification (MRV).
  • This pipeline utilized Large Language Models (LLMs) to extract, validate, and categorize gorilla preferences.
  • This unique approach advanced data mining, developed an API for preference identification, and established a foundation for broader applications.

Evidence from the source

110x
Covering 440 mountain gorillas, Anthropic’s Claude integrated diverse datasets—including census reports, genetics, health, diet, social behavior, and movements.
In 2024, Tehanu sought to launch a project focusing on a family of mountain gorillas in Rwanda’s Volcanoes National Park, on the border with Uganda and the Democratic Republic of Congo. With the objective to automate the aggregation of acad
Scalable AI Solutions on AWS Support a New Era of Wildlife Conservation The project’s success relied heavily on the scalability and efficiency of the AWS Global Infrastructure , which formed the backbone of a solution capable of processing
The estimated financial value of Rwandan mountain gorillas now stands at $1.55 billion, equivalent to nearly 10% of Rwanda’s annual Gross Domestic Product (GDP).
With comparable results, generative AI was 110 times faster, highlighting its efficiency in analyzing data and comparing methods.
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.

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
    7 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.
02

Traceability

Sources & provenance

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

  1. AWS

    Tehanu customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/tehanu-adastra-anthropic/
    Source type
    Vendor-published customer story
    Published
    Feb 20, 2025
    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
  2. LinkedIn

    Tehanu | LinkedIn

    Independent secondary
    https://www.linkedin.com/company/tehanu/

    numberOfEmployees":{"value":1,"@type":"QuantitativeValue"}

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

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

Related

Similar use cases

See all like this
AWS Jul 6, 2026 World Rugby Nonprofit global World Rugby wanted to make the Women’s Rugby World Cup 2025 more engaging by unlocking and activating complex match data in real time so broadcasters, commentators, and digital channels could give fans richer insights… 1,521 insights that were used across 147 million viewing hours Real-time Analytics Amazon Bedrock · Opta match data · TryZone IQ AWS Apr 4, 2024 Instituto de Ingeniería del Conocimiento Nonprofit Spain For nearly 20 years, Instituto de Ingenieria del Conocimiento (IIC) has provided renewable energy prediction services to its clients. The research and development center specializes in artificial intelligence (AI) and… 20 million Real-time Analytics AWS Glue · AWS Lambda · AWS Step Functions Anthropic Archived · Oct 4, 2026 International Rescue Committee Nonprofit Burkina Faso IRC teams needed to analyze frontline health data quickly enough to influence staffing, outreach, and supplies, but the information came from ministry-run facilities in inconsistent paper and Excel formats and often had… 4+ hours per month saved by half of surveyed pilot users Real-time Analytics Claude Enterprise