AWS Software / SaaS Global

nnamu

Application Refactoring · IT Operations Automation

Founded in 2022 in Germany, nnamu built a proprietary artificial intelligence (AI) for strategic reasoning based on game theory. The company’s first AI agent is live and can autonomously define negotiation strategies and negotiate with suppliers on behalf of procurement organizations.

30 percent
30% development time savings
33 percent
33% acceptance rate of code generated by Amazon Q Developer

Reported impact

Business outcome

With a modern foundation in place, nnamu will integrate Amazon Q and generative AI even further into its development process and products. The startup intends to use generative AI across the entire product lifecycle, from creating rapid prototypes for user testing to maintaining and enhancing existing code bases.

  • Cost Reduction

Solution

Founded in 2022 in Germany, nnamu built a proprietary artificial intelligence (AI) for strategic reasoning based on game theory. The company’s first AI agent is live and can autonomously define negotiation strategies and negotiate with suppliers on behalf of procurement organizations. To streamline the modernization project, nnamu implemented Amazon Q into every stage of the development lifecycle. The startup provided each team member with a subscription to Amazon Q Developer, a generative AI–powered assistant for software development that generates code suggestions in near real time. Using this tool, developers can quickly identify areas to optimize code, improving quality and consistency. Product owners create detailed prompts describing the requirements and functionalities of new features, systems, or products.

Data flow

Data is stored in Amazon DynamoDB and processed by AWS Lambda.

Solution architecture

4 components · 3 layers
  1. Compute
    • AWS Lambda Runs event-driven application logic and serverless processing tasks.
  2. Storage
    • Amazon DynamoDB Stores application state and metadata with low-latency access.
  3. Serving
    • Amazon Q Developer Assists engineers with code generation, troubleshooting, and modernization tasks.
    • Amazon Q Business Indexes enterprise content and answers employee questions across internal systems.

Architecture clues

  • AWS Lambda , a serverless, event-driven compute service, and Amazon DynamoDB , a NoSQL, fully managed database.
  • Employees can quickly find answers to questions such as “Why did we choose this architecture?” or “What is the policy for this specific area?” The chatbot provides a centralized so
  • These serverless capabilities, combined with Amazon Q, empower nnamu to continually scale its product, maintain high code quality, and rapidly deliver new features to its customers
  • This tool integrates with nnamu’s project management software and ingests technical documentation, human resources policies, and project management spaces into a natural language c
  • To align with these requirements, nnamu chose to transform its legacy application into a serverless architecture on Amazon Web Services (AWS).

Evidence from the source

Founded in 2022 in Germany, nnamu built a proprietary artificial intelligence (AI) for strategic reasoning based on game theory.
The company’s first AI agent is live and can autonomously define negotiation strategies and negotiate with suppliers on behalf of procurement organizations.
The startup previously powered these capabilities with a legacy monolithic application, which was challenging to scale and maintain.
The startup provided each team member with a subscription to Amazon Q Developer, a generative AI–powered assistant for software development that generates code suggestions in near real time.
To streamline the modernization project, nnamu implemented Amazon Q into every stage of the development lifecycle.
nnamu is an AI startup that specializes in strategic reasoning for businesses, with a focus on supply chain and procurement negotiations.
Transparency Evidence & methodology 91/100 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 value91/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 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

    nnamu customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/nnamu/
    Source type
    Vendor-published customer story
    Published
    Oct 25, 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.