From AI Prototype to Production: What Are the Challenges?
Many companies see the potential of LLMs and experiment with Proof of Concepts (PoCs). During the testing phase, everything runs smoothly: The solution is optimized, infrastructure is stable, and the initial tests show promising results. But once the application goes live and users start interacting with it, new challenges arise:
- Unexpected output problems: What worked well in PoC tests suddenly performs less consistently in production.
- Model changes without warning: LLM providers like OpenAI release updates without changelogs or rollback options.
- Difficult debugging: When quality degrades, it's not immediately clear whether the issue lies in prompt engineering, the infrastructure, or the LLM itself.
The result? Teams are firefighting instead of optimizing, and customers experiencing frustration.
The Solution: Observability and Quality Control with LangWatch
LangWatch offers a powerful Observability solution for GenAI applications, helping companies and AI developers stay ahead of these challenges. Thanks to our partnership, our customers can benefit from:
- Real-time monitoring & debugging: Issues with output quality are detected and localized early.
- Automated evaluations: Critical use cases are continuously tested and optimized to keep AI performance sharp.
- Faster iteration & development: LangWatch helps teams automatically discover better prompts and few-shot examples, dramatically reducing optimization time.
- LLM experimentation: With LangWatch Optimization Studio, companies can easily experiment with the latest models (released weekly) and smoothly switch to better or more cost-efficient alternatives, while maintaining quality.
Compliance and AI Observability under the EU AI Act
With the upcoming EU AI Act, transparency and monitoring of AI systems will become a requirement, especially for companies using AI in critical applications. LangWatch is primarily a tool for developers to manage AI applications but also helps businesses meet the most crucial regulatory requirements:
- Detailed logging and traceability of AI output.
- Risk assessment & bias detection by continuously analyzing model behavior.
- Auditability and reproducibility of AI decisions, as required by legislation.
Observability is no longer optional: it is a must-have for any serious GenAI application.
Self-Hosted AI Observability: Perfect for Our Customers
Another critical aspect of our partnership is on-premise and self-hosted deployment. Many of our customers require AI solutions that run entirely on-premises, without relying on external cloud providers. LangWatch offers this flexibility without compromising functionality.
As part of this collaboration, adesso provides specialized deployment services to help companies implement and manage LangWatch within their own infrastructure.
adesso: Expert in Large-Scale GenAI Solutions
adesso has a proven track record of building enterprise-grade AI solutions. From LLM integrations and machine learning applications to AI strategies for large organizations: We help businesses operationalize AI.
By adding LangWatch to our technology stack, we not only provide powerful AI functionality but also the control and transparency needed for successful implementation.
We also support both our customers and future LangWatch customers with self-hosted solutions and new AI projects.
Together, We Take AI to the Next Level
With this partnership, adesso and LangWatch make it easier and safer for companies to move Generative AI from experimentation to scalable production. Real-time observability, model flexibility, and compliance-by-design ensure that AI innovation is not only faster but also more reliable.
