We develop the overarching technical architecture for AI systems and integrate models, data access and existing platform and system environments.
Scalability, availability and future development are taken into account right from the start.
AI Architecture & Operations
Making AI systems ready for production and economically viable to operate
jambit develops AI architectures that reliably connect models, data and existing systems, enabling transparent control over quality, usage and costs. In this way, functioning prototypes are transformed into robust AI systems ready for production use.
From a successful prototype to a robust AI system
Nowadays, an AI prototype can be developed relatively quickly. However, when put into production, the requirements become significantly more demanding: models and data must be reliably integrated, system behaviour must be verified, costs must be managed, and ongoing operations must be ensured.
At the same time, models, providers and pricing models are changing at a rapid pace. A robust architecture must therefore not only work today, but also make future changes and dependencies manageable.
jambit designs this technical foundation in such a way that AI systems can be reliably operated, monitored and further developed.
How we make productive AI technically manageable
Aligning architecture and infrastructure for productive use
Flexibly integrate models and providers
We integrate suitable models and AI services in line with the requirements of the respective system.
Where appropriate, different models can be used for different tasks. This allows for more informed management of performance, costs and technological dependencies.
Making quality and system behaviour visible
We incorporate evaluation, monitoring and technical control mechanisms to ensure that the behaviour and quality of an AI system remain traceable even when it is in production.
Changes become visible and can be assessed in a targeted manner.
Managing usage, costs and operations
We ensure transparency regarding model usage and token consumption, and take the economic implications into account right from the architecture and model selection stage.
At the same time, we lay the technical foundations for stable operation and continuous further development.
How a resilient AI architecture is created
1. Clarify usage and operational requirements
We determine the specific requirements of the use case in terms of quality, speed, availability, scalability and costs.
This creates a robust basis for technical decisions.
2. Design architecture, models and dependencies
Based on these requirements, we develop the appropriate technical architecture and integrate models, data and existing systems.
Provider and model dependencies are deliberately designed in this way, rather than being allowed to become uncontrolled operational risks at a later stage.
3. Ensuring quality, cost and operational reliability
Production capability does not end with the successful integration of the model.
Evaluation, monitoring, consumption and operational requirements are therefore integrated directly into the architecture and the system.
An overview of service modules
AI Architecture & Infrastructure
Architecture and technical foundations for AI components, data access, integrations, deployment and production use.
Model & Provider Integration
Integration of suitable models and AI services, and defining their role within the overall architecture.
Evaluation & Quality Engineering
Technical mechanisms for evaluating and continuously monitoring AI behaviour and the quality of results.
AI Observability & Monitoring
Transparency regarding usage, system behaviour and relevant technical operating parameters.
AI Cost & Token Management
Providing visibility into consumption and costs, and enabling technical control over model selection and usage.
Operational Enablement
Establishing the technical prerequisites for stable operation, further development and operational management.
The technical foundation for the productive use of AI
When AI moves from the experimental stage to production, architecture, costs, quality and operations become factors of business significance.
AI Architecture & Operations provides the technical foundation for ensuring these factors can be managed on an ongoing basis.
Depending on the project, we combine this responsibility with:
When AI Architecture & Operations is appropriate
A successful AI prototype is to be deployed in production
The technical concept works. Now the architecture, integration and operations need to be implemented in a robust manner.
Several AI applications are being developed in parallel
Models, providers and technical access points are evolving in different ways and need to become more manageable.
AI costs are becoming a significant factor
Token consumption, model selection and usage need to become transparent and technically controllable.
Quality and system behaviour are difficult to track during operation
Evaluation and monitoring need to be permanently embedded within the system.
AI is being integrated more closely into business-critical systems
Availability, costs, further development and technological dependencies are becoming increasingly important.
Next step – making AI ready for production
Would you like to integrate an AI prototype into production, further develop your existing architecture, or better manage costs, quality and operations?
Together, we’ll lay the technical foundations for an AI system that works reliably and remains scalable.
* Mandatory field
