AI Engineering & Agentic Systems
We build AI capabilities into applications, processes and organizations, with the engineering discipline required to move beyond prototypes.
Explore the collaborationWhen we can help
You are moving an AI prototype into production
You need to evaluate quality and address reliability, security and costs under real usage.
You want to use AI with internal knowledge
You need to connect models with organizational documents and data, including access rules.
You are integrating AI into an application or process
You need to design the integration, tools and boundaries of the tasks the system can perform.
You need control over AI behavior
You want to evaluate answers, track costs and define when a human should take over a decision.
Our expertise
AI-powered applications
We integrate language-model capabilities into real software products and internal systems, shaped around the task, users and operational constraints.
Enterprise knowledge and retrieval
Retrieval, contextual search and RAG connect models with relevant organizational knowledge while respecting the surrounding application architecture.
Agents and agentic workflows
Tools, multi-step automation and human-in-the-loop controls turn models into systems that can perform bounded, observable work.
Production AI
Evaluation, security, reliability, observability, model selection and cost are part of the architecture, not an afterthought after a successful demo.
What does it take to move an AI prototype into production?
Beyond integrating the model, the work includes data access, quality evaluation, failure handling, costs and situations where a human makes the decision.
Deliverables
We agree on the specific deliverables to match your project. Depending on the scope, these can include:
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AI suitability assessment
An assessment of the task, available data and constraints that affect the suitability of AI.
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Integration design
Architecture connecting models, data, tools and existing systems.
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Pilot solution
Implementation of a bounded use case for evaluation under agreed conditions.
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Evaluation scenarios
A set of typical and edge cases with criteria for assessing answers or completed tasks.
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Monitoring setup
Visibility into system behavior, failures and costs during use.
How we work together
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Understand the problem
We clarify your goals, current situation and constraints, including the people and systems involved.
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Agree on scope and deliverables
We define the work, expected outputs and how we will verify them.
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Implement and verify
We carry out the agreed work, review the results together and identify the next useful steps.
Related services
Let’s discuss your AI use case
Tell us what task AI should handle, what data you work with and your current stage.