AI & agent engineering
Design and build AI systems around the decisions, conversations and actions your business needs. From a knowledge assistant to a product agent, we connect the experience to the data, tools and operating boundaries behind it.
Discuss your projectProduct leaders, technology teams and businesses developing a new AI capability
Considered together.
Built around your work.
A promising model demonstration leaves important questions unanswered. Where does the information come from? Can the user trust the answer? What happens when a tool fails? Who approves an action? Our AI engineering work addresses these questions as part of the product, from the first workflow through integration and evaluation.
Shape a workstream around the capabilities you need. The proposal defines which parts are included and how they will be evaluated.
Define the job the agent is responsible for, the context it needs and the point at which a person should take over. Design the conversation alongside the interface so users can inspect, correct and continue the work.
Scope can include task decomposition, instruction design, tool selection, structured responses, contextual handoff and approval states. We distinguish an answer, a proposed action and a completed action in the experience.
Connect approved information sources and choose a model approach appropriate to the task. Retrieval, model selection and response grounding are designed around the information you can actually provide.
The implementation considers source permissions, freshness, missing information, provider boundaries and how references are shown. When a question cannot be answered reliably, the product needs an explicit next step.
Turn a conversation into useful work through scoped tools and application authority. Keep account access, approvals, durable task state and external writes separate from the model’s suggestions.
For multi-step work, we define checkpoints, retries and the evidence returned by connected systems. A booking request, for example, becomes a confirmation only after the booking system accepts it.
Evaluate the complete workflow with representative inputs, including incomplete requests, unavailable tools and escalation. Inspect the user-visible result as well as the model output.
Quality criteria can include grounded answers, correct tool arguments, appropriate approvals and successful handoff. Latency and provider cost are measured for the chosen configuration rather than assumed from a generic benchmark.
Concrete outputs to agree at the start of an engagement. The final scope depends on your systems, access and priorities.
Inside an engagement
Illustrative engagement: a business wants an assistant to understand a request, consult approved knowledge and prepare a service case.
The evaluation follows the entire request into its next system. A fluent conversation alone is not the acceptance criterion.
Start with enough detail to make a good decision. Expand the work when the first scope has earned it.
Review the problem, existing systems and intended users. Identify the first complete workflow, its dependencies and the evidence that would demonstrate a useful result.
Agree responsibilities and review points. Develop the experience and technical implementation together, with working increments that can be inspected against the brief.
Review the result in the agreed environment. Document configuration, remaining limits and operating ownership. Ongoing support or further development is scoped explicitly.
Scope, responsibilities and commercial terms are agreed with your team.
No. Start with the task, the data boundary and the quality requirements. Model and provider choices follow from those needs, including deployment constraints and the evaluation results.
Yes. An engagement can focus on one existing workflow, using the application’s current authentication and systems. A new application or framework is not a prerequisite.
The allowed scope is a design decision. We define which actions are read-only, which require confirmation and which may execute within pre-agreed limits. Autonomy is evaluated for the actual task.
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