AI engineering, from architecture to operations.
A practical engineering process for AI agents, LLM applications, and automation. Scope the work, test the behavior, and integrate with the people and software already in place.
Defined requirements. Evaluated systems.
Define the job
Start with a specific workflow, representative examples, and the constraints the system needs to respect.
Build a focused pilot
Connect the smallest useful combination of model, data, and tools so the team can review the approach with real examples.
Test the behavior
Review task quality, difficult inputs, failure cases, latency, and usage costs before deciding what is ready to integrate.
Put the workflow to work
Integrate the agreed scope with existing software and define how people will monitor, review, and maintain it.
Engineering around
your workflow.
The scope combines the application, data connections, evaluations, and operating practices the task needs. Deliverables and responsibilities are agreed before implementation.
Explore the AI StackDecisions to make before release.
| Area | Design decision | Validation |
|---|---|---|
| Task | What should the system do? | Agree on examples of useful and unacceptable outputs. |
| Knowledge | Which sources may it use? | Check access rules, retrieval quality, and source attribution. |
| Actions | What is it allowed to change? | Test tool inputs, permissions, and required approvals. |
| Exceptions | When should a person take over? | Review missing data, uncertain answers, and failed steps. |
| Operations | How will it be maintained? | Define monitoring, cost review, ownership, and recovery. |
Common questions.
The timeline depends on the workflow, data access, integrations, and evaluation needs. We agree on a focused pilot and its acceptance criteria before setting implementation milestones.
Our focus is applied AI engineering: integrating available models with your data, software, and workflows. Model providers and deployment choices are selected for the project.
Start with the task and a set of representative examples. Compare candidate models on output quality, failure cases, data constraints, latency, and operating cost before choosing an approach.
Actions should be limited by explicit permissions, input validation, and approval rules. We define where automation is appropriate and where a person must review the proposed action.
We start with representative examples and approved data sources. Before connecting a system, we agree on access boundaries, what information can leave the environment, and any retention requirements.
We review the evidence against the agreed acceptance criteria, then decide whether to improve, integrate, or stop the approach. Any ongoing operation, support, source code handover, and ownership terms are defined in the project scope.
Applied across enterprise workflows.
Explore how the engineering approach applies to knowledge, document processing, operations, and support.
Explore AI Use Cases