Automation with observable behavior
AI and automation engineering
Build AI into real workflows with explicit quality targets, security boundaries and operating economics. We focus on the system around the model: inputs, evaluation, failure handling, human control and production operations.
Discuss this workThe situation
When this work becomes necessary.
A team has identified a workflow worth automating, but a demo is not enough. The result must work with existing data and systems, behave predictably enough for its role and remain supportable after launch.
Workflow design
Define the decision boundary, human role, acceptable error and recovery path before selecting models.
Data and integration
Connect source systems, retrieval, tools and permissions with traceable data handling.
Evaluation
Create representative test sets, quality checks and release gates for non-deterministic behavior.
Production operation
Instrument latency, cost, failure modes, security controls and model or prompt changes.
Context acquisition
What we need, and what we can reconstruct.
- Examples of the work, decisions or outputs the system should improve.
- Available data sources, access rules, sensitivity and retention requirements.
- A business owner who can define acceptable outcomes and escalation behavior.
When context is incomplete: When the workflow is informal, we reconstruct it from real cases, operator decisions, source material and downstream consequences. This produces testable scenarios instead of relying on a generic model demo.
Work packages
Bounded work with a visible result.
Feasibility package
Workflow definition, data review, model options, evaluation plan and production risks.
Applied prototype
A bounded end-to-end system tested against representative examples and failure cases.
Production integration
Secure integration, evaluation gates, observability, human controls and operating runbooks.
Constraints considered in the estimate
- Model output can be non-deterministic and must be bounded according to the consequence of error.
- Data rights, privacy and security can limit which providers or deployment patterns are suitable.
- Latency and token, model or infrastructure cost are part of product behavior, not an afterthought.
Deliverables
What your team receives.
- Workflow and threat boundaries with an explicit evaluation approach.
- Working automation integrated with required systems and human review points.
- Quality, cost and failure telemetry plus the documentation to operate it.
Questions
Before an engagement starts.
Do you train custom models?
When the evidence supports it. Many useful systems are better served by retrieval, tools, controlled prompting or smaller specialized models.
How do you measure quality?
We define task-specific examples and scoring before production, then monitor the failures and business outcomes that matter.
Can sensitive data stay controlled?
Yes, but the architecture depends on data classification, provider terms, hosting requirements and the controls your organization needs.
Start with the problem
You do not need to prepare a perfect specification.
Share what exists, identify the people who know what cannot be discovered and we will investigate the rest.
Discuss a project