AI Automation

AI Automation with Clear Boundaries

We build automation that uses probabilistic AI for suitable tasks and deterministic rules for defined controls. Inputs, outputs, approvals, exceptions, and available system events are documented so humans can review the workflow without assuming every step is perfectly observable or reliable.

Bounded Workflow Design

We start by defining the workflow's boundaries: what triggers it, what inputs it accepts, and what outputs it must produce. We separate the parts that benefit from a probabilistic model—like extraction, classification, or drafting—from the parts that must be deterministic, such as calculations, validations, and routing. This distinction is central to our design.

Model and Rule Integration

The model does the fuzzy work; deterministic rules handle the rest. For example, a model might classify an email, but a rule decides whether it goes to a human for review. We define clear interfaces between the two, so the system behaves predictably. We also build retries and exception paths for cases where the model is uncertain or the workflow fails.

Approvals, Handoffs, and Audit Logs

Not every step should be automated. We design approval gates for actions that have business impact, and we make handoffs to human teams explicit. Available workflow events can record inputs, model outputs, rule decisions, approvals, and exceptions according to the selected architecture and retention policy. These records support investigation and review, but they may not explain every model behavior or capture events outside integrated systems.

Evaluation and Change Control

We build evaluation sets that test both the model's accuracy and the overall workflow's correctness. Before any change, we run regression tests to ensure the automation still behaves as expected. Change control is documented, so you know who changed what and when. We do not claim the automation is perfect; we give you the means to measure and improve it.

What the engagement can produce

Practical deliverables

Workflow Boundary and Input/Output Spec

A precise definition of the automation's triggers, inputs, outputs, and non-goals.

Model vs. Rule Decision Matrix

A table showing which steps use probabilistic models and which use deterministic rules, with rationale.

Approval and Handoff Flowchart

A visual guide to human checkpoints, escalation paths, and exception handling.

Audit Log Schema and Evaluation Report

A logging format for every run, plus a baseline evaluation report with test cases and results.

Automate the right parts

Let's separate what AI should do from what rules should do—and build it with audit trails.