Work samples · Agentic workflows
The hard part of putting AI on real business data is not the model. It is procurement, compliance, observability, and the event chain that lets you trust the output. We run foundation models inside walled-garden environments, wired to event-sourced data, with explicit human checkpoints where confidence dictates.
The approach
We do not have one architecture for the prototype and another for production. The walled garden, the event-sourced context, the confidence scoring, and the human checkpoints exist on day one. The only thing that scales is the volume of work the workflow handles.
Foundation model running inside an isolated environment with zero data leakage by construction. Your data never trains anything outside the wall and never crosses a boundary you have not signed off on.
Designed to clear procurement and compliance review on day one. The hard part of putting AI on real business data is not the model — it is procurement saying yes. The architecture answers the questions reviewers actually ask.
The model reads from the same event log the integrations write to. Context is reconstructable, decisions are reproducible, and bug fixes can be replayed against history instead of triggering a one-off migration.
Confidence-scored output with explicit checkpoints for low-confidence cases. Operators stay in the loop where their judgment is the differentiator; everything else runs unattended with a clean audit trail.
Why this architecture
Most AI projects fail in the same three places: legal review never clears, debugging is impossible because nothing is observable, and the first prompt change destroys whatever consistency the previous version had. This architecture removes all three.
Most 'AI for the business' projects die in legal review. The walled-garden architecture passes review fast because it answers the questions reviewers actually ask — data residency, training boundary, egress control, audit, retention.
Every decision is tied to the events it read and the prompt it ran. Debugging an agent is debugging a function, not interrogating a black box. Production support is a normal engineering activity.
When the prompt changes, the policy changes, or the model upgrades, the same events run through the new path. You see the delta, you do not have to imagine it. Every change is an experiment with a control group.
Coverage
We do not put a model on every workflow. We put one where the combination of context, judgment, and volume actually creates leverage — and where the audit trail and confidence scoring make the output defensible to finance, legal, and the regulator.
Start with the work
We will help separate a useful first deployment from the AI theater that never reaches production — and we will keep legal on your side while we do it.
