A structural walkthrough of this engagement as we run it: the scope rules, the architecture stance, and the operating rhythm. This is not a client account. We do not publish client names, stories, or performance numbers.
The starting point
This engagement targets a process most companies still run manually: a person reads incoming documents, extracts what matters, re-keys it into other systems, and flags exceptions by email. The process works, which is exactly why it never gets fixed. It also consumes skilled staff hours every day and scales linearly with volume.
How the build runs
We apply a one-workflow rule: scope a single process end to end instead of exploring AI opportunities broadly. Discovery maps the workflow as it actually runs, including the undocumented judgment calls the team makes without noticing, before any architecture is drawn.
The build itself is an integration project with a model in the middle: document intake, LLM-based extraction with confidence thresholds, validation against existing systems of record, and a human review queue for anything below threshold. The people who run the process manually review and correct outputs from the first week. That builds trust with the team the system affects most, and their corrections become the evaluation data the system is tuned against.
The architecture stance
Every capability is exposed as an API endpoint so the client’s own team can extend the system without us. It deploys in the client’s cloud accounts, under their access controls, from day one. Low-confidence cases route to people by design; the goal is redirected hours, not a headless process.
What the client owns
Source, documentation, prompts, evaluation datasets, and the infrastructure itself. Nothing about the system requires Markosh to operate it. We consider that a feature of the engagement.