Build
Workflow automation, anomaly detection, document data extraction, and LLM-assisted internal tooling — built so that a human can always see why the system did what it did.
01The problem
Two failure modes dominate. Manual operational work — reconciliation, recurring reports, result calculation, exception spotting — quietly consumes a large share of a team’s week. And the rush to automate it with AI often produces systems nobody can audit, which is unusable anywhere the number has to be defensible.
02How we approach it
We start from the process, not the model. Most of the value is deterministic: scheduled jobs, rule engines, reconciliation checks, automated exports. Where a probabilistic component genuinely helps — extracting data from documents, flagging outliers, drafting text — it is scoped tightly, given a confidence threshold, and always leaves a record a person can inspect and override.
An honest boundary: our production automation record is in deterministic pipelines, anomaly detection and document extraction. Retrieval-augmented LLM systems we have built in a research setting, not yet as a commercial delivery. We will say which one your project is.
03What you get
04How it runs
Which manual step actually consumes the hours, and what does an error there cost you.
Most operational waste is rules, not intelligence. Rules are cheaper, faster and auditable.
A model only where a rule genuinely cannot express the task — with thresholds and a fallback.
Every automated decision leaves a record. If it cannot be explained, it does not ship.
Automated result calculation and anomaly detection in a prior enterprise engagement cut manual audit time by half.
Tell us what the system has to do and where it currently breaks. You will get a reply from the engineer who would do the work.