A view of the kind of systems we build for international development, the public sector, and regulated enterprise. Engagements are anonymized to respect client confidentiality; detailed outcomes and references are shared under NDA.
Policy and HR guidance was scattered across documents and intranets. People could not find current answers, and could not tell which version was authoritative.
A permission-aware retrieval system over approved sources, with grounded answers that cite the exact passage and decline questions they cannot support.
Incoming agreements were processed by hand. Key dates and obligations were easy to miss, and nothing was structured for downstream systems.
A Document AI pipeline that classifies agreements, extracts terms with confidence scoring, validates against records, and routes low-confidence items to review.
AI ideas kept stalling on data no one fully trusted: unclear lineage, inconsistent definitions, and no access model.
A modeled BigQuery foundation with quality checks, lineage, and column-level access controls: the reliable base a grounded assistant was built on.
Building a public case-study library with named clients and verified metrics is in progress, pending client approval. We do not publish client names, logos, or figures without written consent.
Tell us what you are trying to ground in AI. We will tell you the honest path to production.