Most organizations are paying for AI and seeing two-percent of the value on the table. The gap is rarely the model. It's everything around it.
We do not wait for the next frontier release. Off-the-shelf models, applied through clean data and a real integration, move the needle for revenue, throughput, and citizen outcomes — right now.
We use AI on every engagement to compress what used to be 12-week consulting cycles into weeks. Pipelines, integrations, evals, and documentation are co-written with models — under human review, with our own guardrails.
Three years in, we are a federal prime contractor with a roster of agencies and Fortune-class commercial clients. The team is small on purpose: senior engineers, no layers, every name on the brief is on the work.
Four practices that bridge the gap between a capable model and a working outcome — on real infrastructure, in real organizations.
Stand up the pipelines, warehouses, and access layers that off-the-shelf AI needs to perform. Quietly, with audit trails.
Wire models into the actual systems your teams use — not a sandbox. Existing tools, existing workflows, real outputs.
Build, evaluate, and operate the agents that close the loop on real work: intake, triage, summarization, routing, action.
Guardrails, evals, and the human practice that gets people actually using what we ship. Adoption is half the engagement.
Under Aretum (Prime), StandardData rebuilt the OCR and metadata pipeline for the Library of Congress's National Digital Newspaper Program. 300,000+ historic newspaper pages reprocessed and live on Chronicling America — at roughly $50 per ~5,000-issue batch, 100× cheaper than commercial OCR and 99% faster than the legacy on-prem path. ALTO XML, METS, Dublin Core, NISO. Zero critical CVEs. The Library has publicly announced the pipeline will be open-sourced in 2026.
