We bet the company on three things.
Most companies are already paying for AI and getting a fraction of it back. The gap is almost never the model. It's everything you've bolted around it.
Today's models, today's impact.
We don't wait for the next frontier release. An off-the-shelf model, pointed at clean data and wired into real systems, already moves the numbers a business or an agency lives by. The bottleneck was never the model.
AI-native, by practice.
We connect the entire process: source data, business rules, AI execution, checks, and the handoff to the people responsible. ResponseRail puts that method to work in our own government business development, from opportunity assessment to a draft ready for our review.
Builders who operate what they build.
Founded in 2020, StandardData brings data engineering and delivery experience across federal and commercial work. We hold a GSA MAS contract and build the workflows our own team relies on. Senior engineers stay close to the work and accountable for the result.
We needed more capacity.
We built ResponseRail.
Our GSA MAS contract opened a path to more federal business. Evaluating opportunities and preparing responses put new demands on a small team already delivering client work.
We applied our data engineering and AI experience to the whole response process. That internal workflow became ResponseRail, now available to other firms.
- 01
Company knowledge
Past performance, capabilities, partner roles, and the evidence behind them.
- 02
Connected execution
Opportunity assessment, requirements analysis, drafting, and checks in one workflow.
- 03
A useful handoff
An editable response with open questions, ready for the team's judgment and final review.
The lesson carries beyond proposals: useful AI needs the right company knowledge, checks that catch gaps, and a result people can use in their everyday work.
Clear the blockers. The value's already there.
Three things stand between a capable model and a result you can actually use. We do all three.
We deploy any frontier model, from Anthropic, OpenAI, and Google to open-weight, chosen on merit, not our margin.
See how we actually deliverFederal-grade OCR.
Proven at national archive scale.
Under Aretum (Prime), StandardData rebuilt the OCR and metadata pipeline for a historical newspaper preservation program. 300,000+ historic newspaper pages reprocessed and live on the program's public archive. At roughly $50 per ~5,000-issue batch, that's 100× cheaper than commercial OCR and 99% faster than the legacy on-prem path. ALTO XML, METS, Dublin Core, NISO. Zero critical CVEs. The pipeline has been publicly announced for open-source release in 2026.
Big names. Quiet work. Anonymized on purpose.
Federally proven.
Now working for small & midsize business.
Your business already produces the data to run smarter. It's just trapped in spreadsheets and disconnected apps. We connect it and put it to work within a framework your team uses and trusts.
The same engineers who run production pipelines for a national newspaper preservation program build the reporting, automation, and AI integrations that small- to mid-size, owner-led teams use to define the next chapter of their success.
Re-platformed onto Elastic on full-resolution data, scaled ingestion ~20x (~600 → ~12,000 docs/sec), and cut total infrastructure operating cost by roughly 94%.
Full end-to-end demo in ~3 weeks, with automatic schema mapping and validation moved to the front of the pipeline. Bad data caught up front, with alerts naming the exact missing fields.
Confirmed on the record by the client. A fully autonomous ontology + CSV → knowledge-graph pipeline with auto-generated validation shapes and full provenance lineage.