AI agents that survive contact with production
An agent demo takes an afternoon. An agent that runs inside your business — with real data access, real permissions, and real consequences when it is wrong — takes engineering. DEV3LOPCOM, LLC is an Austin, Texas consultancy that builds agents the second way. We are a data engineering shop first, which is exactly the background agent work demands: agents are only as useful as the data they can reach and the systems they can act on.
We build knowledge assistants that answer from your documents instead of hallucinating, workflow agents that move tickets and records through your CRM and ERP, and retrieval systems that keep model output grounded in sources your team can audit.
How an engagement runs
We start with a short discovery phase: which workflows eat the most hours, where the data for them lives, and what failure would cost. From that we pick one or two use cases with clear data access and a measurable outcome — not a moonshot, a first win.
Then we build. That usually means a retrieval layer over your trusted sources, tool integrations with scoped permissions, logging on every action the agent takes, and an evaluation harness so you know accuracy before users do. We ship to a pilot group, measure, tighten, then expand. Your team gets the code, the eval suite, and the documentation to run it without us.
Common problems we fix
- The prototype that never shipped. A team built a chatbot in a hackathon; legal and security killed it. We add the permission boundaries, audit logs, and data controls that get it approved.
- RAG that returns garbage. Retrieval quality is a data engineering problem — chunking, freshness, and source authority. We fix the pipeline, not just the prompt.
- Agents with too much access. An agent with admin credentials is an incident waiting to happen. We design least-privilege tool access with human approval gates on sensitive actions.
- No way to measure quality. We build evaluation sets from your real cases so “it seems better” becomes a number.
Why dev3lop
We have spent years automating work that companies assumed needed headcount. For Lever, we replaced a manual reporting operation — 282 SQL queries, 186 workbooks, and 11 Python applications — saving the client roughly 22,000 hours a year. That project predates the current agent wave, but it is the same discipline: understand the workflow, automate it reliably, prove the savings. Agents are a new tool for that job, not a new job.
We are 100% onshore, and our founder is a former Tableau Software Professional Services consultant, so agent output lands in reporting your stakeholders already trust — whether that is Tableau or Power BI. If your use case is really a marketplace or multi-tenant platform, see our workspace agent marketplace consulting.
Start with one workflow
Bring us the process your team complains about most. We will tell you honestly whether an agent fits, what data it needs, and what it should cost to run. Contact us to scope a pilot.