01
Work that resists standard software
The operation crosses too many systems, exceptions, and unwritten rules for a generic product to fit.
Little Rock, Arkansas · Applied AI + Software Engineering
Hard operational problems are where we start.
Ouachita Labs works alongside domain experts to understand complex workflows, find where software and AI can create real leverage, and build reliable systems around the way the business actually works.
When to call us
Specific work · Practical systems
When off-the-shelf software stops short.
We are most useful when high-value work is spread across documents, spreadsheets, portals, phone calls, and expert judgment—or when an AI prototype needs to become a dependable production system.
02
Expert judgment trapped in people
Valuable decisions depend on a small number of people manually gathering context and applying hard-won knowledge.
03
AI pilots that need to earn trust
A promising demo needs the data, integrations, evaluations, and human review required for real work.
What we do
Strategy → Build → Operate
We find the leverage, then build what works.
Strategy and implementation stay together. Each engagement moves from understanding the operation to shipping a useful system and making it dependable in production.
01 / Discover
Find the leverage
Map the workflow, quantify the friction, assess the available data and risk, and identify the intervention worth building.
02 / Build
Build the system
Ship internal tools, AI-assisted workflows, integrations, and data pipelines with your experts continuously involved.
03 / Operate
Make it dependable
Add evaluations, human review, monitoring, and iteration so the system earns trust in real operations.
How we work
Founder-led · Senior engineering
Senior engineering, without the handoffs.
Work with the same senior engineer from the first workflow map through production. The person learning the problem is also the person building the solution.
Ouachita Labs is a senior software and data engineering practice focused on applied AI, production data systems, and practical tools for expert teams.
01
Start with the economics
Technology follows the operational outcome, not the other way around.
02
Build beside the experts
Domain judgment stays close to every product and engineering decision.
03
Keep people in control
Human review remains explicit wherever context and judgment matter.
Applied in the field
Healthcare revenue recovery
Selected thinking
Applied AI · Engineering
Notes from building real systems.
Practical writing about agent architecture, model interfaces, and the engineering choices that move AI work into production.
Agent architecture
Borrow your harness
Spend the innovation budget on the domain problem instead of rebuilding mature agent infrastructure.
Read article ↗Document intelligence
Tool calls with BAML
A practical extraction workflow over semi-structured financial documents and source evidence.
Read article ↗Model interfaces
Structured outputs
How to make model responses predictable enough to participate in ordinary application code.
Read article ↗How an engagement begins
Working session → Discovery → Build
You don’t need an AI roadmap. Start with the work.
We begin with a working session to understand the operation. When there is a real opportunity, we scope a short discovery engagement, test the riskiest assumptions, and recommend what to build, buy, automate—or leave alone.
Talk through a workflow ↗01 / Working session
Walk through the workflow, bottlenecks, systems, constraints, and economics.
02 / Discovery
Map the data and risk, then test the uncertainty that matters most.
03 / Build
Move into implementation when the path and expected value are clear.