Systems that know your business. And move it forward.
Ask it a question
Slow water. Deep roots. We build systems the same way.
It usually starts with one of these.
An AI pilot can't cross the production gap.
The demo works, but data quality, permissions, evaluation, economics or operating ownership are still unresolved.
Decide: scale, revise, buy or stop.
Critical knowledge is fragmented.
Policies, documents, data and expert judgment live in different systems. Teams don't always mean the same thing by the same words.
Measure: answer quality, provenance, rework.
A valuable workflow is still manual.
People spend their time gathering context, reconciling sources, routing exceptions and moving work between systems.
Measure: cycle time, touches, exceptions.
Leadership needs an independent decision.
What to fund, what to integrate, what to govern and what not to build, before more budget gets committed.
Decide: build, buy, sequence or defer.
Then we get to work.
What we build.
Agentic AI systems
Agents that use real business tools to finish useful work, with permissions, evaluation and human control.
The graph behind the agents
One connected map of your data, policies and expertise, so agents and people work from the same context.
AI experience design
Interfaces, dashboards and briefings people trust and enjoy using, prototyped in working software.
AI products and automation
From a validated workflow to a product your team can run: pilot, integration, rollout and handoff.
Built here. Working everywhere.
Where it's worked.
Financial Services
Operational-resilience mapping
What breaks if this service fails, queryable, not reconstructed.
Financial Services
Operational-risk graphs
Risk, control, process, and incident data in one assessable structure.
Manufacturing
Agentic AI deployment on an enterprise graph
Agents working from one governed map of plant and business data.
Three anonymized programs. Not proof, references, or endorsements.
Where the work is specific
And hand your team something it can run.
How it runs.
2 to 6 weeks
Executive AI and knowledge diagnostic
What should we fund, and what should we avoid?
4 to 8 weeks
Knowledge foundation sprint
What must people and systems agree on?
6 to 12 weeks
Production-shaped agent pilot
Can this workflow create measurable value safely?
3 to 6 months
Scale and operating partnership
How do we productionize and transfer ownership?
Principal-led, first call to handoff.
Matt Lucas founded Palmetto after nearly ten years at Morgan Stanley, including as an Executive Director of global technology delivery, and a run as Field CTO for Financial Services at Stardog.
Meet PalmettoBring us the hard one.
What we're seeing.
Good questions.
What does Palmetto Interactive do?
Palmetto Interactive is a national consultancy based in Charleston. We solve business problems with AI agents and the graph behind them, then design the experience so people actually want to use it. Our work is built for regulated institutions, global enterprises, and any team with real workflow complexity.
Where is Palmetto Interactive located and what areas do you serve?
Palmetto Interactive has Charleston roots and serves organizations nationwide, with on-site collaboration across major U.S. business markets.
How much does it cost to work with Palmetto Interactive?
We work in four common shapes: Working session, Production Readiness Assessment, Production pilot, Build & operate / advisory. The working session establishes fit before either side commits to delivery. Commercial terms are scoped after the working session and depend on the work required.
What makes Palmetto Interactive different from a typical AI agency?
We start with the business problem. Then we connect AI agents to the graph, the architecture, and the operating controls that make them useful in production. We design the experience so people use them. We match the solution to the need and carry the question through delivery with measurable outcomes.
Which organizations does Palmetto serve?
Palmetto works best where fragmented data, governance obligations, operational risk, or cross-functional complexity make the work hard to run by hand. We take on governed agent platforms, knowledge graphs, AI products and their interfaces, and enterprise delivery programs.
Tell us the problem.
Where's knowledge trapped? What work is repetitive? Which decision is harder than it should be? We'll respond directly, set up a working call and come back with a scoped next step.
Proudly made in Charleston, SC