01
Service and operations resolution
Interpret a request, retrieve the relevant policy and history, prepare the response, and update approved systems with human review where needed.
Services · Core offer
From assisted work to controlled action
Agentic AI Systems
Palmetto builds agentic systems around one real workflow, not a demo chatbot. Each system gets the tools, permissions, and judgment it needs to move work forward, with accountability that stays visible.
When to call
Recognize the operating
signal.
Start with the condition you can observe, the consequence it creates, and the person accountable for changing it.
- 01
A workflow depends on people reading, reconciling, deciding, and updating several systems by hand
- 02
An existing copilot can answer questions but cannot reliably complete the surrounding work
- 03
Cycle time or backlog is growing while experienced people spend time on repeatable coordination
- 04
Leadership wants an evidence-based business case before committing to a larger AI platform or rollout
Where it shows up
Concrete uses,
not AI theater.
We apply these patterns to the workflow, systems, decision rights, and measures that drive your business.
02
Analyst research and briefing
Gather, compare, and cite internal and external evidence before assembling a decision-ready brief or escalating an exception.
03
Controlled case workflows
Organize evidence, apply explicit rules, route exceptions, and preserve a reviewable trail for risk, compliance, finance, or other governed work.
04
Revenue and customer operations
Prepare account context, recommend next actions, draft follow-up, and improve system hygiene without hiding decisions from the team.
The work product
What gets delivered.
What keeps it controlled.
Core deliverables
- Workflow map, value hypothesis, baseline, and prioritized automation boundary
- Agent architecture, tool contracts, permission model, and human decision points
- Representative test set, evaluation scorecard, and release criteria
- Production-shaped agent workflow integrated with agreed systems and data
- Telemetry, operating runbook, rollout plan, and prioritized improvement backlog
Production and governance controls
- Least-privilege tool access and explicit approval for consequential writes
- Source grounding, provenance, and clear handling when evidence is missing or conflicting
- Evaluation gates, traceability, cost and latency monitoring, and controlled change
- Reversible actions, escalation paths, and human ownership of material decisions
Delivery path
Move from decision
to production.
Start with the priority workflow, then expand through pilot and rollout as the opportunity grows.
01
Diagnostic
Scope-dependent
Define the operating problem, baseline the current state, test feasibility, and make the value, risk, and decision path explicit.
Typical outputDecision brief, baseline, target state, and sequenced recommendations02
Production-shaped pilot
Scope-dependent
Exercise the hardest assumptions with representative data, real users, appropriate controls, and an architecture that can survive beyond a demo.
Typical outputWorking pilot, evaluation evidence, operating lessons, and go-forward decision03
Scale & rollout
Scope-dependent
Harden what works, integrate it into the operating environment, expand adoption, and transfer the knowledge required to run and improve it.
Typical outputProduction capability, rollout evidence, runbooks, and ownership handoff
Measures & team
Measure the change.
Build with the owners.
Measures that show progress
- End-to-end cycle time and active human touch time
- Completion, acceptance, exception, and rework rates
- Throughput, backlog, and service-level performance
- Adoption, unit cost, and the quality measures specific to the workflow
The team we build with
- An executive sponsor and an accountable workflow owner
- Domain experts who can define good decisions, edge cases, and unacceptable outcomes
- System, data, security, and risk owners available for access and control decisions
- Representative users who can test the work in context and validate adoption
Ownership
Build a capability
your team can own.
Architecture, tool contracts, evaluation assets, and code are documented and handed to your team.
Questions buyers ask
Before we
begin.
What is the difference between an agentic AI system and a chatbot?
A chatbot answers questions. An agent plans and takes bounded action inside a workflow. What matters is whether it improves a measurable process, not the label.
How do you reduce the risk of an AI agent taking the wrong action?
Controls get built with the workflow: narrow permissions, tested evaluations, approval on high-impact actions, and an escalation path to a person.
Start with the operating problem
Bring the context.
We will define the next step.
Share the workflow or decision, who owns it, what it costs today, and what makes the timing matter. We turn that context into a practical path with clear deliverables, measures, timing, and commercial terms.
Discuss an agentic workflow →Or email [email protected].