OPERATOR / AUTOMATION
OpenClaw
An operator and automation layer for turning instructions into repeatable, tool-connected work with the right review points.
Salesprov AI systems practice · 2026
AI systems that move work forward—without outsourcing judgment.
We design practical AI operating systems for revenue, service, and operations teams: the right model, trusted data, clear guardrails, and workflows people can actually run.
Capability architecture
Choose the system layer before choosing a model.
A useful AI system is more than a chat interface. We assemble the operator layer, orchestration, reasoning, knowledge, tools, controls, and measurement around the job to be done.
ORCHESTRATION
Hermes Agent
An agent orchestration option for coordinating research, browser work, files, APIs, and multi-step workflows under explicit controls.
REASONING / CODING
Claude
A reasoning and coding option for analysis, drafting, structured work, and development tasks where evaluation and context matter.
FOUNDATION + CONTROLS
Data, tools, and guardrails
Retrieval, permissions, audit trails, tool contracts, human approvals, and evaluation make capability usable in production.
Model and provider options
Model-neutral by design.
We recommend a fit for the workflow, not a preferred vendor. We are not affiliated with, sponsored by, or endorsed by the platforms named here.
OpenAI & ChatGPT
General-purpose models and product surfaces for language, multimodal, and tool-enabled experiences.
Claude
Reasoning and coding workflows that benefit from deliberate task design and evaluation.
Gemini
A provider option to evaluate where its capabilities, ecosystem, and commercial fit support the use case.
Open-weight & local models
Options for teams weighing local deployment, data location, customization, or infrastructure control.
Selection guidance
Make the trade-offs visible before you build.
Security & permissions
Who can access which data, call which tools, approve which action, and see the audit trail?
Workflow & reliability
Where does AI assist, decide, or act—and what happens when confidence or a tool call fails?
Data & location
Which sources are appropriate, how fresh must they be, and where may sensitive data be processed?
Tool integration
Can the system work with your CRM, knowledge base, ticketing, communication, and internal APIs?
Cost & operations
Consider usage, infrastructure, support burden, failure recovery, and the cost of human review.
Quality & evaluation
Use representative tasks, acceptance criteria, and ongoing measurement—not a one-time demo—to judge quality.
What we deliver
From an honest assessment to a team-owned operating model.
01 / ASSESSMENT
AI systems assessment
Map workflows, data, risks, stakeholders, and current tooling. Prioritize a use case worth improving.
02 / BUILD
Workflow and agent build
Design prompts, tools, retrieval, interfaces, approval states, and integration contracts around real work.
03 / GOVERN
Governance and measurement
Set ownership, safeguards, test cases, success metrics, incident paths, and a cadence for improving quality.
04 / ENABLE
Enablement
Equip operators, managers, and technical teams to use, review, maintain, and evolve the system.
A practical standard
No black-box promises. Clear operating decisions.
We document what the system can do, what it must not do, who owns each decision, and how progress is measured. The result is a credible path from experiment to accountable AI capability.
Start with a working session
Book an AI systems assessment.
Bring one workflow that matters. We will clarify the opportunity, constraints, stakeholders, and next sensible step—whether that is a build, a pilot, or a decision not to automate.