Clear answers before work begins.

Yes. The engagement begins with the business workflow, not your company’s level of AI maturity. We identify the operational problem, the people and systems involved, and how success should be measured before recommending technology.

FindToni Consulting is best suited to growing businesses with a meaningful operational constraint to address. Common signals include manual coordination, missed customer or revenue opportunities, disconnected tools, and reporting that takes too much time to prepare.

The first conversation focuses on the workflows or bottlenecks you want to improve, the desired business outcome, the systems involved, and whether there is a credible path through AI, automation, or a broader systems change. It is not a generic AI sales presentation.

No. Many clients begin with a business problem rather than a defined solution. The AI Opportunity Sprint is specifically designed to assess workflows, prioritize use cases, review feasibility and risk, and produce an implementation blueprint.

We offer three connected services: the AI Opportunity Sprint for assessment and planning, the AI Operator Build for implementation, and Managed AI Operations for monitoring, support, and continuous improvement after launch.

The AI Opportunity Sprint starts at $3,500, an AI Operator Build starts at $12,000, and Managed AI Operations starts at $2,500 per month. Final scope and investment are confirmed after the initial opportunity call because each engagement is tailored to the workflow and outcome.

An AI Opportunity Sprint typically takes two to three weeks. An AI Operator Build typically takes six to ten weeks. Managed AI Operations is an ongoing engagement. Timing depends on workflow complexity, system access, stakeholder availability, and the number of integrations involved.

Yes. A focused, well-bounded workflow is often the strongest place to start. The goal is to prove operational value, adoption, and reliability before expanding into additional workflows or teams.

Usually not. The first priority is determining whether your current systems can be connected or improved before introducing unnecessary tools. Recommendations are based on workflow fit, maintainability, security, and total operating cost.

Yes. Engagements can include direct implementation, technical leadership, solution architecture, or collaboration with an existing engineering, IT, data, product, or operations team. Responsibilities and handoffs are agreed before the build begins.

Examples include lead qualification and follow-up systems, customer-support and onboarding workflows, internal knowledge assistants, document processing and approval systems, automated reporting, management briefings, custom AI agents, and operational dashboards.

You will need a representative or workflow owner who can explain the current process, provide timely access and feedback, validate edge cases, and support adoption. Systems become dependable when the people who understand the work are involved in designing and testing them.

Data access follows least-privilege principles and client-approved security requirements. The design considers what data is required, where it is processed, who can access it, how long it is retained, and where human approval is necessary. Specific confidentiality and data-processing terms are finalized in the engagement agreement.

Only where that behavior is explicitly approved and appropriate for the risk involved. Important customer communications, sensitive decisions, and consequential actions can include mandatory human review, clear approval checkpoints, audit trails, and escalation paths.

Success metrics are established against the current workflow before implementation. Depending on the use case, these may include response time, processing time, completion rate, manual steps removed, error rate, team capacity recovered, operating cost, or customer-experience measures.

The system is observed against real workflows, issues are resolved, and improvements are prioritized using actual results. Managed AI Operations can include workflow monitoring, performance and cost reviews, prompt and automation improvements, team support, documentation, monthly reporting, and an expansion roadmap.