Common questions about working with Dot AI Consulting

Typically organizations with 10–249 employees that have repetitive, document-heavy, or coordination-heavy workflows and no dedicated internal AI team. Operations-heavy businesses, professional-services firms, and lower mid-market companies moving from AI experimentation to dependable adoption are the strongest fit. If you have a real workflow with a clear cost of doing nothing and are ready for a structured paid engagement, we are likely a good fit.

Both. The AI Workflow Audit produces a prioritized roadmap and identifies the right first pilot. The AI Workflow Implementation and Integration service builds that pilot — connected to your existing tools and data, with human review, evaluation, documentation, and an ownership handoff. Ariel Salem is a senior software and AI product engineer with more than 10 years of production experience. Recommendations come from building real systems, not slide decks.

Yes. Integration with existing tools is a core part of every implementation engagement. That includes CRMs, document stores, databases, project trackers, communication tools, and internal systems. A standalone prototype that requires a separate integration to be useful is not a finished system — integration is part of the build, not an afterthought.

Not necessarily. The workflow audit includes a data-source and integration map that identifies what is available, what is accessible, and what is missing. Some workflows can start with imperfect data. Others require data cleanup before a pilot makes sense. If data readiness is a blocker, that will be identified early and included in the scope discussion — not discovered partway through a build.

Every engagement begins with a review of the data involved, who should have access, what can be sent to third-party tools, where human review is required, and how quality will be tested. Sensitive data is not sent to external model providers without explicit review and approval. Recommended tools are evaluated for their data-handling practices before endorsement. Where specialist legal, security, or compliance review is required, that will be stated clearly.

Tool and model selection is matched to the workflow, data context, security requirements, cost profile, and integration needs — not locked to a single vendor. Common platforms include OpenAI, Anthropic Claude, and various embedding and retrieval tools. Existing tool licenses and infrastructure are used where they fit rather than recommending unnecessary new platforms. Vendor flexibility is a working principle, not just a talking point.

That is a legitimate outcome. Some workflows need better process design, cleaner data, or a simpler automation rather than a custom AI system. Dot AI Consulting evaluates the business problem first. If a conventional automation, an existing software feature, or no change at all is the better recommendation, that is what you will hear. A free discovery call that saves you from a poor investment is still a good call.

No price ranges are published because engagements vary significantly in scope based on the workflow, systems, data, integrations, stakeholders, and depth of analysis or implementation required. What is consistent: after an initial discovery call, you will receive a clear written proposal with a defined scope, deliverables, timeline, and fixed fee before any work begins. You approve the complete scope and fee before committing.

The AI Workflow Audit typically runs 1–3 weeks depending on the number of workflows, stakeholders, and systems involved. A focused implementation pilot typically runs 3–8 weeks for a single workflow. Team enablement programs run from a half-day to multi-session formats depending on team size, roles, and whether a written safe-use playbook is included. Timelines are confirmed in the written proposal before work begins.

Yes. The Team AI Enablement and Safe-Use Playbook service is designed specifically for non-technical employees. Sessions are built around the actual workflows the team runs — not a machine learning lecture or general AI trends overview. Participants leave with reusable prompt templates, safe-use rules, and practical habits they can apply the next day. Sessions can be customized for roles including operations, legal, accounting, client services, and management.

Yes. Many engagements involve collaboration with an internal technical team. The workflow audit can inform an internal build. Implementation work can be handed off to an internal team with full documentation. In some cases the engagement serves as a scoping and design exercise that an internal team then executes. The goal is always to leave the client owning the system, not dependent on continued consultant involvement.

Ongoing AI Advisory and Optimization is available after an initial engagement for clients who need continued vendor review, policy updates, cost and token optimization, evaluation improvements, or workflow expansion. Ongoing support is proposed after the first engagement has a clear owner and demonstrated value — it is follow-on work, not the default first step. The discovery call and initial engagement are not commitments to an ongoing retainer.