AI workflow strategy, implementation, and training for small and midsize teams
Turn repetitive work into AI dependable workflows.
Dot AI Consulting identifies high-value opportunities, connects AI to the tools and data your company already uses, and builds practical pilots your team can adopt safely.
Bring one workflow — I will tell you whether AI, automation, or process redesign is the right next step. No obligation.
Example deliverable
AI Workflow Opportunity Map
Proposal drafting
HighHighYesLowPilotInternal policy search
MediumHighYesLowPilotSupport triage
HighMediumPartialMediumAudit firstWeekly report generation
MediumHighYesLowPilotAutonomous client advice
HighLowNoHighDo not pursue
Ariel Salem
Founder & AI Engineer · Personally leads every engagement
- Document Q&A / RAG
- Autonomous agents
- Existing-tool integration
- Human-reviewed workflows
- Guardrails & evaluation
- Legal & ops domains
The problem
AI is already entering your business. The opportunity is to connect it to work that is slow, repetitive, and expensive.
Knowledge is scattered, handoffs are manual, and most AI experiments stop at the demo. The gap is not the model — it is integration, ownership, and a path to dependable use.
Repetitive communication
Customer, client, employee, and vendor messages require repeated drafting, routing, and follow-up — work that consumes time without adding judgment.
Scattered knowledge
Teams cannot reliably retrieve approved answers from documents, policies, project files, and internal systems when they need them.
Manual handoffs
Information is copied between email, forms, spreadsheets, CRMs, project tools, and reporting systems — one step at a time, by hand.
Pilots that stop at the demo
Early AI experiments lack integrations, evaluation, ownership, monitoring, and a path to reliable use. They impress once and then sit idle.
Who this is for
Built for teams with real workflow pain and no dedicated AI department.
Organizations with 10–249 employees that have real workflow pain and no dedicated AI team.
Operations-heavy businesses
Teams managing repetitive communication, proposals, reports, scheduling, documents, customer requests, or manual movement between systems.
Professional-services firms
Law, accounting, consulting, and other expert teams that need role-specific adoption, controlled use, internal knowledge access, and human-reviewed workflows.
Lower mid-market teams
Organizations with 50–249 employees that have AI ideas or pilots but lack the internal bandwidth to prioritize, integrate, test, and operationalize them.
The strongest engagements have:
- A defined workflow or operational problem to start from
- A clear owner for the process being evaluated
- Access to the relevant tools, data, and systems
- A measurable business outcome — time, accuracy, or volume
- Willingness to begin with a focused, scoped pilot
- A human-review process for decisions that matter
Example automations
The kind of work AI can take off your team's plate.
These are common starting points — practical, well-scoped automations that connect to the tools and data your team already uses.
Email triage and draft replies
Incoming support, sales, or ops emails are classified by type and urgency, and draft responses are generated for a team member to review and send.
Contract and document Q&A
Upload contracts, policies, SOPs, or reports and ask questions in plain language — the system surfaces the relevant clause, figure, or section.
Weekly report generation
Pulls data from your CRM, project tracker, or analytics tool and assembles a structured first draft of a status report on a recurring schedule.
CRM enrichment and cleanup
Researches contacts and accounts, fills in missing fields, flags stale or duplicate records, and normalizes data formats — without manual effort.
Intake form and document processing
Parses submitted forms, uploaded PDFs, or inbound emails, extracts structured data, and routes each record to the right system or team member.
Meeting notes to action items
Transcribes recorded calls, extracts decisions and next steps, and pushes them into Notion, Jira, Slack, or whatever tool your team already uses.
Services
Four focused engagements. Each scoped around a defined problem.
Every engagement ends with a concrete artifact — a policy, a roadmap, a working system, or a trained team. Start with the workflow, not the tool.
AI Safe-Use Policy and Governance Assessment
1–2 weeks
A structured review of how your team is currently using AI, what risks exist, and what policies need to be in place. The output is a written acceptable-use policy, a prohibited-use inventory, escalation procedures, and an approved-tool reference — designed for organizations that need governance in place before a broader rollout.
Key outcomes
- Written AI acceptable-use policy tailored to your organization
- Prohibited-use inventory matched to your data and risk context
- Escalation and human-review procedures for high-impact outputs
AI Workflow Audit and Quick-Win Roadmap
1–3 weeks
The AI Workflow Audit identifies where AI or automation can produce meaningful value, what data and integrations are required, what should be avoided, and which pilot is worth funding first. It is scoped around a real business problem — not a technology category.
Key outcomes
- Workflow and tool inventory across key functions
- Prioritized opportunity map with impact, effort, risk, and data-readiness scoring
- Build-vs-buy recommendations with realistic effort estimates
AI Workflow Implementation and Integration
3–8 weeks
Dot AI Consulting designs and builds focused AI workflows inside the systems your team already uses, with human review, evaluation, documentation, and ownership built in. This is a working pilot or production-ready internal system — not a proof of concept that stops at the demo.
Key outcomes
- Pilot or production-ready AI workflow built and tested
- Integrations with existing tools, APIs, and data sources
- Human review and fallback paths for reliability and safety
Team AI Enablement and Safe-Use Playbook
Half-day to multi-session
Role-specific enablement sessions built around the workflows your team runs every day. Each session produces a reusable safe-use playbook — not a general AI literacy overview or motivational keynote. The goal is consistent, auditable adoption with real habits employees can follow the next day.
Key outcomes
- Role-specific sessions using your team's actual work and tools
- Safe-use playbook with approved-use, prohibited-use, and escalation rules
- Reusable prompt and process templates for recurring tasks
Ongoing AI Advisory and Optimization
Available after an initial project for continued vendor review, policy updates, cost control, evaluation, and workflow improvement. Tailored to the client's operating needs.
Proof and experience
Relevant systems and workflows Ariel has built.
Prior professional engineering work — not client consulting case studies. These examples illustrate the type of system, problem, and approach behind the recommendations.
Production AI workflows
Built conversational AI, document Q&A, AI search, structured outputs, guardrails, and streaming user experiences for production applications.
Complex data and systems
Worked with APIs, GraphQL, relational databases, analytics systems, authentication, billing, and sensitive business data at scale.
Adoption and reliability
Created testing practices, evaluation workflows, observability, documentation, and team guidance to make new systems maintainable and owned.
Example
Document search and Q&A
- Problem
- A team repeatedly searched contracts and internal documents manually to answer time-sensitive questions.
- Approach
- Designed and implemented a permission-aware document search and Q&A system with structured citations, evaluation checks, and human review before outputs were acted on.
- Outcome
- A faster, more repeatable way to retrieve and review contract information — without manually scanning documents each time.
Built as part of professional AI engineering work at a legal AI platform.
Example
Autonomous agent workflows
- Problem
- Engineering teams needed AI features that could make decisions, call tools, and operate reliably without human intervention at every step.
- Approach
- Designed and built multi-step autonomous agent systems with tool calling, workflow orchestration, guardrails, and integration with progressive delivery infrastructure.
- Outcome
- Production-ready agent workflows that are observable, controllable, and deployable incrementally without breaking existing systems.
Built as part of current professional engineering work on a software AI platform.
How engagements work
A clear path from first conversation to working system.
You will know the scope, deliverables, timeline, and fee before work begins.
Bring one workflow or business problem
The free discovery call focuses on a real process, its owner, the tools involved, the data available, and the cost of the current approach. It is not a sales pitch — it is a qualification conversation.
Define a paid first engagement
Most projects begin with a workflow audit, a role-specific enablement program, or a tightly scoped implementation pilot. You receive a written proposal with scope, deliverables, timeline, and fee before any paid work begins.
Deliver a concrete artifact or working system
The engagement ends with a roadmap, playbook, implementation, test plan, documentation, or trained team — not an open-ended strategy deck.
Expand only after value is demonstrated
Additional workflows, systems, or advisory support are proposed after the first engagement has a clear owner, success measure, and demonstrated value. Follow-on work is optional.
Bring one workflow that is slow, repetitive, or difficult to measure.
In a free 30-minute call, we will discuss the current process, the tools and data involved, the owner of the workflow, and whether AI is likely to improve it. If there is a sensible next step, you will receive a recommended engagement path. Leave with a clearer next step whether or not we work together.