AI Consulting for Execution

Turn AI ideas into practical workflows, better decisions, and usable systems.

We help teams identify where AI actually fits, what to prioritize, and what to build next.

Fit check find the smartest first move
Workflow lens connect goals, constraints, handoffs, and AI fit
Usable outputs maps, blueprints, memos, workshops, and systems
Tailored Workflow Optimizer
Step 1: Discover Friction
Identify operational bottlenecks & redundant tasks
Step 2: Map to Practical Use Case
Fit appropriate LLM, agentic system, or API automation
Step 3: Measure Business Outcome
Quantified hours saved and operational speedups
Real-world execution

Typical deliverables include opportunity maps, workshop agendas, workflow blueprints, and executive decision memos.

Operations Productivity Applied AI Strategy
The Intellegen workflow lens

Work backward before you automate.

Every engagement starts with the same question: what outcome matters, what constraints shape the work, and where AI actually belongs.

  1. 01 Goal What outcome actually matters?
  2. 02 Constraints What tools, data, policies, and risks shape the work?
  3. 03 Workflow Where do delays, handoffs, and decisions happen?
  4. 04 AI fit Is this a prompt, process, agent, or full system?
  5. 05 Next step What should happen now, later, or not at all?
Choose your starting point

Choose your starting point.

Different teams need different first steps. Pick the situation closest to yours.

Recommended

Custom AI Workshop

Best for teams that need shared language, role-specific examples, and practical use cases people can recognize in their own work.

Most teams do not need more AI enthusiasm. They need better workflow decisions.

AI usually fails at the workflow level: unclear priorities, poor fit, scattered experiments, and no clear path from idea to execution.

Intellegen helps teams work backward from goals, constraints, and operating reality to make better decisions about where AI belongs.

How we do things differently
  • Abstract AI trends and tool demos
  • Generic prompts disconnected from real work
  • Experiments without shared operating outcomes
  • Workflow-based opportunity mapping
  • Recommendations shaped by systems and constraints
  • Clear outputs teams can use immediately
Services

Five ways to turn workflow uncertainty into useful AI execution.

Each offer starts from the same workflow lens, then adapts to what your team needs most: alignment, prioritization, workflow design, systems build, or leadership clarity.

Alignment Offer

Custom AI Workshops

Tailored sessions built around your team’s actual workflows, tasks, and constraints so people leave with relevant examples—not abstract tool demos.

  • Pre-work interviews and workflow review
  • Hands-on exercises using real scenarios
  • Shared prompt patterns, tool ideas, and team playbook
Explore workshops ->
Diagnostic Offer

AI Opportunity Assessment

A focused review of your workflows, bottlenecks, and business goals to rank the AI use cases most worth pursuing now, later, or not at all.

  • Workflow audit and operator interviews
  • Data, risk, effort, and integration review
  • Ranked backlog with pilot recommendations
Explore assessments ->
Workflow Offer

AI Workflow Review

Design support for automations, copilots, and human-in-the-loop workflows that need to fit existing systems, validation rules, and operational reality.

  • Trigger, data, and validation design
  • Human handoffs and failure-mode planning
  • Implementation requirements and handoff notes
Explore workflow review ->
Leadership Offer

Strategic Advisory

Ongoing guidance for leaders making decisions about AI priorities, vendors, governance, implementation paths, and execution risk.

  • Vendor, stack, and roadmap review
  • Governance, privacy, and risk guidance
  • Executive memos and decision support
Explore advisory ->
Build Offer

AI Systems Design & Build

For companies that need a complete AI solution, from concept and architecture through prototype or production-oriented implementation.

  • Custom and generic AI systems built around real workflows
  • Agentic, workflow-driven, and decision-support applications
  • Usable software, prototype, or implementation path
Explore AI systems ->
Methodology

A practical process from discovery to action.

Each engagement moves from workflow understanding to prioritized next steps your team can actually use.

01

Discover

Map workflows, goals, tools, data constraints, and repetitive friction.

02

Prioritize

Score use cases by value, feasibility, risk, and speed to adoption.

03

Tailor

Design workshops, prompts, workflows, or advisory outputs around your context.

04

Activate

Leave your team with artifacts, owners, and next-step implementation guidance.

Before

Manual handoffs

Email queue Spreadsheet copy Manager review CRM update
After

Validated pipeline

Intake Classify Validate Handoff
Outputs & Proof

What the workflow lens produces.

Engagements are designed to produce usable artifacts, not just conversations. Each output connects a real workflow to business value, implementation constraints, and the next decision your team needs to make.

Opportunity Map Prioritization

Ranked AI Opportunity Map

Scores AI use cases by value, effort, risk, and readiness so priorities are easier to defend.

  • Compares candidate use cases
  • Separates now, later, and not-at-all ideas

Business value: Better pilot choices before time and budget are committed.

Workflow Blueprint Implementation clarity

Workflow Blueprint

Maps triggers, validations, handoffs, and system updates before build decisions are made.

  • Shows where AI and human review fit
  • Clarifies implementation requirements

Business value: Fewer assumptions before build.

Workshop Assets Team adoption

Role-Specific Workshop Assets

Turns attendee roles and recurring work into practical AI examples, prompts, and workflow ideas.

  • Tailored examples by role
  • Shared language for adoption

Business value: Faster alignment than generic training.

Decision Memo Leadership clarity

Executive AI Decision Memo

Summarizes which AI bets to pursue, delay, or avoid before pilots, vendors, or internal builds.

  • Frames decisions in business terms
  • Connects opportunity, risk, and execution tradeoffs

Business value: Better decisions before resources are committed.

Representative workflows

Examples of practical AI systems shaped around real work.

Examples of how Intellegen AI helps teams reduce manual work, improve decisions, and speed up execution across operations, reporting, and AI workflow design.

ERP + Operations

ERP + Operations Planning

Challenge

Purchasing and inventory decisions depended on daily manual review of dense ERP and purchase-order data.

What changed

A decision-support layer helped planners compare purchasing options, inventory tradeoffs, EOQ-style logic, and what-if scenarios.

Outcome

Faster replenishment review, clearer tradeoff analysis, and less repetitive manual scanning.

Marketing + Reporting

Marketing + Reporting Automation

Challenge

Analysts spent too much time pulling, cleaning, and assembling campaign data from multiple reporting sources.

What changed

Reporting preparation was automated so analysts could focus on interpretation, recommendations, and client-ready narratives.

Outcome

Less manual report production and more time for insight selection.

BPO + Voice Workflows

BPO + Voice Workflow Orchestration

Challenge

New voicebots and client workflows took months to configure before large campaigns could go live.

What changed

A proof of concept introduced natural-language workflow creation with reusable skills, simulations, and guided testing.

Outcome

Shorter launch cycles, less internal build dependency, and faster time to revenue.

These outputs are shaped around your current workflows, available systems, data constraints, and measurable next steps—not generic AI recommendations.

Gilbert Mizrahi, founder of Intellegen
Gilbert Mizrahi Founder, Intellegen
Founder

Work directly with Gilbert Mizrahi on practical AI adoption.

Gilbert Mizrahi is the founder of Intellegen and a hands-on systems strategist focused on practical AI adoption. He helps teams identify the right use cases, design reliable workflows, and move from promising demos to systems that support real business decisions.

Stanford MS

Operations Research background applied to prioritization and decision systems.

Decision Systems

Experience building systems for DoD, NASA, and national data infrastructure.

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Free initial fit check
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