Turn manual work into AI-powered systems.

I design and build AI agents, automations, and internal tools for marketing and revenue teams, using the systems you already have.

Show Me What You’re Doing Manually See What I Build

10+ years building marketing & revenue systems.

Examples of systems I build.

Each concept starts with a real operating problem, a defined decision, and a human-readable output. These are illustrative system concepts, not published case studies.

AI Lead Qualification Agent

Prioritizes leads and gives sales a readable qualification recommendation.

Mechanism: Account and engagement signals → fit evaluation → human review → Salesforce recommendation

  • AI Agent
  • Salesforce
  • Lead Management
Account signals flow through AI fit evaluation and human review to a Salesforce recommendation

Decision agentSystem Concept

Marketing Performance Explorer

Gives stakeholders searchable campaign performance without direct marketing-automation access.

Mechanism: Marketo campaign data → normalized metrics → search and filters → stakeholder view

  • Internal Tool
  • Marketo
  • Analytics
Campaign data becomes normalized metrics, searchable filters, and a stakeholder dashboard

Internal toolSystem Concept

AI Campaign QA Agent

Returns issues, warnings, and a launch-readiness decision.

Mechanism: Configuration → validation rules → human review → launch status

  • AI Agent
  • Marketing Ops
  • QA
Campaign configuration passes validation rules and human review before launch approval

QA loopSystem Concept

Attribution System

Connects campaign engagement to pipeline while preserving historical touchpoints.

Mechanism: Campaign touches → relationship rules → attribution window → pipeline view

  • Salesforce
  • Attribution
  • RevOps
Campaign touchpoints connect through relationship rules and an attribution window to a pipeline view

Relationship modelSystem Concept

AI Research + Outreach Pipeline

Prepares account research, relevant signals, and reviewed outreach context.

Mechanism: Account signals → research agent → human review → outreach brief

  • AI Agent
  • Research
  • Sales
Account signals feed an AI research agent, human review, and an outreach brief

Research pipelineSystem Concept

Replace the recurring workaround.

Start with the work your team repeats, rebuilds, watches, or carries between systems.

Manual

Manual lead research

Built system

Agent-generated context, fit evaluation, and reasoning.

Manual

Rebuilt weekly report

Built system

Searchable internal performance tool.

Manual

Checklist campaign QA

Built system

Automated configuration validation.

Manual

Field-change watching

Built system

Event-driven routing with context.

Manual

Six disconnected tools

Built system

One workflow that gathers context and acts.

Choose the right build path.

Start with a focused review, a working first version, or a larger system scope.

Map the opportunity

AI Workflow Audit

$750

Workflow mapping, opportunity prioritization, architecture recommendation, tool and integration recommendations, and a written build plan.

The $750 fee may be credited toward an AI Systems Sprint booked within 30 days, subject to fit and scope approval.

Book an AI Workflow Audit

Flagship build

AI Systems Sprint

$2,500–$5,000

Bring me one painful workflow. In one to two weeks, I’ll design and build a working first version within an agreed scope.

  • One workflow
  • Agreed systems and data sources
  • One primary user or team
  • Defined output and acceptance criteria
  • Human review for consequential or uncertain decisions

Additional integrations, security review, data remediation, and production hardening receive separate scopes.

Kyle confirms the one-to-two-week timeline only after reviewing access requirements, API constraints, security requirements, data quality, and scope.

Start a Systems Sprint

Expand the system

Custom AI Build

Starting at $5,000

Larger internal applications, multi-agent workflows, complex integrations, and cross-system orchestration.

Discuss a Custom Build

Move from buried work to a usable system.

  1. 01

    Find

    Identify the repeated work, decision, handoff, and current constraints.

  2. 02

    Design

    Define the system boundary, data, human checkpoints, and acceptance criteria.

  3. 03

    Build

    Connect the workflow and produce a working first version for the agreed scope.

  4. 04

    Hand Off

    Document the system, review how it works, and define the next operating step.

Hi, I’m Kyle.

I have more than a decade of hands-on experience across marketing, revenue operations, CRM, lifecycle, reporting, and automation. I now focus that operating experience on AI-native replacements for manual work buried inside teams and tools.

I also document what I’m building through The AI Operator, my public-building and education brand.

10+ years
Building marketing and revenue systems.
Systems depth
Salesforce, Marketo, CRM, lifecycle, attribution, analytics, and automation.
Operating range
Lead management, campaign operations, reporting, data quality, and cross-system workflows.

Show me the workflow you hate doing.

If your team repeatedly copies, checks, researches, routes, reports, or updates something manually, there is a good chance we can build a better system for it.

kyle@village-consulting.com

Describe the workflow

Kyle reviews every request and follows up about fit, scope, timing, and price.

Your request goes directly to Kyle. No mailing list or automated sales sequence.

Prefer email? Write to Kyle directly.