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AI deployment practice

Forward-Deployed AI Engineering

I embed with B2B revenue teams to ship production AI workflows across CRM, sales, marketing, and customer operations.

See how it works
NYC teams: onsite deployment available
Colin Gillingham

Operator experience across GTM, product, and revenue systems

Tesla logo
Mapbox logo
Clearbit logo
HubSpot logo

Tesla sales ops, Mapbox GTM systems, Clearbit growth, and HubSpot automation platform work.

New York City

For NYC teams, I can work onsite.

Deployment moves faster in the room: mapping workflows, pairing with RevOps, training reps, and driving adoption.

How the work starts

Embed

Bring one messy workflow.

I work inside the real GTM motion: the CRM, the handoffs, the data quality problems, and the people expected to run the system after I leave.

A practical starting point, not a vague AI roadmap.

See engagement paths

Deployment path

One workflow, shipped into production.

Engagement paths

Start scoped. Prove value. Then scale.

Three practical ways to get from messy GTM workflow to owned production AI system. Start with the smallest path that can prove the operating model.

01 / Diagnose

1-2 weeks

Diagnostic

$10k-$15k

Map the workflow and deployment path before anyone overbuilds.

Best when

When the workflow is messy and the right build is not obvious yet.

Included

  • Workflow and systems map
  • Data and tool readiness
  • ROI-ranked use cases
  • 30/60/90-day plan

02 / Ship

4-6 weeks

Most common

Build Sprint

$40k-$85k

Ship the first production workflow inside your existing stack.

Best when

When the team has a high-value workflow and needs it shipped.

Included

  • Workflow design
  • Model, prompt, and eval build
  • CRM and tool integrations
  • Playbooks and training

03 / Scale

3+ months

Embedded Partner

$15k-$30k/mo

Scale systems, adoption, and team ownership across more workflows.

Best when

When AI workflows need an owner across adoption and expansion.

Included

  • Ongoing optimization
  • New workflow deployment
  • Performance reporting
  • Team enablement

Not sure where to start?

Bring one workflow. We'll choose the right path together.

Deployment fit

Built where your team already works.

I choose the tools around the workflow: frontier models, evals, automation, CRM, and the systems your revenue team already uses.

New York City

For NYC teams, I can work onsite.

Deployment moves faster in the room: mapping workflows, pairing with RevOps, training reps, and driving adoption.

Working surface

The stack is a means to an owned workflow.

Models and agentic coding

OpenAI logo
OpenAI
Anthropic logo
Anthropic
Claude Code logo
Claude Code
OpenAI Codex logo
OpenAI Codex
Open-source models logo
Open-source models

Automation and orchestration

n8n logo
n8n
Slack logo
Slack

Revenue systems

HubSpot logo
HubSpot
Salesforce logo
Salesforce
Gong logo
Gong

Good fit

  • Messy GTM data or CRM drift
  • Manual qualification work
  • AI pilots that need production rigor
  • Automation that needs a real owner

Not a fit

  • Large-firm consulting theater
  • Strategy-only advisory
  • Work that stays outside the tools

Ready when the workflow is real

Bring one real workflow.

In 30 minutes, we'll map where AI can create leverage, what data it needs, what system it should update, and what the first deployment should look like.

Identify the highest-leverage workflow
Pressure-test the data, tools, and deployment path
Choose the right first step

First step

Deployment-fit call

Best fit if you run a B2B revenue team with messy customer data, manual GTM work, and pressure to turn AI pilots into systems.

30 minutes
No pitch deck
Leave with a first-step plan

NYC teams: onsite available.