Implementation Process

From stuck workflows to production AI, with governance built in

We do not build throwaway pilots. We select one high-value workflow, ship a working system with real users, and add the controls legal, security, and leadership need to approve rollout.

Week 1
Select workflow + ROI
Weeks 2-4
Build working system
Week 5+
Governed rollout
Production Launch Path3-8 Weeks to Launch
🔍
Week 1: Select workflow + ROI
Approved
⚙️
Weeks 2-4: Build working system
In Progress
🚀
Week 5+: Governed rollout
Controls Ready

Why This Gets to Production

We do not build pilots. We ship governed systems with real users.

No Throwaway Pilots

The first build is scoped to launch with real users, not sit in a demo folder.

Governance Before Launch

Access controls, audit trails, data boundaries, and review flows are designed before rollout.

Real Users Early

The system is tested against the workflow, data boundaries, and adoption signals that matter.

Metrics Decide Scale

Cycle time, review load, accuracy, adoption, cost, and user satisfaction decide what expands next.

One Launch Path

Select the workflow, build the system, then roll it out with controls.

Phase 1
Week 1

Select Workflow + ROI

We map the stuck workflow, review data access and risk, and choose the first AI system worth shipping.

Deliverables:

  • Workflow map with bottlenecks and decision points
  • ROI model for the recommended first build
  • Security, data, and launch plan
Phase 2
Weeks 2-4

Build Working System

We build the assistant, automation, dashboard, or infrastructure layer with real users and measurable usage.

Deliverables:

  • Working AI system deployed for active users
  • Training and documentation
  • Usage and outcome metrics from launch
Phase 3
Week 5+

Governed Rollout

Once the system is working, we add the policies, controls, training, and monitoring needed to expand safely.

Deliverables:

  • Rollout plan for additional teams or workflows
  • Governance policies and approval controls
  • Usage monitoring, logging, and optimization

Example Production Outcomes

The kinds of workflow results this process is designed to produce

Mid-Market SaaS

3 weeks to production

Challenge

Support team needed faster answers without giving up quality review or source-backed responses

Results

82%
Tickets auto-resolved
2.1 hours
Avg response time
+34 points
NPS improvement

Professional Services Firm

4 weeks to production

Challenge

Proposal creation was slowing sales capacity because every RFP required heavy manual research and drafting

Results

87%
Faster proposals
3.2x
More RFPs handled
$1.8M
Added pipeline

Manufacturing Company

3 weeks to production

Challenge

Contract review created deal delays because legal had to manually inspect routine documents

Results

94%
Auto-approved contracts
15 min
Avg review time
22 days
Faster deal cycles

Find the workflow worth launching first.

Book a free 30-minute call to identify your highest-impact AI opportunity and the controls needed to ship it safely.