A phased path to AI across your operations.
Larry FisherLarry@SoftSnow.ai
Reid ValferReid@SoftSnow.ai
July 2026
What we heard

We started by listening

1
Customer service is your top priority
2
Supply chain is a close second
3
Planning runs on planner-by-planner judgment
4
Answers wait behind cross-team email handoffs
5
Most operational data still lives in the ERP
6
Goal: consolidate both teams into LA
Start now
Supply chain, the track that's ready to go
Phase two
Customer service, validated next
The throughline
Team time back, from pilots to scale
How SoftSnow works

An AI-native approach

Operators who build AI around how your team already works.

The right tool for the job

Power users of the tools. We fit the tool to the problem.

Past the demo

We build the working rollout a slick demo only hints at.

Strategy and execution

One partner, from AI strategy to agents your team adopts.

Wired into your data

Agents connected to your ERP, Zendesk, and files.

01
Where we'd start
Two tracks, sequenced to your readiness.
Phase 1 · start here

Supply chain and forecasting

Where planning runs on manual judgment today.
TodayWith SoftSnow
Planners check inventory site by site→One refreshed planning view across six sites
Safety stock updated inconsistently→Rules run on schedule, every item
Forecast rebuilt by hand in Excel→First-pass forecast drafted for planner review
Follow-ups chased across email→At-risk orders flagged, supplier drafts prepared
Foundation first
We unify the ERP, WMS, OTM, and OMS data your new data transformation manager now owns. You don't need it clean to start.
Art of the possible

Your planning cycle, reimagined

Supply chain planning agentClick a stage
Today

With the agent

Your team's role

Benefit
AI clears the busywork. Planners keep judgment on tradeoffs and supplier relationships.
Phase 2 · validated next

Customer service, one platform

The delay lives in the handoffs between teams.
Where time is lost today
  • Expedite requests bounce between teams by email
  • Supply chain replies hours later; customers wait days
  • Answers vary by rep and by day
On one platform
  • AI reads the order and replies right away
  • Order status and dates surfaced to customers upfront
  • Internal email handoffs cut sharply
  • Service that keeps customers and protects revenue
Support for your Zendesk evaluation
We join Josh's team to tune your Zendesk AI during the evaluation window, the extra hands the evaluation has been waiting on.
We do this today for another client on their Sierra platform.
A first draft of your roadmap

AI Opportunity Matrix™

Prioritized against your goals and dataClick a row
RoleTaskToday Est. timeSystemsPriorityPayoff
A working draft. The engagement refines it with your team; time is qualitative until Discover.
Proof from our work

A manufacturer like you

A mid-sized U.S. dispensing and fluid-control equipment manufacturer
The problem
  • Support answers scattered across sheets, contacts, two sites
  • Every reply depended on individual knowledge
  • Slow, inconsistent, hard to scale
What we built
  • Knowledge-grounded assistant for reps and customers
  • Drafts customer replies from the conversation
  • Rolled out in phases across two product sites
The result
  • Live across both product sites today
  • Self-auditing keeps answers aligned to the site
  • Ongoing QC, reviewed every two weeks
The same phased approach we would bring to Bobrick.
How we deliver

Discover, design, deliver

Discover

Learn the work

  • Intake with your steering and working groups
  • AI data readiness: tech, people, data
  • Map the workflow before we build
Design

Prioritize

  • The AI Opportunity Matrix sets the order
  • Ranked by feasibility, data access, impact
  • Agree the KPI before anything is built
Deliver

Build and adopt

  • Build the first agents, then guided transformation
  • Change management and training so adoption sticks
  • Track impact against the agreed KPI
Governed and people-first
Standards for data, access, and quality. Your team keeps judgment, risk, and relationships.
First 90 days

A concrete first 90 days

Weeks 1–2

Intake and data readiness

Interview Brianna's team; map the planning workflow across sites.

Weeks 3–4

AI Opportunity Matrix

Prioritize the first agents; confirm data access and KPIs.

Weeks 5–8

Build the first agents

Planning view and safety-stock consistency; begin enablement.

Weeks 9–12

Live and measure

Run in a first pod; measure results; plan the scale-up.

Day 90Proven wins and a scale plan. A fair decision point, easy to exit.
In parallel: we augment Josh's Zendesk AI evaluation.
Recommended next steps

Where we go from here

1
Supply chain discovery with Brianna, this month
2
Draft SOW for the supply chain track
3
Help tune Zendesk AI with Josh's team
4
Case study and a reference introduction for your team
We start where you can move now, and earn the next step.
A 90-day proof of concept, built to show value fast.

Thank you

SoftSnow · AI Operators & Strategists
Larry FisherLarry@SoftSnow.ai
Reid ValferReid@SoftSnow.ai

Slides