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Case study · Vertical SaaS · Revenue operations

A leading vertical SaaS company

Their revenue team moved 5x as fast.

Their reps couldn't read their own revenue data without an engineer in the loop. We built a custom AI system wired into Salesforce, Snowflake, and Stripe, so any rep can ask any question of their pipeline in plain English and act on the answer without leaving the conversation. No analyst, no BI ticket, no waiting.

7

Days

Kickoff to production.

Enterprise AI rollouts usually measure timelines in quarters. We measure in days.

Industry

Vertical SaaS

Function

Revenue operations

Systems integrated

Salesforce · Snowflake · Stripe

Deployment

Private environment

The problem

The sales team couldn't ask their own systems a question without an engineer in the loop. Ten reporting requests a week piled up in the engineering queue.

Which accounts are stuck in proposal over 30 days in our top segment?

A question a rep should answer in seconds was taking a week and a sprint ticket. Both sides lost. Sales is information work, and the team that can ask better questions of its own data wins more deals.

Engineering reporting queue

10

Pipeline by region

waiting 3d

QBR cohort export

waiting 2d

Stuck deals · top segment

waiting 1d

Vertical breakdown

waiting 5h

+ 6 more in queue

Our approach

We ran the engagement as a phased program, not a tool drop. Each phase had explicit gates so leadership could see progress from kickoff to rollout.

Day 1

Readiness

Mapped permissions across Salesforce, Snowflake, and Stripe, and the recurring questions reps were asking.

Days 2 to 3

Build

Built a custom integration layer across all three systems, tuned to how this revenue team actually works.

Days 4 to 5

Deploy

Shipped to the customer's private environment with every integration wired and live.

Days 6 to 7

Train

One on one training with all 12 reps on the revenue team, against their own pipeline.

What we shipped

Three deliverables, all built in the same week as the rollout.

Custom integration layer

Salesforce, Snowflake, and Stripe exposed as one queryable surface, scoped to each user's native permissions.

>

"Pipeline by region for Q3"

>

"Stuck deals in our top segment"

>

"QBR cohort, top 20 accounts"

Reports on demand

Reps describe what they need in plain English. The system returns the slice, cohort, or chart in seconds.

1:1 sessions

12/12
SK

Sara K.

AE

ML

Mike L.

AE

LP

Lisa P.

Manager

+ 9 more reps

Per rep training and setup

One on one with all 12 reps, against their own pipeline. Workspace, access, and prompts wired before the session ended.

Security & governance

Shipped to their security team's checklist from day one, not retrofitted in month three.

  • Deployed in the customer's private environment. No data ever left their tenant.

  • The integration layer enforces each user's native permissions. The system cannot query data the user couldn't already see.

  • Zero training on their data, contractually, under enterprise terms with the model provider.

Permission scope · per user

Read

Update

Export

Account Exec

Sales Manager

SDR

Mirrors native source-system ACLs

Adoption

Training wasn't a lecture. We ran a one on one with every rep on the revenue team, showed them what they could now ask their own systems, and had everything set up on their laptop before the call ended. The result: 100% weekly adoption from week one through week seven.

Active reps · weekly

100%

W1

W2

W3

W4

W5

W6

W7

Outcomes

What changed after the rollout.

Reporting queue

0

Queue cleared · was 10/wk

Engineering capacity reclaimed

Sales reporting requests to engineering dropped from ten a week to zero. Sprint capacity that used to service pipeline queries now ships product.

Pre meeting prep

60m2m−97%

Revenue velocity

Reps self serve pipeline slices, account research, and QBR cohorts, and the team moves 5x as fast. Pre meeting prep compressed from an hour to two minutes. Deal reviews run on current data, not a Monday snapshot.

Your workflow could be next.

Thirty minutes. A straight conversation about your systems and where custom AI would pay back first.