Procurement · Production

Cribl achieves 15% contract savings and 4 hours saved per benchmarking with Zip's agentic AI agents

The problem

Cribl's first procurement hire inherited a fragmented landscape with multiple disconnected tools, no formal spend management, inconsistent approval workflows, and manual data entry causing inaccurate records. Strategic tasks like price benchmarking consumed hours or even days, leaving little time for relationship-building.

Workflow diagram · grounded in source
1
Purchase request submitted
trigger
“Everyone submits requests in the same place and they can follow the buying journey through that”
2
AI price benchmarking
ai_action
“Benchmark pricing using reliable third-party data in 30 seconds to one minute, work that previously took 3-4 hours or even days”
3
Negotiation email drafted
output
“The agent pre-drafts negotiation emails, enabling business partners to self-serve on contract negotiations. This has delivered 15% average savings on contracts.”
4
Hotel contract analysis
ai_action
“Analyzes complex hotel contracts with multiple charges and percentages, providing clear cost breakdowns and specific negotiation recommendations. Used by the events team and executive assistants, it saves 45 minutes to an hour per contra…”
5
Executive summary generated
ai_action
“Automatically summarizes strategic contracts over $100K for executive staff approval, synthesizing the request, contract details, and interaction history. What once took 30 minutes to an hour now takes seconds.”
6
Executive staff review
human_review
“for executive staff approval”
Reported outcome

Cribl achieved 15% average contract savings, reduced benchmarking from 3-4 hours to 30 seconds to one minute, attained a 10-day average SLA from intake to contracting, and an 8.5/10 user satisfaction score, enabling the team to shift to strategic work.

Reported metrics
Average contract savings15%
Time saved per benchmarking exercise4 hours
Benchmarking time (after)30 seconds to one minute
Benchmarking time (before)3-4 hours or even days
Show all 10 reported metrics
average contract savings15%
time saved per benchmarking exercise4 hours
benchmarking time (after)30 seconds to one minute
benchmarking time (before)3-4 hours or even days
hotel contract review time saved45 minutes to an hour
intake-to-contracting average SLA10-day average
marketing contract SLA3 days
user satisfaction score8.5/10
executive summary preparation time (before)30 minutes to an hour
executive summary preparation time (after)now takes seconds
Reported stack
ZipPrice Negotiation AgentHotel Contract Review AgentExecutive Summary AgentCLMJiraOracle ERP
Source
https://ziphq.com/customers/cribl
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Cribl achieved 15% average contract savings, reduced benchmarking from 3-4 hours to 30 seconds to one minute, attained a 10-day average SLA from intake to contracting, and an 8.5/10 user satisfaction score, enabling t…

What tools did this team use?

Zip, Price Negotiation Agent, Hotel Contract Review Agent, Executive Summary Agent, CLM, Jira, Oracle ERP.

What results were reported?

Average contract savings: 15%; Time saved per benchmarking exercise: 4 hours; Benchmarking time (after): 30 seconds to one minute; Benchmarking time (before): 3-4 hours or even days (source-reported, not independently verified).

How is this procurement AI workflow structured?

Purchase request submitted → AI price benchmarking → Negotiation email drafted → Hotel contract analysis → Executive summary generated → Executive staff review.

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