Zoho-Based AI Quote Verification: ~$63,000 in Estimated Annual Value

Key results

68%

faster quote review

29.5 hours

returned per month

92%

first-pass extraction accuracy

74%

of comparable lines matched at high confidence

construction zoho case study

Summary

The client is a US construction company reviewing approximately 118 supplier quotes per month for project materials. Quotes arrived in different formats and used inconsistent product names, units, and part numbers. Checking prices was not as simple as comparing two numbers. The purchasing team had to confirm that products were comparable, standardize units, find a current reference price, and separate product costs from delivery and tax.
WiserBrand built an AI-assisted quote verification workflow around Zoho Mail, Zoho Flow, and Zoho CRM. It converts incoming quotes into structured line-item records, checks comparable prices using an approved benchmark hierarchy, and sends the purchasing team a concise list of the exceptions carrying the greatest dollar impact. The workflow prepares the evidence required for a faster, more consistent review while leaving every purchasing and supplier-facing decision with the client.
Cooperation Period
6 weeks
Location
USA
Industry
Construction
Service Provided
AI Automation
construction site zoho case study

Business Challenge

Quote Data Required Manual Preparation

Supplier quotes arrived as PDFs, scans, phone photos, spreadsheets, and email text, often across multiple revisions. The purchasing team had to identify the latest version, extract line items, standardize units, and separate product prices from delivery and tax before any comparison could begin.


Price Differences Needed Context

A price gap did not necessarily mean a supplier was charging too much. The team still had to find a current benchmark and judge whether specifications, quantities, availability, lead times, and other terms made the comparison valid.


Reviews Were Difficult to Track

The purchasing team searched purchase history, distributor lists, and retail sites line by line. Exceptions were not consistently ranked by dollar impact, and the evidence behind each decision was difficult to retrieve or compare over time.

What We Did

  • 1

    Centralized Quote Intake in Zoho

    WiserBrand connected a dedicated Zoho Mail inbox to Zoho Flow and Zoho CRM. A document-extraction service converted incoming quotes into structured records, with benchmark results and match confidence stored in custom CRM modules. Revisions and duplicates remained linked to the same quote history.

  • 2

    Applied a Tiered Benchmark Hierarchy

    The agent compared each line against the most reliable available source:

    • The client’s own purchase history
    • Approved distributor price lists
    • Current retail pricing as a final sanity check

    Retail pricing was not treated as the target purchase price. Each comparison retained its source, link, and timestamp. Product prices were compared before tax and delivery, with those costs recorded separately.

  • 3

    Added Product Matching and Confidence Rules

    The agent matched products by UPC or SKU where available, then fell back to brand, model, specifications, and material attributes. Units and pack sizes were normalized before any price variance was calculated.

    High-confidence matches were compared automatically, while uncertain or incomplete matches were routed for manual review. Category-specific thresholds sorted lines into in range, requires review, or appears inflated.

  • 4

    Prioritized Exceptions for Review

    Exceptions were ranked by total dollar impact and delivered to the purchasing team with the benchmark, variance, confidence level, and supporting evidence attached.

    The agent did not reject quotes, contact suppliers, or approve purchases. The purchasing team confirmed the validity of each comparison and decided whether to approve the quote, request clarification, or negotiate.

Project Results

Quote checks became part of every approval
Each quote now goes through the same verification process before the purchasing team makes a decision.
Reviews focus on the exceptions that matter
The purchasing team receives a prioritized queue with comparison evidence already attached, while ambiguous items stay clearly marked for manual review.
Supplier pricing patterns became measurable
The company can track average price variance, the number and value of inflated items, and the share of reviewed lines above the selected benchmark by supplier and by period.
Pricing decisions are easier to trace
Each review keeps the benchmark source, match details, confirmed exceptions, and approval notes in one place. When the team questions a price, the supporting evidence is already available.