How AI Turned a 3-Hour Quoting Process Into 5 Minutes

When a national waterworks manufacturer approached us, their quoting process was broken in a way that's painfully familiar across manufacturing: customer emails arrived with product requests buried in free-text, estimators manually looked up items across six spreadsheets, applied discount logic from memory, and assembled quotes in Word. A single quote took hours. Complex ones took days.

The Problem: Every Quote Is a Research Project

Their estimators — experienced, skilled professionals — were spending 40–60% of their time on research, not estimation. Each incoming email required cross-referencing product catalogs, checking customer-specific pricing tiers, applying regional discount rules, and manually formatting the output. The process was slow, error-prone, and completely invisible to management.

“We had no idea how many quotes we were processing, what our win rate was, or where the bottlenecks were. It was all in people's heads and inboxes.”
— VP of Sales Operations

The core challenges were threefold: parsing unstructured customer requests, matching them to the correct products in a 5,000+ item catalog, and applying a four-layer discount matrix that varied by customer, region, volume, and product category. No off-the-shelf CRM or quoting tool could handle this complexity without significant customization.

Our Approach: AI-Native, Microsoft-Native

We designed QuoteApp to work entirely within the client's existing Microsoft ecosystem — no new vendors, no new security boundaries. The architecture uses Azure OpenAI via Microsoft Foundry for AI intelligence, Dataverse for the data platform, Power Automate for workflow orchestration, and Power Apps for the review interface.

The Pipeline: Email to PDF in Under 5 Minutes

The system works in four stages. First, AI Email Parsing — when a customer email arrives, Azure OpenAI extracts the customer identity, requested products (including informal descriptions), quantities, and delivery requirements. Second, Product Matching — a semantic search engine matches extracted items against the full product catalog with confidence scoring, handling misspellings, abbreviations, and industry shorthand.

Third, Quote Assembly — the system applies the four-layer discount matrix automatically: cell code discounts, volume tiers, special pricing agreements, and discretionary adjustments. Regional pricing rules and customer-specific rate cards are applied without human intervention. Finally, Review & Delivery — the assembled quote routes to the appropriate estimator via Teams notification with a deep link to the Power Apps review interface. One click approves and sends a branded PDF to the customer.

Customer Email (Exchange Online)
    ↓
AI Email Parsing (Azure OpenAI via Foundry)
    ↓
Semantic Product Matching (5,000+ item catalog)
    ↓
Four-Layer Discount Matrix (cell, volume, special, discretionary)
    ↓
Quote Assembly (Dataverse + branded PDF generation)
    ↓
Estimator Review (Power Apps via Teams notification)
    ↓
One-Click Delivery (branded PDF to customer)

Results: Measurable Business Impact

Within the first month of deployment, the results were clear. Quote turnaround dropped from hours-to-days down to under 5 minutes. AI product matching achieved 95% accuracy on first pass, with the remaining 5% flagged for human review. Estimators shifted from research to high-value work — reviewing edge cases, building customer relationships, and handling complex custom projects.

Perhaps most importantly, management gained complete pipeline visibility for the first time. Copilot Studio provides natural-language reporting directly in Teams — managers can ask “show me this week's quote summary” and get instant analytics on volume, value, turnaround time, and pending reviews.

Lessons Learned

Three patterns from this project apply broadly to enterprise AI implementations:

1. Start with the workflow, not the model. The hardest part wasn't the AI; it was mapping the existing tribal knowledge into configurable rules.
2. Build on what exists. Using the Microsoft stack eliminated procurement delays, security reviews, and training overhead.
3. Make AI transparent. Every AI decision in QuoteApp includes a confidence score, matched product reasoning, and applied discount logic, giving estimators full context for their review.

“QuoteApp cut our turnaround from hours to minutes. The AI actually understands what customers are asking for — even when they use shorthand we've never formally documented.”
— VP of Sales Operations

Is your quoting process eating hours that could be spent closing deals? We help manufacturers automate quote generation, product matching, and discount logic. Let's talk about your workflow →