AI-Powered Quoting

Speed wins in B2B quoting — research shows that 78% of customers buy from the first vendor to respond, and responding within one hour makes you seven times more likely to close the deal. AI is embedded throughout HyperQuote's quoting engine to compress the time between a customer asking for a price and receiving a quote.

The quoting process traditionally takes days: receive request, identify suppliers, send inquiries, wait for responses, compare prices, apply margins, build the document, get approval, send to customer. AI compresses multiple steps in this chain.

When a quote request arrives, the system immediately classifies each line item against one of three pricing tiers:

• Instant (60-80% of items) — Supplier prices are fresh (updated within 24 hours) or covered by a framework agreement. The AI applies margin rules and presents a ready-to-quote price to the sales representative in real time.
• Fast (10-20% of items) — Prices are aging (1-3 days old). The AI sends a verification request to the supplier and estimates a price from historical data while waiting. Confirmed pricing typically arrives within 15 minutes to 2 hours.
• Standard (10-20% of items) — No cached pricing exists. AI identifies the best suppliers to query based on product category, past performance, and regional coverage, then queues the items for procurement outreach.

This tiered approach means that for many requests, the sales representative can build a quote while the customer is still on the phone.

For items in the "fast" and "standard" tiers, AI generates price predictions based on historical transaction data. These predictions draw on past purchases of the same product, seasonal price trends, supplier pricing patterns, and current market conditions for the material category.

Price predictions appear with confidence indicators. A prediction based on dozens of recent transactions for the same product from the same supplier carries high confidence. A prediction for a new product category with limited history carries lower confidence and is flagged for manual verification.

When procurement needs to source pricing for an item, AI recommends which suppliers to contact based on:

• Product category expertise — Which suppliers carry this type of material
• Historical performance — Past win rates, pricing competitiveness, delivery reliability
• Stock freshness — Suppliers with recently updated stock data are prioritized
• Regional coverage — Suppliers who can deliver to the customer's area
• Capacity — Suppliers not already at high utilization from other active orders

This replaces the manual process of a procurement officer deciding who to call based on memory and spreadsheets.

Customer quote requests arrive in various formats: typed material lists in the portal, uploaded Excel spreadsheets, PDF bills of quantities from architects, and even free-text messages. AI parses all of these into structured line items — product name, quantity, unit of measure, and specifications — creating a clean material list for the quoting team to work with.

For ambiguous requests ("wood" without specifying species, grade, or dimensions), the AI flags the item for clarification rather than guessing. The sales representative resolves ambiguities during their single comprehensive call to the customer.

The quote builder includes AI-assisted margin calculations. The system applies margin rules by product category (cement at 18-22%, steel at 12-18%, specialty items at 30-45%), flags any line item below the margin floor, and suggests adjustments that optimize the overall deal margin while remaining competitive. Sales representatives can adjust individual line margins within their authority level, with below-floor pricing requiring manager approval.