Increase win rates
Respond to RFQs faster with optimized, data-driven pricing that improves conversion.
Quote pricing, powered by AI and automation
Optimize your entire quote to order workflow, from the first customer request, to order confirmation, and discover how AI can revolutionize your sales team's day-to-day.
The UiPath Solution for Quote to Order
Quote pricing, powered by AI and automation
Optimize your entire quote to order workflow, from the first customer request, to order confirmation, and discover how AI can revolutionize your sales team's day-to-day.
Responding to quote requests takes time
Prospects expect fast, tailored quotes. But responding to requests for quotation (RFQs) is often manual, fragmented, and time-consuming. Teams pull data from multiple systems, review historical pricing, and build quotes by hand, which can take hours and slow down response times. That delay risks losing business to competitors. Under pressure to hit targets, it's easy to rely on discounting to close deals. You need to deliver quotes quickly at a price that wins new business while protecting margin.
See the UiPath quote pricing capabilities in action
Maximize revenue and profit margins
Balance win probability and margin with AI-powered price optimization.
Reduce excessive discounting
Replace manual reviews and reactive discounting with consistent, intelligent pricing guidance.
Gain a competitive edge
Orchestrate agents, systems, and data seamlessly to respond faster and price smarter than the competition.
The platform behind every quote
Quote pricing is built on the same orchestration, governance, and execution layer that runs every agentic workflow at UiPath.
Under the hood
The UiPath Solution for commercial pricing's quote pricing module uses AI models to estimate sale conversion probability on a range of prices to instantly recommend a price for each quote, helping you maximize margin and increase conversion rates.
Key users
Sales Manager
Pricing Manager
Pricing Analyst
Salesperson
Channel Sales Specialist
Business Development Manager
Field Sales Representative
Territory Sales Representative
Sales Representative
Actions and results
Businesses who have deployed AI for quote pricing are seeing up to 8% improvement in quote conversion and up to 4% improvement in profits, with a significant number of days saved in list price maintenance.
Real-time visibility of quotes across your sales network, at all stages in the pricing and quoting process:
Review current quotes and update their status
Integrate directly with your own systems
Key users
Sales Manager
Pricing Manager
Pricing Analyst
Salesperson
Channel Sales Specialist
Business Development Manager
Field Sales Representative
Territory Sales Representative
Sales Representative
Actions and results
Businesses who have deployed AI for quote pricing are seeing up to 8% improvement in quote conversion and up to 4% improvement in profits, with a significant number of days saved in list price maintenance.
Real-time visibility of quotes across your sales network, at all stages in the pricing and quoting process:
Review current quotes and update their status
Integrate directly with your own systems
Quote pricing FAQs
How AI quote pricing works, what data it needs, and how it fits with the systems your sales team already runs.
AI quote pricing uses machine learning models to recommend the price most likely to win a deal while protecting margin. The models analyze historical quotes, customer history, basket mix, competitor pricing, and market conditions, then surface a margin-balanced recommendation inside the sales rep's CRM or pricing tool.
Unlike traditional pricing tools that apply static rules and discount tables, AI quote pricing learns continuously from won and lost quotes and adapts to inflation, inventory shifts, and changing customer behavior. The result is faster quote response, more consistent pricing decisions, and less reliance on discretionary discounting.
Pricing recommendations stay under human control. Sales reps review and approve every quote, and guardrails keep recommendations within approved business rules.
Manual quoting workflows force sales teams to balance speed and accuracy without the data to make confident decisions. Under pressure to hit targets, reps over-discount, and inconsistent pricing erodes margin across the customer base.
AI quote pricing reverses both problems. Pricing recommendations are calibrated to the specific customer, product, region, and market conditions, so reps quote with confidence in seconds. Discount guardrails apply automatically, so the floor on every quote is protected. Approval thresholds route exceptions to the right approver, so margin-eroding deals get visibility before they go out.
Across UiPath customers, this combination has driven measurable improvements in quote conversion and reductions in discretionary discounting.
Quote pricing recommendations are delivered directly into the sales rep's working environment, not a separate dashboard. UiPath connects to CRM platforms, pricing and quoting tools, ERP systems (including SAP), and historical quote stores.
Automation handles the data work: pulling RFQs from email and customer portals, structuring the data, and writing it into the system the sales team uses. The AI model generates a pricing recommendation. The sales rep reviews, edits, and sends.
Connectors are built into the UiPath Platform, so deployments stand up without custom integration work for every system in the stack.
AI quote pricing models need three categories of data to generate accurate recommendations:
Historical quote data. Past RFQs, the pricing decisions made, and the win or loss outcome. The richer the history, the faster the model calibrates.
Customer and product data. Customer profile, account value, purchase history, product catalog, and basket mix. These power conversion probability and elasticity scoring.
Operational data. Current inventory, logistics constraints, competitor pricing where available, and any market signals that should influence the recommendation.
Most manufacturers already hold this data in CRM, pricing, ERP, and historical quote systems. UiPath orchestrates the ingestion and structuring of the data, so onboarding focuses on calibration, not migration.
Every pricing recommendation is a suggestion, not an instruction. Sales reps review the recommended price, the supporting reasoning, and the underlying data before sending the quote. They can accept the recommendation, adjust it within configured guardrails, or escalate for approval if the deal sits outside thresholds.
The UiPath AI Trust Layer applies deterministic guardrails to every AI-driven decision. Approval workflows route exceptions to the right reviewer. Audit logs capture every recommendation, every override, and every approval, so commercial leaders can see what the system is doing and why.
This is the model behind the broader UiPath agentic platform: agents, robots, and people, with the system orchestrating the work and people staying in charge of every consequential decision.
