8 Pricing and Quotes AI Agents, Live in Production.
8 live agents automate pricing and quotes within sales. 1 runs on a schedule, 5 run on demand and 2 run when a message arrives. Each one publishes the inputs it needs, the steps it works through and what it hands back.
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Pricing Exception Remediation
Upload quotes flagged as pricing exceptions and get each one explained, classified, and routed — separating genuine one-offs from a pattern nobody has fixed.
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Pricing Policy Compliance
Check a proposed price against your pricing policy — margin floors, discount authority, and the rules that apply to this customer's sector — before it is quoted.
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Pricing Scenario Planner
Describe a deal and get three defensible pricing scenarios compared side by side, with the margin maths, the risks in each, and a recommendation.
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Quote Builder
Describe what the customer wants and get a priced quote built from your own price book and discount policy, validated against that policy before it leaves your hands.
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Quote Discrepancy Resolution
Compare what was signed against what was approved and get every difference found — line by line, with the ones that change the money separated from the ones that do not.
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Quote Intake Completeness
Check a quote request for everything needed to price it, and get the questions that close the gaps drafted for the requester.
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Quote Request Qualification
Paste an inbound quote request and get a go/no-go decision against your qualification rules, with what is missing listed and a reply drafted either way.
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Quote Status Sync
Find quotes whose status disagrees across CRM, e-signature, and finance — the signed-but-unbilled, the expired-but-open, the billed-but-unsigned.
Encoding pricing policy so speed does not cost margin
Pricing breaks when complexity outstrips a person’s ability to apply rules consistently under time pressure. Reps navigate price books, discount ladders, approval matrices and sector-specific rules across several tools, and each quote becomes a small judgement call made quickly. Margin leakage rarely comes from reckless discounting; it comes from reasonable concessions granted without visibility into profitability or precedent. At the same time, slow quote turnaround hands competitors the opportunity to frame the buyer’s decision and pushes sellers into last-minute discounting to recover. The organisation ends up with the worst combination available: it moves too slowly and prices too loosely, and the two problems reinforce each other.
The agents here make speed and control the same mechanism rather than a trade-off. Quote Intake Completeness checks a request for everything needed to price it and drafts the questions that close the gaps, so the cycle does not start on assumptions. Quote Request Qualification applies a go/no-go against your qualification rules with a reply drafted either way. Quote Builder then assembles a priced quote from your own price book and discount policy and validates it against that policy before it leaves your hands, while Pricing Policy Compliance tests a proposed price against margin floors, discount authority and sector rules and names the approvals needed. Where the deal warrants analysis, Pricing Scenario Planner compares three defensible scenarios with the margin arithmetic, the risks in each and a recommendation. After signature, Quote Discrepancy Resolution compares the signed contract against the approved quote and escalates before invoicing, and Quote Status Sync finds quotes whose status disagrees across CRM, e-signature and finance — the signed-but-unbilled and the billed-but-unsigned.
What this moves
- Quote turnaround time
- Quotes are assembled from the price book and validated against policy in one pass, removing the manual build and the wait for a compliance opinion.
- Margin percentage
- Discount authority and margin floors are checked before a price is quoted rather than discovered afterwards, so concessions require an explicit approval.
- Billing disputes from quote errors
- Signed contracts are compared line by line against what was approved, separating differences that change the money from those that do not.
Sales
How AI agents handle pricing and quotes
Drawn from the 8 agents above — what they require, how they run, and what comes back.
What they need
- Flagged quotes
- Remediation settings
- The proposed price
- The deal
- Pricing constraints
- What the customer wants
- Quote details
- Approved quote
What comes back
- Remediation note
- Each exception
- Exception types
- Not a one-off
- Can this be quoted?
- What to do
- Line by line
- Approvals needed
How they run
- Run on demand
- 5
- Triggered by an incoming message
- 2
- Runs on a schedule
- 1
- Steps per run
- 2–5
- Credits per run
- 5–7
- Use a knowledge base
- 2
Where pricing and quotes fits in sales
Most of a rep’s day goes on looking things up, updating records and writing follow-ups. These agents do that part, so the hours left over go into conversations with buyers.
Next Step
Deploying pricing and quotes agents
These run as-is against the inputs listed above. Most deployments adapt one — a different source system, a different tolerance, a different approval path. The first call establishes which.