Expansion
AI Renewal Agent
Runs the renewal cadence, prepares renewal summaries and flags risk early.
Expansion
AI Cross-Sell Agent
Matches existing customers to complementary products based on what they already use and buy.
Customers who buy one of your products rarely hear about the others unless a rep happens to mention it. This agent looks at what each existing customer already uses and buys, matches that pattern to complementary products in your catalog, and drafts a specific recommendation for your team to approve. Customers hear about what genuinely fits them, not a blanket email offering everything.
- Complementary products matched to each customer's real purchases
- Specific, on-brand recommendations drafted for review
- No outbound message sent without a person's approval
$3,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Cross-Sell Agent looks at what each existing customer already uses and has purchased, matches that pattern to complementary products in your catalog, and drafts a tailored recommendation for your team to approve. Pairing and eligibility rules are yours to set, and nothing is sent without a person signing it off.
Why businesses buy it
Most businesses sell far less to existing customers than they could, simply because nobody connects the dots. Product pairings live in experienced reps' heads, and generic promotions get ignored. This agent applies your pairing rules to every customer's real history and drafts recommendations that feel personal and useful. Average order value grows without extra acquisition spend, and customers feel understood. The pairing and eligibility rules, data sources and approval step are written into the scope before you order, so you decide exactly what is offered and to whom.
Problems this solves
Customers buy one product and never hear about the rest
The agent looks at what each customer already uses and has bought, then matches it to complementary products in your catalog. It drafts a tailored recommendation for your team.
Cross-sell suggestions feel random and irrelevant to the buyer
Pairing and eligibility rules are yours to set, so suggestions only pair products that genuinely belong together. Customers who fall outside your rules are simply left out.
Nobody clearly owns the job of spotting cross-sell openings
Up to six workflows run with your approval rules, so the search happens every time. A person signs off each recommendation, so ownership is clear and nothing goes out unchecked.
A good fit if
You sell more than one product or service, and customers who buy one rarely hear about the others unless a rep happens to mention it.
You can describe which products pair well together, even if that pairing lives in a rep's head rather than a written rule today.
You already track purchase and usage history in a system such as HubSpot, Salesforce, Zoho CRM or Pipedrive.
You want a drafted, specific recommendation waiting for review, not a blanket email offering everything to everyone.
Not the right fit if
You want to push customers toward a bigger plan or tier, not a different product. See AI Upsell Agent.
Your priority is protecting revenue at risk of churn, not growing it. See AI Renewal Agent.
You just want to see which accounts fit which product, without drafted outreach. See AI Customer Scoring.
You want cross-sell detection bundled with scoring, forecasting and call analysis in one connected system. See AI Revenue Intelligence.
How it works
- We connect to your CRM and order or usage history with read and write access, so the agent can see purchases and write drafts back.
- The agent builds a product-affinity pattern from what customers who bought one product went on to buy, using your own history, not a generic catalog rule.
- When a customer's profile matches a pattern you approved, it drafts a specific, relevant recommendation, referencing what they already use.
- The draft sits in a review queue. Nothing reaches a customer until the approver you named signs off, following the approval rules you set.
- Approved recommendations are logged against the account record, so your team can see what was suggested and when.
- We test the agent against fifty documented scenarios built from your own product catalog and customer base before handoff.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Admin or API access to the connected systems, with sandbox or test records if you have them.
- Your rules for which products pair well together, and any approval limits on what can be recommended without a manager's sign-off.
- Ten real sample conversations or emails, so drafted recommendations sound like your team, not a template.
- The name of the approver who reviews and sends each recommendation.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker who can sign off on the final workflow before launch.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified and product-pairing rules agreed.
Weeks 1–2
Affinity logic and recommendation drafting built.
Week 3
Systems connected with read and write access.
Weeks 3–4
50 documented test scenarios run against your own catalog.
Week 4
Your review and two rounds of changes.
Go-live
Live, recorded handoff session, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,900 |
| Express | 15–25 working days | $4,600 |
| Rush | 10–20 working days | $5,250 |
What you receive at handover
- AI Cross-Sell Agent configured on one channel, connected to up to two systems
- Your pairing and eligibility rules built into up to six approval workflows
- Catalog and reference material loaded, up to 100 documents
- A test report covering 50 documented scenarios
- An admin guide, a recorded handoff session and 30 days of post-launch support
Expected benefits and how to measure them
- Helps existing customers discover products they may genuinely need
- Can raise average account value over time
- Makes recommendations consistent across account managers
- Customers holding more than one product
- Count them in your CRM or billing system at the end of each quarter, starting before launch.
- Recommendation acceptance
- Each month, match approved recommendations from the agent's log against resulting orders.
- Average revenue per account
- Compare billing reports for the two quarters before launch with the two quarters after.
Before you start: Record how many customers hold more than one product. Note your current average revenue per account.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running the agent costs roughly $50 to $250 a month for AI model usage and hosting, on top of your existing CRM or billing plan.
AI usage during the build and testing is billed to your own accounts, so every provider relationship stays yours. Nothing runs through us.
Ask about running costsCustom requirements
Add a note at checkout if you need something specific. If it fits within the Scope Card it is simply included. If it goes beyond it, you get a written Change Quote within two working days, and that work starts only once you accept it, with its time added to your delivery date.
Request a custom versionKeep it running
After launch, monthly maintenance keeps recommendations in step with your catalog: monitoring, fixes, model and platform updates, a monthly health report and enough monthly hours to add a new product pairing after a catalog update. It starts after the 30 days of support and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A recommendation engine charges a monthly fee tied to catalog size | An agency quotes after a discovery phase | Ours is one fixed price, shown before you buy |
| Scope | A recommendation engine needs you to maintain the pairing rules yourself | An agency agrees scope during the project | Ours is written on the Scope Card before purchase |
| Delivery | A recommendation engine is fast to install but slow to tune well | An agency usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Approval | A recommendation engine can send offers automatically once matched | An agency build depends on what was agreed | Ours holds every recommendation for a named approver before it sends |
| Basis for the match | A recommendation engine often relies on catalog metadata alone | An agency build depends on what was agreed | Ours is built from your own customers' real purchase history |
Questions about this product
Does it send recommendations automatically?
No. Every drafted recommendation sits in a review queue and only sends once the approver you named signs off, under the approval rules you set at intake. A person reviews every message before a customer sees it, and can reject or edit the draft before it ever goes out.
How does it decide which products pair well?
From the patterns in your own purchase and usage history, combined with the pairing rules you confirm during intake. It looks at what customers who already bought one product went on to buy next, rather than a generic catalog category match that ignores your actual sales data.
How do drafted recommendations end up sounding like us?
They are written from the ten real sample conversations or emails you provide during intake, so recommendations are drafted to sound like your team and reference what the customer already owns, not a generic template pulled from a catalog description.
How much should I budget for it monthly?
Roughly $50 to $250 a month for AI model usage and hosting, on top of your existing CRM or billing plan, paid directly in your own accounts. Monthly maintenance is optional if you want ongoing changes to the pairing rules.
How soon will it be live?
The build follows a fixed schedule that starts once your catalog and systems are confirmed. The exact date is shown on this page before you buy, along with express and rush options if you need it sooner. Your catalog, pairing rules and system access are due within 72 hours of ordering, and each day late moves delivery by 3 working days.
Can I change the product pairings later?
Yes, within the two rounds of changes included in the build, and afterwards through monthly maintenance if you keep it. Adding a pairing after you launch a new product is exactly what the monthly change hours are built to cover, without a separate project.
Which systems does it write to?
Up to two systems, typically your CRM and an order or billing platform, so it can read purchase history and write the drafted recommendation back to the account record. Access is scoped to those systems only, shared by adding our team as a user or creating a key you control, never handled by email.
What if a recommendation doesn't fit a customer?
The approver reviewing the draft can reject or edit it before anything sends, so a poor match never reaches a customer. Recurring mismatches are the kind of tuning covered by the two included revision rounds, or by monthly maintenance once the initial support period ends.
Stop leaving your own catalog undiscovered.
Order AI Cross-Sell Agent and get tailored, approver-reviewed recommendations for your existing customers, on a delivery date you see before you pay.
Works well with
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AI Upsell Agent
Spots upgrade-ready, sales-owned accounts and drafts tailored upgrade offers for approval.