An AI support agent on top of Zendesk or Intercom, without paying $1 per answer
We put an agent in front of your helpdesk. It answers what it can from your knowledge base, hands the rest to your team and writes resolutions back through the API. You keep the ticketing system and stop paying a markup on every AI resolution.
- $2,970 → $180–$350 a month at 3,000 AI answers
- No helpdesk migration
- Pays back in 10–20 months
Zendesk
Intercom
Freshdesk
Gorgias
Help Scout
Front
Intercom Fin versus your own agent, before seat costs. A 20-agent Zendesk Suite Professional deployment with Copilot runs $39,600 a year in seats alone, which is $165 per agent per month before a single resolution is billed.
Why you are overpaying
Tickets, AI agents and automated resolution.
Every vendor here splits pricing into seats plus consumption, and each one meters something different, so by design you can’t compare the price lists. We go after the consumption layer, meaning the fee per AI answer, and leave the ticketing system itself alone.
The markup
To resolve a support request, the agent retrieves the right knowledge-base content and holds a short exchange: about 20,000 input tokens with caching and 2,000 output. At current mid-tier prices that is roughly $0.06, and aggressive prompt caching brings it down toward $0.02. Intercom charges $0.99 for the same outcome, Help Scout $0.75, and Zendesk an estimated $1.20–$1.50.
- An answering agent in front of your existing helpdesk: it resolves what it can from your knowledge base and writes resolutions back through the helpdesk’s API
- The ticketing system: SLA tracking, routing rules, macros, CSAT, the omnichannel inbox, mobile apps and reporting
Why we don’t replace Zendesk
The ticketing system is a large, mature interface used by many roles, and rebuilding it rarely pays. An agent in front of your helpdesk keeps what you have already invested in it and needs no migration, while you still save the whole difference in per-resolution fees.
Build economics
Indicative ranges for a production system rather than a prototype: authentication, audit log, error handling, monitoring and a usable admin are included. Build assumes AI-assisted delivery by a senior team. Run cost covers inference, hosting, monitoring and maintenance, but not new features.
How the project goes
- 01
SaaS audit
Identify which tools have the highest cost-to-utility ratio.
- 02
The 80/20 parity
Instead of aiming for 100% feature parity, build the 20% of features your team uses 80% of the time.
- 03
Context-first architecture
Design the system around your data (vector databases + RAG) so the AI understands your jargon, clients, and history from day one, and organize it around workflows rather than apps.
- 04
An AI-native operating model
Plan governance, risk controls, human oversight, data readiness, and team structure as part of the strategy instead of leaving them for cleanup after launch.
- 05
Phased decommissioning
Run the custom tool in parallel with the SaaS for 30 days before cutting the subscription.
Cases
Here goes a project in this category: the task, what we built and the before/after figures.
Here goes a project in this category: the task, what we built and the before/after figures.
Questions about this replacement
No. The agent sits in front of your helpdesk and writes into it through the API. SLA tracking, routing, macros and reporting stay where they are.
Let’s size this for your company
Tell us which tool you pay for and roughly what volumes you handle. We’ll come back with an honest estimate and tell you if switching to another vendor makes more sense than building.
