Saved by choosing My AskAI
$142,212
over 12 months, compared with building it yourself — including the build cost and the resolution-rate gap
Self-build set-up cost
$95,202
$30,000 of engineering + $65,202 of savings missed during the 2-month build, when you could already be live on My AskAI.
A self-build must hiti
69% resolution
just to break even versus My AskAI (at 75%).
Time to go live
Days vs 2 months
My AskAI deploys in days; the build answers nothing until v1 ships.
Monthly cost, side by side
| Humans only | My AskAI | Self-build | |
|---|---|---|---|
| Monthly tickets | 30,000 | 30,000 | 30,000 |
| AI resolution rate | — | 75% | 60% |
| Tickets resolved by AI | 0 | 22,500 | 18,000 |
| AI software / LLM cost | $0 | $4,899 | $600 |
| Engineering upkeep | $0 | $0 | $1,500 |
| Tickets still needing humans | 30,000 | 7,500 | 12,000 |
| Cost per human-handled ticket | $1.67 | $1.67 | $1.67 |
| Human support cost | $50,000 | $12,500 | $20,000 |
| Total monthly cost | $50,000 | $17,399 | $22,100 |
| Monthly savings vs humans only | — | $32,601 | $27,900 |
On top of this, the self-build needs 2 months and $30,000 of engineering before it answers its first ticket — My AskAI goes live in days. The headline number above includes both.
What else comes with building
None of this is priced into the numbers above.
- Maintenance, forever. Model deprecations, helpdesk API changes, knowledge-base drift, prompt regressions. The 0.2-FTE default is the steady state, not the bad month.
- Focus. The engineers on this aren't shipping your roadmap. An internal support agent becomes a product with one customer, a backlog, and an on-call rota.
- Model churn. A new frontier model lands every few months. Each swap means re-running evals, retuning prompts, and revalidating edge cases — or falling behind on quality and cost.
- Evals and guardrails. The demo is the easy 20%. Groundedness checks, hallucination containment, and QA against real tickets are the long tail that takes the months.
- The agent is only half the product. Around the AI itself sits a platform: dashboards showing what got resolved and what didn't, tools to test changes safely, spot knowledge gaps, and tune guardrails. A self-build ships the agent without the controls for managing and improving it — or you build those too.
- A widening gap. A vendor ships improvements weekly — integrations, task execution, analytics, resolution-rate gains learned across hundreds of customers. A solo build only improves when someone works on it.
