The model works. The product does not exist yet.
Everything between a working notebook and something customers pay for — and that gap is most of the work.
The problem
The hard part is behind you, or so it looks. The model does what it should on the data you have. What remains is "just engineering".
That remainder is serving it affordably, holding latency under real traffic, accounts and billing, the support surface, and every way the model fails in front of a customer who is paying for it.
It is most of the work, most of the cost, and where most AI projects quietly stop.
How we approach it
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Decide precisely what the model must be right about, and what it is allowed to get wrong
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Build the product around that, rather than fitting a product to the model afterwards
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Solve serving economics before scale, not after the invoice arrives
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Ship, measure against real usage, and correct
Where we have done this
Both of these are our own products, carrying our own costs and our own pager. We build AI products under the same constraints we would build yours.
TheHeartbeat.ai - AI-Powered Call Analytics SaaS
Flagship product: AI-powered call quality monitoring that analyzes 100% of customer calls in real-time.
Read the case study → Managed Infra SaaSVigilCloud - Managed Infrastructure with Accountability
Our own product: managed monitoring, security, compliance, cost, and CI/CD — 80% automated, 20% senior engineers, and the 2am page is ours.
Read the case study →The services behind this
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