Why one LLM isn't enough: routing prompts across models without losing control
Model quality changes every month. Here's how we design routing, fallbacks and cost caps so your product never depends on a single AI vendor.
Every D2C founder hits the same wall: ads work at small budgets, then performance collapses the moment spend goes up. When we scaled a direct-to-consumer furniture brand four times over, with monthly ad spend reaching the ₹10–20 lakh range, the difference wasn't a secret targeting hack. It was discipline about when to scale and when not to.
Fix tracking before you scale
If your ad platform says one thing and your bank account says another, you can't make good decisions. Before increasing budgets, we set up server-side conversion tracking and reconciled reported purchases against real orders every day. Only once those numbers agreed did we trust them enough to scale.
Scale on contribution margin, not ROAS
Return on ad spend ignores product cost, shipping, returns and payment fees. A 3× ROAS can be profitable for one product and loss-making for another. We calculate contribution margin per order and set a target cost per acquisition from it. Budgets go up only when that target holds.
Raise budgets in steps
Large jumps reset the platform's learning and usually spike costs. We increase budgets on winning campaigns gradually, a step at a time, and watch cost per purchase for a few days before the next step.
Feed the machine new creative
Creative fatigue is the most common reason performance drops at scale. The same audience sees the same ad too often. We run a weekly testing rhythm — new hooks, formats and offers — and retire ads as soon as frequency rises and click-through falls.
Know when to stop
Sometimes the right move is to hold or cut. If cost per purchase rises above target for several days despite fresh creative, the market may be saturated at that price point. Pause, fix the offer or landing page, and come back stronger.
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