Artificial Intelligence & Machine Learning
“The Model That Got a Bit Overeager”
A customer success team came to us with an issue: their model for flagging dissatisfied users was flagging too many—even happy ones. It turned out the algorithm had latched onto a few common phrases like “thanks” or “appreciate it,” which ironically appeared in both good and bad feedback. The model wasn’t broken—it just needed some tuning. We retrained it with richer context, included metadata like ticket resolution time, and layered in some simple business rules to filter false alarms.
It’s a reminder we see often: smart models still need guardrails. And context always beats correlation. At Indeses, we believe every AI journey starts with asking better questions of the data—before the model ever trains. Is it clean? Is it representative? Does it tell the whole story?
Our approach includes:
Contextual Classification Models:
We go beyond keyword matching—capturing patterns that align with real-world actions.
Human-Centered Feedback Loops:
Every rollout includes checks, toggles, and fallback paths to avoid over-automation.
Scalable Infrastructure:
Whether you're using CRM, ticketing, or custom tools, we slot into your tech stack with minimal fuss.
Business KPIs First:
We focus on models that lift real outcomes: faster closure, better NPS, fewer escalations.
When AI is quietly helpful—and not in the way—that’s a win.
Ready to Let Your CRM Do the Remembering?
Let your team focus on what they do best—selling, supporting, and delighting.
No slides. Just live screens.
Change That Makes Sense
We keep it stable. We move forward together.
