Overview
Add AI to an existing product without rewriting it: a focused capability, wired into your current stack and measured against a real baseline.
Teams are told to add AI, then discover the hard part is not the model. It is retrieval, evaluation, cost control and failure behaviour inside a system that already exists.
How Devyst Approaches It
We pick one capability with a measurable outcome, build it against your live data, and put cost and quality monitoring around it before it reaches users.
What Gets Delivered
Engagement Process
- 01
Pick the capability
One use case with a number attached, so success is not a matter of opinion.
- 02
Prototype against real data
Built on your actual data, because that is where the difficulty always is.
- 03
Integrate and monitor
Shipped with cost, latency and quality tracked from the first day.
Use Cases
Semantic search
Retail & EcommerceSearch that understands intent, over a catalogue or document library.
Summarisation in product
HealthcareLong records condensed for the person who has thirty seconds to read them.
Frequently Asked Questions
Whichever fits the task, cost and privacy constraints. We build behind an abstraction so the choice can change without a rewrite.
Caching, right sized models per task, and hard budget alerts. Cost is a design constraint from the start, not a surprise later.