A demo that worked once tells you nothing about what happens on real traffic with real edge cases
We build a scored evaluation set in the first weeks, so every change to a prompt, a model or a retrieval setting shows up as a number that moved
Teams without one are shipping on instinct and usually find out from a customer
Anything that touches your systems runs through typed tools with narrow permissions and caps on spend, steps and side effects
Failures retry and resume rather than half-writing a record, and anything unresolved stops and asks
That is what makes it defensible to point at production rather than at a sandbox
Most AI features die on unit economics rather than on quality
We instrument where tokens actually go, then cut with caching, routing and context trimming
You get budgets, dashboards and a number you can put in a business case before the invoice arrives
Froggik
"DESH Team maintained effective communication throughout the project."
Thanks to DESH Team's work, the client saw increased product recognition within the cryptocurrency community. The team managed the...
Viktoriia Bernatska
Co-Founder
ChainCrafters
"I liked their corporate policy and how they turned to customers and their wishes."
DESH Team delivered the project on time, effectively improving the site's UX and flow. The team took the time to understand the cl...
Kolya Vovkun
CEO, Founder
Dropshipping
"I really like how they treat their clients."
DESH Team successfully completed all deliverables; the branding was a great fit for the client's company, and the website was done...
Tetyana Yarchak
CEO
Both, and the second is more common. Adding a feature to a live product means working inside your codebase, your review process and your deployment pipeline, which is usually the faster path to something users touch. Building from scratch starts with feasibility and a data readiness check, because that is where these projects most often turn out to be impossible.
Whichever wins on your evaluation set at your price point. We build model-agnostic so switching is a configuration change, because the ranking between providers has moved several times a year since 2023. In practice we route routine steps to a cheaper model and the hard ones to a stronger one, which cuts cost noticeably without a quality change users can detect.
Yes, with open-weight models, where data residency or volume justifies it. It is a real trade: you take on GPU capacity and operations in exchange for control and predictable cost. For regulated data it is often the only workable option, and we will model both before you decide.
Anything factual answers from retrieved content and cites what it used, and says so when retrieval returns nothing relevant. That behaviour is scored on an evaluation set built from your real questions, including ones we know it should refuse — so it is a number you can check rather than a claim in a proposal.
A focused feature inside an existing product typically ships in four to eight weeks, including the evaluation set. A full agent or retrieval system with integrations runs three to five months. Every engagement starts with scoping, so the timeline is realistic before production begins.
