INDUSTRY // ENTERPRISE AI & SAAS
AI products that survive the enterprise pilot, not just the demo.
We took a vibe-coded prototype to enterprise pilot-ready in two weeks, split a GPU-heavy ML monolith into services that deploy without a war room, and owned QA across a seven-microservice enterprise platform for a year and a half. The pattern: enterprise buyers probe every path the demo skipped.
Book a thirty-minute call“The client wanted UI polish. We rebuilt the foundations first, because UI shine does not survive an enterprise demo if the product underneath is fragile.”
What we build in this industry
Three engagements define our enterprise AI and SaaS work.
Twinsoft AI builds enterprise AI twins. They came to us with a vibe-coded prototype and enterprise pilot calls already on the calendar. We kept the working logic, rewrote the spine, and delivered a pilot-ready MVP in two weeks without compromising the UI bar an enterprise room expects.
Hyperstate AI ran an AI-assisted music production platform on a GPU-heavy monolith. We broke it into orchestrated services and swapped self-hosted ML libraries for scalable alternatives. Latency down, bill down, deployments boring. The startup later ran out of funding after launch, and the case study says exactly that.
Polity is an enterprise platform on seven microservices. We owned QA across all of them and multiple vendors for 1.5 years. Hundreds of bugs closed, release gates held through every refactor.
Prototype-to-production hardening
You have a vibe-coded or AI-generated prototype and a real buyer about to look at it. We triage what to keep, rebuild what will not survive scrutiny, and ship on the deadline.
AI architecture & cost surgery
LLM and GPU bills that grow faster than revenue, latency that embarrasses the demo. We re-architect for cost and speed, the Hyperstate shape.
Long-run QA ownership
Enterprise platforms with multiple vendors need someone accountable for quality across all of them. We have held that seat for 1.5 years straight.
What makes this industry hard
Enterprise buyers probe the unhappy path
A pilot call is an adversarial code review with a budget attached. Authentication edge cases, permissions, data isolation, what happens when the model is wrong. That is what we harden first.
AI demos rot into AI liabilities
Most AI agent projects get cancelled before production, and the cause is usually architecture, evaluation, and cost, not model quality. We have written publicly about why, and we build to avoid it.
LLM costs are an architecture decision
Token bills, GPU hosting, and evaluation pipelines decide your margin. We design the model layer like infrastructure, with budgets, fallbacks, and a measured reason for every model choice.
Shipped work in this industry
Three engagements: a two-week hardening sprint, a deep re-architecture, and 1.5 years of QA ownership.
Took a vibe-coded prototype to enterprise pilot-ready, no shortcuts.
1.5 years owning QA across seven services and multiple vendors. Quality stayed shippable through every refactor.
Split a GPU-heavy monolith into orchestrated services and swapped self-hosted ML libs for scalable alternatives. Latency and cost …