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
Our enterprise AI and SaaS cases cover product engineering, architecture and assurance as distinct responsibilities.
Twinsoft AI builds enterprise AI twins. They came to us with vibe-coded software and enterprise pilot calls already on the calendar. We kept the working logic, rewrote the spine with proper AI integration, 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. Our software quality assurance owned all of them across multiple vendors for 1.5 years. Hundreds of bugs closed, release gates held through every refactor.
For Aniron, Wavect was the main contributor to the application behind the public landing page. The developer portal covers onboarding, API-key management, credit top-ups and usage visibility, with supporting integrations, tests and delivery tooling.
Cedars: Wavect created documentation structures, reviewed software and AI use, and delivered prioritized assessment reporting for technical and management teams.
For how we run an enterprise engagement end to end, from procurement questions to delivery, see enterprise software delivery.
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.
Fractional CPO for post-PMF SaaS
When a SaaS product is shipping whatever the loudest customer asked for last week, a fractional CPO installs a prioritisation framework and a discovery cadence. Post-PMF only, and only when the founder is ready to hand over the product call.
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
Five engagements spanning AI product delivery, enterprise QA, developer applications and software assessment.
These are selected projects, not our full portfolio. We have shipped 100+ products since 2018.