--- title: "Enterprise AI & SaaS Development" canonical: https://wavect.io/industries/enterprise-ai-saas/ language: en description: "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 …" image: "https://wavect.io/img/general/bak/open_graph_preview.jpg" --- 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. 3 case studies 3 questions answered 4 related services Shipped work > “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.” TL;DR Vibe-coded prototypes hardened to enterprise pilot-ready in weeks, GPU monoliths split into boring deployments, and 1.5 years of QA ownership across seven microservices. // 01 ## What we build in this industry Three engagements define our enterprise AI and SaaS work. [Twinsoft AI](/case-studies/twinsoft-ai/) builds enterprise AI twins. They came to us with [vibe-coded software](/glossary/vibe-coded-software/) and enterprise pilot calls already on the calendar. We kept the working logic, rewrote the spine with proper [AI integration](/services/artificial-intelligence/), and delivered a pilot-ready MVP in two weeks without compromising the UI bar an enterprise room expects. [Hyperstate AI](/case-studies/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](/case-studies/polity/) is an enterprise platform on seven [microservices](/glossary/microservices/). Our [software quality assurance](/services/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 how we run an enterprise engagement end to end, from procurement questions to delivery, see [enterprise software delivery](/enterprise/). ### 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. // 02 ## 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. Regulatory reality Selling AI into EU enterprises means your buyers will ask about the [EU AI Act](/blog/eu-ai-act-compliance-cost-startup/) in procurement, and [GDPR obligations stack on top](/blog/gdpr-ai-act-stacking-dach-saas/). Having an implementable answer in the data room is a sales asset, not just a compliance cost. // 03 ## Shipped work in this industry Three engagements: a two-week hardening sprint, a deep re-architecture, and 1.5 years of QA ownership. ### [Twinsoft AI](/case-studies/twinsoft-ai/) - **Status:** Live - **Outcome:** 2 weeks - To Sellable MVP - **Summary:** Took a vibe-coded prototype to enterprise pilot-ready, no shortcuts. - **Stack:** Python / Flask, LLMs, Terraform ### [Polity](/case-studies/polity/) - **Status:** Live - **Outcome:** 7 services - Unified Testing - **Summary:** 1.5 years owning QA across seven services and multiple vendors. Quality stayed shippable through every refactor. - **Stack:** React, Java/Kotlin, Go ### [Hyperstate AI](/case-studies/hyperstate-ai/) - **Status:** Wind Down - **Outcome:** GPU Monolith - → Microservices - **Summary:** Split a GPU-heavy monolith into orchestrated services and swapped self-hosted ML libs for scalable alternatives. Latency and cost … - **Stack:** Python / Django, OpenAI API, Neo4j These are selected projects, not our full portfolio. We have shipped 75+ products since 2018. // 04 ## Related services & reading - [SERVICE AI Integration](/services/artificial-intelligence/) - [SERVICE Software Development](/services/software-development/) - [SERVICE Software Quality Assurance](/services/software-quality-assurance/) - [SERVICE AI Enablement](/services/ai-enablement/) - [ARTICLE Why AI agent projects get cancelled](/blog/why-ai-agent-projects-get-cancelled/) - [ARTICLE RAG production readiness checklist for the EU](/blog/rag-production-readiness-checklist-eu/) - [ARTICLE LLM evaluation cost and ROI in production](/blog/llm-evaluation-cost-roi-production/) - [GUIDE How to choose a software development agency](/software-development-guide/how-to-choose-a-software-agency/) // 05 ## FAQs ### Our prototype was built with AI tools. Is that a problem? No, it is normal now. The logic is often right and the structure is often not production-defensible. The fastest path is rarely a full rewrite: we triage, refactor the demo path first, and rebuild the rest properly. ### Can you make our LLM bill smaller? Usually, yes. Model routing, caching, evaluation-driven model choice, and replacing self-hosted GPU workloads where managed alternatives are cheaper. The Hyperstate engagement cut latency and cost in the same move. ### Do you build AI agents? Yes, and we are openly skeptical about most agent architectures we see, because most agent projects die before production. We build the boring version that ships: scoped agents, hard evaluation, costs measured from day one. 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