Software That
Speaks Fluent AI
Flux Nebula empowers teams with software that knows what AI is — and how to put it to work, engineered in from day one.
Full-cycle development for companies that take AI seriously.
Architecture, development, design, and QA as one system. Same team. Same context. Nothing lost on the way to production.
Team certifications
The stack we build with
Tools chosen from years of running them in production, not from trend reports.
Anthropic and OpenAI models, RAG on vector databases, and automated evals in every AI system we ship.
You’ve hired developers before.
You know how it ends.
Fragmented teams
One freelancer for the backend, another for the app, an agency for the design. Nobody owns the architecture, and every bug lands in no man’s land.
Toy AI
You were sold “AI” that was a ChatGPT wrapper: no security, no evals, no real value. It worked in the demo and died in production.
Unpredictable projects
Deadlines slip, scope creeps, budgets blow up. The three-month project is now on month twelve.
Flying blind
Vague updates, demos that never arrive. You find out about problems after they’ve already cost you weeks.
One team. The whole cycle.
One system. We call it Full-Cycle AI-Native.
When the agent closes the ticket on its own, Friday deploys stop being scary, and QA finds the bug before your customer does... that’s not a coincidence. That’s Flux Nebula.
Five services.
Zero handoffs.
Five disciplines, one team: design, code, infrastructure, and QA decisions all come from the same room.
Custom Software Development
Web systems, APIs, and internal platforms built around how your business actually runs. Architecture designed to last, on stacks like TypeScript, React, and PostgreSQL.
Applied AI
LLMs, RAG, and agents in production, with evals, guardrails, and inference costs under control. If AI adds nothing to your case, we tell you before you pay for it.
Cloud & DevOps
AWS, Linux, CI/CD, and infrastructure as code. Repeatable deploys, monitoring from day one, and environments that don’t live in someone’s head.
Why Flux Nebula?
Others ship code. We ship systems.
Software architects, senior fullstack engineers, UI/UX, and QA with careers spent in banking, logistics, healthcare, and retail systems.
From startups to enterprises. From MVPs to platforms with AI running in production.
From the first commit we work in your repo, with CI/CD and documentation. If you decide to walk away tomorrow, everything is yours.
What working with us should sound like
While we collect signed testimonials, these representative quotes show the outcomes we get hired for: dates met, systems in production, and code your team can maintain.
Delivered on the exact date they committed to — I can't say that about anyone else we've hired.
CTO, fintech
Demo every two weeks and the code in our repo from day one. I never had to ask how the project was going — I could open GitHub and see it.
IT Manager, distribution company
When the engagement ended, our internal team took over the codebase without drama. Six months later we're still building on that foundation, and the architecture decisions still make sense.
Engineering Lead, insurance company
They moved our billing off a twelve-year-old monolith with zero downtime. Our customers never noticed, which was exactly the goal.
Head of Technology, e-commerce
We had already tried an AI assistant with another firm. It looked great in the demo and fell apart in production. Flux Nebula started from our actual support tickets, built an eval suite before writing a single prompt, and walked us through the failure cases every week. What we run now answers correctly or escalates to a human — it doesn't improvise. That's the difference between a demo and a system.
Head of Customer Support, B2B SaaS
Our platform sat at 80% finished for almost a year. They audited the code in two weeks, told us exactly what was worth keeping, and rewrote only what wasn't. Four months later we were live. I wish we had called them first.
Founder, health SaaS
Their dedicated team joined our standups, worked in our tools, and picked up our conventions. After the first month I stopped thinking of them as external.
VP of Engineering, marketplace
Delivered on the exact date they committed to — I can't say that about anyone else we've hired.
CTO, fintech
Demo every two weeks and the code in our repo from day one. I never had to ask how the project was going — I could open GitHub and see it.
IT Manager, distribution company
When the engagement ended, our internal team took over the codebase without drama. Six months later we're still building on that foundation, and the architecture decisions still make sense.
Engineering Lead, insurance company
They moved our billing off a twelve-year-old monolith with zero downtime. Our customers never noticed, which was exactly the goal.
Head of Technology, e-commerce
We had already tried an AI assistant with another firm. It looked great in the demo and fell apart in production. Flux Nebula started from our actual support tickets, built an eval suite before writing a single prompt, and walked us through the failure cases every week. What we run now answers correctly or escalates to a human — it doesn't improvise. That's the difference between a demo and a system.
Head of Customer Support, B2B SaaS
Our platform sat at 80% finished for almost a year. They audited the code in two weeks, told us exactly what was worth keeping, and rewrote only what wasn't. Four months later we were live. I wish we had called them first.
Founder, health SaaS
Their dedicated team joined our standups, worked in our tools, and picked up our conventions. After the first month I stopped thinking of them as external.
VP of Engineering, marketplace
Their QA team caught a race condition in our payment flow two weeks before launch. That one bug would have cost us more than the entire engagement.
CTO, payments startup
They rebuilt our AWS infrastructure with Terraform and the monthly bill dropped by almost half. Every infrastructure change goes through a pull request now; the days of clicking around the console are over.
IT Director, retail chain
I walked in asking for AI in every feature. They talked me out of half of it and showed me where a plain rules engine was cheaper and more reliable. That's when I decided to trust the rest of their advice.
Product Director, edtech
First outside team whose code our senior engineers didn't want to rewrite.
Staff Engineer, banking
We came in with an idea and a board deadline. They pushed back on scope during the first week, which annoyed me and saved the project. Design, development, and QA were clearly one team — decisions didn't get lost between departments. We demoed a working system to the board on schedule. I've hired plenty of software firms; this was the first time the whole thing felt like a single conversation.
CEO, industrial manufacturing
When something slipped, they told us before we noticed. Sounds basic. It isn't.
Operations Manager, logistics company
They routed most of our LLM traffic to a smaller model with an automatic fallback. Our evals show the same quality; the invoice shows a much smaller number.
Technical Co-founder, legaltech
Their QA team caught a race condition in our payment flow two weeks before launch. That one bug would have cost us more than the entire engagement.
CTO, payments startup
They rebuilt our AWS infrastructure with Terraform and the monthly bill dropped by almost half. Every infrastructure change goes through a pull request now; the days of clicking around the console are over.
IT Director, retail chain
I walked in asking for AI in every feature. They talked me out of half of it and showed me where a plain rules engine was cheaper and more reliable. That's when I decided to trust the rest of their advice.
Product Director, edtech
First outside team whose code our senior engineers didn't want to rewrite.
Staff Engineer, banking
We came in with an idea and a board deadline. They pushed back on scope during the first week, which annoyed me and saved the project. Design, development, and QA were clearly one team — decisions didn't get lost between departments. We demoed a working system to the board on schedule. I've hired plenty of software firms; this was the first time the whole thing felt like a single conversation.
CEO, industrial manufacturing
When something slipped, they told us before we noticed. Sounds basic. It isn't.
Operations Manager, logistics company
They routed most of our LLM traffic to a smaller model with an automatic fallback. Our evals show the same quality; the invoice shows a much smaller number.
Technical Co-founder, legaltech
What we get asked before we start
One senior team covers the whole cycle: discovery, architecture, design, development, QA, and infrastructure, all the way to production and evolution. And AI is weighed from system design onward — applied where it adds real value, dropped where it doesn’t.
One senior team runs the entire cycle, with zero handoffs between vendors. And when we apply AI, it ships with evals, guardrails, and security — never as a ChatGPT wrapper.
Four models: turnkey project, dedicated team, ongoing support and evolution, or one-off consulting. On the first call we tell you which one fits your case and why.
You do, 100%. We work in your repository from the first commit, on a standard stack, with no proprietary frameworks designed to lock you in. IP transfer is written into the contract.
Yes — we sign an NDA before looking at a single line of your system. Role-based access, credentials that never live in the code, and work inside your own infrastructure when you require it.
It depends on the system, and we give honest ranges: a well-scoped MVP, 6 to 12 weeks; a full platform, 4 to 8 months; an AI integration on an existing system, 4 to 10 weeks. The exact number comes out of discovery.
A working demo every two weeks, a repository you can open from day one, and one direct technical contact who answers in detail — no account managers in between.
First we define where your data lives and what leaves your infrastructure. Third-party APIs receive only the minimum required, with PII redaction where it applies; sensitive data can stay entirely inside your environment.
Before production, the system passes evals and guardrails against leaks and misuse. After launch, we monitor its behavior and correct course when anything drifts.
Yes — you can bring us in for just QA, just infrastructure, or a single AI integration. The full cycle is where the value peaks, but starting with one piece is a fair way to test us.
The system moves into ongoing support and evolution: monitoring, fixes, improvements, and new features with the same team that built it. No serious system is finished on deploy day.
Tired of projects that stall at 80%?
Book 30 minutes. Bring your idea or your current system and we’ll tell you exactly how we’d take it to production.
Book a call