AI that answers customer emails in your tone.
- Customer emails
- Quotes from enquiries
- Invoices into your books
- Call summaries
- Overdue invoices
- Answers from documents
You don't need an AI strategy, a data team, or a big budget to start. You need one repetitive job — the kind that eats a few hours every week — and someone to test whether AI can actually do it.
nxtinno picks that job with you, tests it on your own data within a few weeks, and builds the full system only if the test pays off.
- Plain language
- Fixed-scope experiments
- A senior engineer, not an agency
Answering customer emails
Drafts are already written when you open your inbox. You read them, adjust, send.
Digging through documents
Ask a question and get the answer out of your own contracts, manuals, and files, with a link to the page it came from.
Already built: an AI assistant over company documentsRetyping invoices
Incoming invoices are read, checked, and entered into your accounting without copy-paste.
Turning enquiries into quotes
An email enquiry becomes a draft offer with prices pulled from your own price list.
Already built: catalogue, pricing, and offer flowsWriting the weekly report
The numbers are pulled from your systems overnight and the report is waiting for you on Monday morning.
Already built: automated dashboards over meter dataHanding out access to guests and staff
A booking arrives and the door code is ready. When the stay ends, access ends with it.
Already built: bookings synced to door accessIf one of these sounds familiar, that is where we would start. Each example is sized for a small or mid-sized company — a few weeks of work, not an enterprise programme.
A worked example
Take a manufacturer that receives orders as emails with a PDF specification attached. Someone opens each one, reads the dimensions and quantities, checks nothing is missing, and types it all into the production system. Here is the same job with the typing taken out.
Today
10–20 minutes of reading, checking, and retyping for every order — and the mistakes that come with doing it at speed.
With the automation
1–2 minutes to review a draft that is already filled in, with the uncertain parts marked for you to decide.
This is an illustrative walkthrough, not a client result. The times are typical for orders handled by hand; what your company would actually save is one of the things an audit is for.
Two years ago, putting AI to work inside a small company meant a research project. Today the models are good enough, the tools are stable enough, and testing a single idea costs a few weeks of work.
That changes what a sensible first step looks like. You no longer have to decide in the abstract whether AI suits your business — you can run it against one real task and find out.
Hours lost to repetitive admin
Handled in the background
Answers buried in files and inboxes
Found in seconds
“We should look into AI”
A working pilot in weeks
Explore
A free 30-minute call. We walk through how you work today and find the places where AI could save real time or money. No preparation, no jargon.
Experiment
A small, fixed-scope pilot built on your real data in a few weeks. You see AI working on your actual problem before committing to anything bigger.
Expand
If the pilot pays off, we build it into a dependable everyday tool — deployed, secured, and maintained. If it doesn't, you have spent a few weeks and know exactly where AI does not help you.
Where most companies start
Before anyone builds anything, it is worth knowing which job is the right one. The audit is a short, fixed-scope look at how your company actually works today — and it ends with a written answer rather than a proposal.
We walk through the work
A 60–90 minute session going through how things get done today: who touches what, where the waiting happens, which steps everyone dreads.
I take three to five processes apart
End to end, including the systems involved and the parts that only exist in someone's head or a spreadsheet.
You get a shortlist, not a wish list
The three opportunities worth starting with, what each would take to build, and the ones I would leave alone for now.
Scope
One week, fixed scope. The written outline is yours either way.
What you walk away with
- a written map of how the reviewed processes run today
- the repetitive steps and handovers, named and counted
- the top three automation opportunities, ranked
- an effort estimate and the technical dependencies for each
- a short implementation outline you can act on
What it is not
- not a sales presentation with a proposal at the end
- not a commitment to build anything afterwards
- not dependent on your data being tidy or your systems being modern
AI systems and agentic workflows
Design and implementation of production-minded AI capabilities: RAG, tool use, MCP servers and clients, multi-step agents, evaluation, guardrails, observability, and human approval flows.
- RAG architecture and retrieval pipelines
- MCP integrations and tool design
- agent orchestration and workflow automation
- evaluation and observability
- secure enterprise integration
Software architecture
Architecture for platforms that need to scale technically and organisationally, with clear boundaries, APIs, data ownership, security, deployment, and operational responsibility.
- distributed and event-driven systems
- API and integration architecture
- domain and service decomposition
- cloud-native and edge architecture
- architecture reviews and modernisation plans
Product engineering
Hands-on engineering that turns an architecture into a usable product, from targeted prototypes to production services, integrations, automation, and delivery pipelines.
- Node.js and TypeScript services
- data pipelines and integrations
- cloud and Kubernetes delivery
- IoT and edge solutions
- DevOps, GitOps, and automation
AI
Assistants, RAG, and agents that earn their place in production.
Solution architecture
System boundaries, APIs, and decisions that keep platforms changeable.
Product engineering
Hands-on builds: services, integrations, and devices, shipped and owned.
Additional work is available under NDA and can be discussed privately.
Founder portrait placeholder
I'm Martin Hudec, a software architect and engineer focused on distributed systems, cloud-native platforms, APIs, integrations, IoT, automation, and practical AI.
I have spent years connecting business requirements with systems that must work under real operational constraints. With nxtinno, clients work with me directly—from the first architecture decision through implementation and launch.
I am most useful when the problem is technically complex, the boundaries are unclear, and the solution needs to be both ambitious and maintainable.
- software and solution architecture
- Node.js and TypeScript engineering
- cloud, Kubernetes, DevOps, and GitOps
- APIs, event-driven systems, and data pipelines
- IoT, edge, and systems integration
- RAG, MCP, agentic workflows, and automation
Curious what AI could actually do for your business?
Bring your questions, including the ones that feel too basic. In 30 minutes we go through how you work today, pick the task with the most to gain, and sketch what testing it would involve. No pitch, no obligation.
Prefer email? hello@nxtinno.com