Neurova AI · Solutions
Consumer and lifestyle apps, internal tools, document pipelines, vision and OCR systems, integrations between software you already pay for. If it involves a model and has to work in production, it's in scope.
The named solutions on this site (receptionists, booking, knowledge assistants, health software) are simply the things we have built often enough to describe in advance. They are not the limit of what is possible, and a good number of the most useful projects do not fit any of them.
This page is for those. If it involves a model and it has to work on a Monday morning when nobody is watching, it is in scope.
Consumer and lifestyle applications. Full products with AI inside them rather than AI features bolted onto something else. Installable web apps that work offline and lock behind biometrics, with the AI doing something genuinely useful rather than sitting in a chat bubble in the corner.
Document pipelines. Getting structured, trustworthy data out of PDFs, scans and forms, with OCR, a confidence check on every extraction, and a review queue for the ones that fall below the line. The confidence check is the part that makes it usable; extraction without it just moves the error somewhere less visible.
Vision and recognition. Photo and barcode recognition, classification, quality checks. Usually paired with a reference database so the answer is grounded in something real rather than inferred.
Internal tools. The unglamorous ones that save an hour a day: drafting, summarising, triaging an inbox, turning a meeting into a structured record, flagging the exception in a list of five hundred rows that a person should actually look at.
Integrations and agents. Connecting the software you already pay for, so a thing that happens in one system causes the right thing in another. Where an agent takes actions rather than just answering, we scope its permissions narrowly and log everything it does.
Production, not pilots. A demo that works on a curated example is not the hard part and never has been. What takes the time is the long tail: the malformed input, the timeout, the model returning something structurally wrong, the day the provider has an outage. Everything we ship has an answer for those, because that is where these projects actually fail.
Deterministic where it should be. Language models are excellent at language and poor at arithmetic, scheduling and rules. We use them for the former and ordinary code for the latter. A surprising amount of what gets sold as AI should be a database query with good validation.
Evaluated, not vibed. We build a test set from your real cases and measure against it, so "is it good enough" is a question with an answer rather than an opinion.
Yours at the end. Your repository, your cloud account, your data. We will run it for you if you want that, and hand over the keys and the documentation if you do not.
It depends on what the data is, and we start there rather than with a platform preference. Public or low-sensitivity material can go to a managed European API without much ceremony. Personal data raises the bar. Health data, legal files and anything under professional confidentiality often should not leave infrastructure you control, and open-weight models running on your own hardware are a genuinely viable answer, slightly behind the frontier on the hardest reasoning, and we will tell you where that trade-off bites before you commit.
Describe the problem and what you think you want built, even roughly. We will come back with a written scope, a fixed price and a date, and if we think the answer is not AI at all, or that something off the shelf will do it for a fraction of the cost, we will tell you that instead.
What AI work do you actually take on? Applications rather than research. We build things that run in production and that somebody depends on: document pipelines, extraction and classification, vision and OCR, retrieval systems, agents that take actions in real tools, and full consumer applications with AI inside them. We do not train foundation models and we will not pretend to.
How do projects start? With a conversation about the problem rather than the technology. Then a written scope with a fixed price and a date, at no charge. Then weekly demos on your real data, so you can see it working (or not working) every week rather than at the end.
How long does it take? A focused pilot is typically two to four weeks. Production, with the monitoring and fallbacks and edge cases that production actually means, is usually four to twelve or more depending on what it integrates with. We give you a date in the scope and we tell you early if it moves.
What do you need from us? A clear problem statement, access to whatever data is involved, and one person who can make decisions quickly. The last of those matters more than people expect: projects rarely stall on engineering, they stall waiting for an answer.
What happens when the model gets something wrong? It will, so that is a design question rather than an accident. Every system we ship has an answer to it: a confidence threshold, a human review step, a deterministic fallback, or a clear failure message. Anything that cannot say "I do not know" will eventually say something worse.
Do we own what you build? Yes. Your code, your data, your infrastructure. No per-seat licence, no lock-in, and no hostage situation if you decide to take it in-house or hand it to someone else.
Describe the problem and what you think you want built. We'll come back with a written scope, a fixed price and a date.