VedereAI is an AI consultancy for teams building private, real-time products. We help you decide where AI belongs, prove it on real devices, and ship it running on the phone, the camera or the edge, not on a rented server.
One small senior team across the whole path: the strategy conversation, the engineering, and the uncomfortable questions about privacy and cost that usually come too late.
We map your product, data and constraints to the AI bets worth making, and the ones worth skipping. You get a ranked roadmap, build-vs-buy calls, and a realistic view of cost, risk and team shape.
We take models from notebook to phone: compression, quantization, runtime selection, battery and thermal budgets, and graceful fallbacks. Built for iOS, Android and embedded targets.
Camera-first experiences that understand what they see: recognition, scene understanding, AR overlays, accessibility narration and visual search, running in real time without a network round-trip.
We design systems so sensitive data never has to leave the device, then test them properly: evaluation suites, red-teaming and documentation your legal, security and compliance teams can actually use.
We pair with your engineers, run focused workshops, and leave behind the code, the playbooks and the instincts so the capability stays after we leave.
Most products need both. The craft is deciding what runs where, and making the handoff invisible.
Three domains where a phone’s sensors and a small, well-tuned model beat a giant remote one.
Phones already carry cameras, microphones, motion sensors and sometimes ECG or SpO₂. Processing that locally means patterns in sleep, posture, breathing or nutrition can be surfaced without sending the most sensitive data you own to anyone. Think meal-photo logging that supports diabetes management, or movement coaching that never uploads a frame.
Point the camera and get an answer now: a street sign translated, a part identified, a scene described aloud for someone who can’t see it. Removing the network from the loop makes these experiences feel native, and makes them work where connectivity doesn’t.
Generative features that learn your voice, your photographic eye or your learning history, locally. Drafting, editing, remixing and tutoring that feel like yours rather than the average of a global dataset, with nothing uploaded to get there.
We build our own privacy-first apps, so the approach we recommend is the one we ship. Everything below runs on the device. Your data never leaves your phone.
Eat smarter, not harder.
An AI food analysis tracker that respects your health and your privacy. Intelligent nutrition insight, powered entirely by on-device AI.
Capture more than just audio.
An AI-powered workspace for your voice. Turn meetings, lectures and ideas into structured, searchable notes using hybrid AI, entirely offline.
Each phase ends with something you can run, measure and decide on, not a slide deck about what might work.
Define the user outcome, the constraints (device, data, budget) and what “good” means in numbers.
Build the thinnest working prototype on real hardware and test it against real inputs. Kill or continue, on evidence.
Harden for production: performance, evaluation, fallbacks, privacy review, release and monitoring.
Documentation, training and clean ownership so your team can iterate without us.
Scope and pricing are set after a short call. We’d rather quote the right thing than the biggest thing.
A short, fixed-scope engagement to answer a hard question: feasibility, architecture, vendor choice, or a second opinion on a plan.
We design and build the AI capability with your team: a working feature on real devices, evaluated and ready to release.
Ongoing senior guidance: architecture reviews, hiring, vendor calls and unblocking, a few days a month or more.
Automated briefs tracking AI in health, education and finance, scored for sentiment. A quick read on where the market is heading.
Briefs are generated automatically from public news and may contain errors. Scores are AI-generated and are not investment advice.
We believe the next wave of AI won’t live only in distant data centres. It will live in the device in your pocket, and feel personal because of it.
VedereAI was founded by engineers and product people with backgrounds in large-scale consumer technology, and a focus on edge AI, mobile optimisation and model deployment. We take on a few engagements at a time so the people you meet in the first call are the people doing the work.
“Vedere” is Italian for to see. It’s a reminder of how we work: look at the real constraint, the real user and the real data before recommending anything.
No. Most good products are hybrid. We help you decide which tasks run locally, which go to the cloud, and how to switch between them without the user noticing.
For narrow, well-defined tasks (recognition, classification, extraction, transcription, personalised suggestions) often yes, once a model is well-tuned and compressed. For open-ended reasoning, a cloud model may still be the right tool. We’ll tell you which is which for your case.
A description of the product or problem, any constraints you already know (devices, regulation, budget) and ideally a sample of the data or user flows involved. A short call is enough to scope the rest.
You do. Work product and trained artefacts are yours, and we document them so your team can maintain them.
Yes, health and finance are core areas for us. We design with data minimisation in mind and produce documentation for your compliance team, though we’re engineers rather than your legal counsel.
It depends on scope, so we quote after a short call. A clarity sprint is a small, fixed-scope engagement. Build projects are priced against a defined outcome, and fractional leadership is a monthly retainer. We’ll tell you plainly if a smaller or cheaper route will do.
A clarity sprint usually takes a few weeks. A working prototype on real devices often takes one to three months, and taking it to production depends on your release process and the quality bar you need. We agree milestones up front so you can see progress throughout.
iOS and Android are our main targets, along with embedded and edge hardware. On the model side we work with Core ML, TensorFlow Lite, ONNX Runtime and similar runtimes, and with both open-weight and commercial models. We choose by what fits your product, not by preference.
Yes. We usually work inside your codebase and alongside your engineers, so what we build fits your architecture and your team can maintain it. We can also build a standalone prototype or SDK if that suits you better.
We agree success metrics in the first phase, such as accuracy on your real data, latency, battery use, model size and user outcomes. We then build an evaluation suite that runs on real devices, so progress is measured against evidence, not demos.
Yes. A common starting point is reviewing what you run in the cloud today and moving suitable tasks on-device to cut latency, cost or privacy exposure. Often the best result is a hybrid, with the cloud kept for the tasks that really need it.
We work under an NDA from the first conversation and ask only for the data we need. Wherever possible we work with samples or synthetic data, and we agree in writing how any data is stored, used and deleted.
That’s where we go deepest, but the thinking applies more widely: speech, text, sensor data and privacy-sensitive workflows. If you’re unsure whether your problem is a fit, send us a short note and we’ll say so honestly.
Yes. We run focused workshops on topics like on-device AI, evaluation and AI product strategy, and pair with your engineers on real work so the skills stay with your team.
We can stay involved for monitoring, model updates and performance tuning, or hand over cleanly with documentation and training. Many clients keep us on a light retainer for the first few months after release.
Tell us what you’re building or considering. We’ll reply with honest thoughts on fit, approach and next steps, even if the answer is “you don’t need us yet.”
