We help product teams decide where AI belongs, prove it works, and ship it, running on the phone, the camera or the edge device instead of a rented server. Faster, private, and cheaper at scale.
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.
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.”