AI strategy and readiness
We map your product, data and constraints to the AI bets worth making, and the ones worth skipping. You get a ranked roadmap, build-versus-buy calls, and a realistic view of cost, risk and team shape.
Everything in.
Nothing out.
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.
On device
- Goes in
- Camera, voice, motion, text
- Goes out
- 0 bytes to any server
From “should we?” to shipped.
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.
On-device and edge engineering
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.
Vision and multimodal products
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.
Privacy, safety and evaluation
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.
Team enablement
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.
The cloud is a great default. It’s a poor only option.
Sending every photo, recording and sensor reading to a server is easy to build. It also sets your latency, your privacy exposure and your cost per user. Here is how the two approaches compare.
| Compared on | Cloud-only AI | On-device first |
|---|---|---|
| Latency | Bound by the network and the queue. | Real-time, frame by frame, with no round-trip. |
| Privacy | Raw photos, audio and health signals leave the device. | Sensitive data is processed locally and never has to be uploaded. |
| Cost | Every interaction is a metered call, so margins shrink as usage grows. | Inference runs on hardware the user already owns. |
| Offline | No signal, no product. | Works on a train, in a clinic, or abroad. |
Most products need both. The craft is deciding what runs where, and making the handoff invisible.
Where on-device AI pays off most.
Health and wellness
Sensitive signals, kept on the phone
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.
Real-time vision and AR
Answers at the speed of the camera
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.
Creative and personal tools
Tools that learn you, locally
Generative features that learn your voice, your photographic eye or your learning history, on the device. Drafting, editing, remixing and tutoring that feel like yours rather than the average of a global dataset, with nothing uploaded to get there.
Two apps we ship ourselves.
We build our own privacy-first apps, so the approach we recommend is the one we use. Everything below runs on the device.
iOS · NutritionAI Smart Plates
Eat smarter, not harder.
An AI food analysis tracker that respects your health and your privacy. Nutrition insight powered entirely by on-device AI.
- Easy AI food analysisSnap a photo and get nutritional insights in seconds using Neural Engine technology.
- Privacy firstAll image analysis happens on your device. Your photos never leave your phone. Read the privacy policy.
- Personal nutrition coachActionable advice based on your consumption trends over time.
- Visual food diaryA calendar-based history view to track your journey.
- Mindful eatingPause and appreciate your food before each meal.
- Works offlineNo internet connection required for AI analysis.
iOS · ProductivityAI Private Notes
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.
- Hybrid AI engineCombines Google Gemma 3 and Apple Foundation Models for stronger text understanding.
- 100% on-deviceFrom transcription to analysis, everything happens locally on your iPhone. Read the privacy policy.
- Visual notesAttach photos to recordings, like a snapped whiteboard during a meeting.
- Smart transcriptionAutomatic speaker detection and separation.
- Deep searchFind any spoken word across your entire library instantly.
- Pro exportGenerate PDF reports with transcripts, summaries and photos.
Small steps, real evidence.
Each phase ends with something you can run, measure and decide on, not a slide deck about what might work.
- Step 1
Frame
Define the user outcome, the constraints (device, data, budget) and what “good” means in numbers.
- Step 2
Prove
Build the thinnest working prototype on real hardware and test it against real inputs. Kill or continue, on evidence.
- Step 3
Ship
Harden for production: performance, evaluation, fallbacks, privacy review, release and monitoring.
- Step 4
Hand over
Documentation, training and clean ownership so your team can iterate without us.
Ways to work together.
Scope and pricing are set after a short call. We’d rather quote the right thing than the biggest thing.
| Engagement | Best for | Shape | You get |
|---|---|---|---|
| Clarity sprint | A hard question to settle before you commit: feasibility, architecture, vendor choice, or a second opinion on a plan. | Short, fixed scope. | Written recommendation, prioritised roadmap, risk and cost model. |
| Prototype to production | Getting a real AI feature working on real devices, evaluated and ready to release. | Built with your team, against agreed milestones. | Working app or SDK, evaluation suite, release support. |
| Fractional AI lead | Ongoing senior guidance without a full-time hire. | A standing cadence, a few days a month or more. | Architecture reviews, hiring and vendor support, team mentoring, board-level briefings. |
What’s moving in applied AI.
Automated briefs tracking AI in health, education and finance, each with an AI-generated sentiment score.
AI in HealthTech
- AI is increasingly deployed to automate administrative burdens and clinical documentation across the UK's NHS and global healthcare systems.
- Diagnostic AI is seeing rapid growth, particularly in radiology and cardiology, with new FDA-cleared tools entering the market.
- A strong correlation exists between a hospital's telehealth scale and its readiness to adopt clinical AI.
- Significant security vulnerabilities persist, highlighted by major data breaches impacting millions of patients via third-party AI vendors.
- Regulatory bodies are shifting toward formal assessment frameworks to ensure AI enhances rather than replaces human decision-making.
- New market entrants and pivots, including generative AI firms moving into medical hardware, signal a broadening of the healthtech ecosystem.
AI in EdTech
- The industry is shifting from traditional online content libraries toward AI-native platforms that provide genuine one-on-one tutoring at scale.
- A significant gap persists between the rapid adoption of AI tools by students and teachers and the lack of formalized institutional strategies.
- Governments are increasingly intervening to ensure AI reduces educational disparities, notably through targeted programs for disadvantaged youth.
- New frameworks from the EU and OECD emphasize AI literacy as a multidisciplinary competence rather than mere technical proficiency.
- Concerns over cognitive offloading and academic integrity are driving a new market for authenticity layers and AI-detection tools.
- Corporate initiatives are expanding to address workforce displacement through large-scale AI retraining and certification programs.
AI in FinTech
- Fintech is transitioning from assistive AI to "Agentic AI" capable of autonomous financial execution and multi-step workflow management.
- Banks are increasingly deploying a tripartite AI strategy consisting of generative, predictive, and agentic systems to drive operational efficiency.
- Regulatory bodies, including the Monetary Authority of Singapore, are developing real-time safeguards to manage the risks of autonomous AI agents.
- Data fragmentation remains a primary barrier preventing AI pilots from reaching full production in sectors such as insurance.
- The rise of token-based billing is creating a new category of untracked operational costs that legacy ERP systems cannot manage.
- Some institutions are restructuring workforces and cutting roles to prioritize investment in AI-driven simplification and agility.
Briefs are generated automatically from public news and may contain errors. Scores are AI-generated and are not investment advice.
A small team that has shipped this before.
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.
- Private by design
- If a feature can work without the data leaving the device, it should.
- Evidence over enthusiasm
- We measure on real devices with real inputs, and say so when an idea doesn’t hold up.
- Boring where it counts
- Predictable latency, graceful failure, clear ownership. The magic is in the user’s experience, not our architecture.
- Leave you stronger
- Success is your team running without us.
Questions we get asked.
Do we have to go fully on-device?
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.
Can small on-device models really match large cloud ones?
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.
What do you need from us to get started?
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.
Who owns the code and models?
You do. Work product and trained artefacts are yours, and we document them so your team can maintain them.
Do you work with regulated industries?
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.
How much does an engagement cost?
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.
How long does a typical project take?
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.
Which platforms and frameworks do you work with?
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.
Will you build on our existing app and team?
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.
How do you measure whether the AI is actually working?
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.
Can you help if we’re already using cloud AI APIs?
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.
How do you handle our data and confidentiality?
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.
Do you only work on mobile and vision?
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.
Do you offer training or workshops for our team?
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.
What happens after launch?
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.
Describe 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.”