Assistants grounded in your data
Chat and search that answer from your documents, your catalogue and your policies, not the open internet. Retrieval-augmented, source-cited and under your control, so customers get answers you can stand behind.
AI, integrated properly
We do not train models; we make them useful. Wannaverse wires modern AI into mobile apps, web products and cloud platforms with the same engineering discipline we apply to everything else.
74%
of companies struggle to achieve and scale measurable value from their AI investments.
Boston Consulting Group, 2024
The gap is rarely the model. It is the engineering around it.
What we deliver
Agents that do the work
Agentic workflows that triage tickets, draft documents, reconcile records and call your APIs, with human approval exactly where it matters. Hours of process become minutes of review.
Document intelligence
Extraction, classification and summarisation pipelines that read what nobody has time to: invoices, contracts, applications and support inboxes, turned into structured data your systems can act on.
Model and provider strategy
Anthropic, OpenAI, AWS Bedrock or self-hosted open models, chosen per workload for quality, latency, privacy and cost. We design the integration so you can switch providers without rebuilding.
AI-assisted engineering
We help teams adopt coding agents and AI tooling without lowering the bar: faster delivery with the same review discipline, so every line that ships is tested, understood and owned by an engineer.
Governance, privacy and guardrails
Evaluation suites before launch, guardrails and fallbacks in production, cost ceilings per feature and UK GDPR-safe data handling throughout. AI features you can put in front of a regulator.
More than a chat window
When most people say AI they mean prompting a hosted chatbot. In product engineering it is a much bigger toolbox: compact models that run on the device, vision and classification models, and reasoning models small enough to ship inside your app. No API round-trip, no per-call cost and no user data leaving the device.
Sometimes the right model is two gigabytes on the device, not an API in the cloud.
From the lab: WannaPass
Password managers find login forms with rules and regex, and break the day a page changes. WannaPass, an internal experiment, used compact reasoning models running entirely on-device to understand forms the way a person does: detect the fields, capture credentials and fill them back in.
- Ran fully on-device; nothing left the machine
- Models from 200 MB to 2 GB, sized per task
- Classified complex flows, including banking journeys
- Kept working when page structures changed
Why Wannaverse
Product engineers first
AI features live inside real products, and we build real products for a living. The model is one component; the app, cloud platform and user experience around it are what make it work.
Evidence over demos
Anyone can make an impressive demo. We measure quality with evaluation suites before launch and monitor it in production, so you know how the feature behaves on your real inputs.
Costs you can predict
Token budgets per feature, caching, and the right-sized model for each job. AI spend is engineered like any other cloud cost, not discovered on the invoice.
Honest scope
If a feature does not need AI, we will tell you, and build the simpler thing that works. That honesty is cheaper for you and safer for us.
Talk to us about AI
Bring us the feature you have in mind, or the process you suspect AI could take off your team’s plate. We will give you an honest view on whether it will work, what it will cost and how long it will take.
Book a free consultation sales@wannaverse.com