AI in the products you already have.
In plain wordsYou have an application or a platform? We add AI features to it.
The challenge
Adding AI to a product that is already running means working with its code, data, users and performance constraints, without breaking what works.
- A product team already busy with its own roadmap
- An architecture that was not designed for AI
- Data that is scattered or poorly prepared
- The fear of degrading the experience of current users
What we add to your products
Smart search
A search that understands questions asked freely, in your users’ own words.
Personalised recommendations
Products, content or next steps suggested from each user’s behaviour and from your catalogue.
Content generation
Product descriptions, summaries, replies and reports drafted by the application, in your tone.
Automatic translation
Content and exchanges translated inside the application, for users in several languages.
Image and document analysis
Reading scanned documents, extracting the information they hold, recognising what an image shows.
An assistant for your users
An assistant built into your product that guides users, answers their questions and acts in the application.
We work in your code, with your team
Your standards, your delivery tools and your security rules apply to our work.
Audit
Code, data and architecture, to know what AI can bring and what needs to change.
Architecture
The changes required: dedicated services, data access, queued processing, monitoring.
Evaluation
Quality measured on your real cases before each release, then monitored continuously.
Handover to your teams
Documented code, automated tests and handover to your product team when you decide.
Examples of use cases
Typical features we add to existing products.
Plain-language search in a catalogue
- Context
- Users of an application cannot find products with keyword search.
- Outcome
- Semantic search that understands freely worded questions, built into the existing application.
Personalised recommendations
- Context
- A platform shows every customer the same products, whatever their history.
- Outcome
- Recommendations computed from each customer’s behaviour, shown in the existing pages, with their effect measured.
Reading the documents users upload
- Context
- Users upload invoices, forms or certificates that staff then check and key in.
- Outcome
- The application reads each document, extracts the information and flags what needs a human check.
An assistant inside a customer application
- Context
- Users get lost in a rich application and call support for simple questions.
- Outcome
- An assistant inside the application answers from your documentation and guides users step by step.
Expected results
- New AI features without rewriting your product
- Quality measured before each release
- Control over data, models and costs
- A product team able to take over
From audit to a feature in production
- 01
Audit
Code, data, architecture and what users expect.
- 02
Prototype and measure
A first version tested on your real data, with a quality baseline.
- 03
Integrate
In your code and delivery process, with your tests and monitoring.
- 04
Release
Progressive release, quality and cost monitoring, regular improvements.
Included in every project
What comes with every projectData
Gathering, cleaning and structuring your data. It is the foundation of every AI project that works.
Security & compliance
Data under your control, hosting on your premises possible, traceability and controlled access. The GDPR, Japan’s APPI and Hong Kong’s PDPO are taken into account, and we adapt to your own security requirements and audits.
Operations & quality evaluation
Monitoring in production, measurement of the quality of the AI’s answers, guardrails, continuous improvement and cost control.
Training & handover
Training for executives and teams in AI, transfer of skills, documentation.
An application to take further?
Describe your product and what you want to add to it. We will tell you how we would integrate it and what it requires.