In short
DevAndCloud brings generative AI into businesses: assistants and chatbots that answer from your documents (RAG), AI agents that act in your tools, and AI features inside your applications. We work with Claude, GPT, Mistral and open-source models, with quality evaluation, guardrails and GDPR compliance.
An AI prototype impresses quickly; a reliable production system takes more: well-prepared data, sourced answers, enforced access rights, measured quality and controlled costs. Getting from prototype to production is exactly where we help.
We start from one specific use case and build a prototype on your real data, measured against a set of test questions. For repetitive tasks, AI also fits into automated workflows, and it can power new features in your web or mobile products.
You want to…
- Let your teams query your documents and data in plain language
- Add AI features to your app or SaaS
- Set up a customer assistant available 24/7
- Turn a promising AI prototype into a reliable production solution
What we do
Business assistants & chatbots
Assistants that answer from your content, with cited sources and a controlled tone.
RAG & semantic search
Secure indexing of your documents and knowledge bases, with access control.
AI agents
Agents that act in your tools: search, draft, update, trigger actions.
Integration & reliability
Model selection, quality evaluation, guardrails, cost control and monitoring.
Typical use cases
Internal knowledge assistant
Questions about your procedures, contracts or technical docs, with links to sources.
Customer support chatbot
24/7 answers from your FAQ and knowledge base, with hand-off to a human.
Lead qualification
Analyse incoming requests, enrich your CRM and draft a reply.
Data extraction
Invoices, purchase orders, CVs or contracts turned into structured data.
AI in your product
Summaries, semantic search, content generation or recommendations inside your app.
Analysis and summaries
Meeting notes, customer feedback, market watch: the essentials in a few lines.
Chatbot, RAG assistant or AI agent: what's the difference?
| Solution | What it does | Example | Complexity |
|---|---|---|---|
| Scripted chatbot | Follows a predefined decision tree | Simple appointment booking | Low |
| RAG assistant | Answers from your documents, citing its sources | Internal FAQ, customer support | Medium |
| AI agent | Reasons and acts in your tools: CRM, email, databases | Handling a request end to end | High, with guardrails |
| AI feature | Performs one specific task in your product | Summarising, classifying, extracting | Varies |
How we work
- 01
Scoping
Pick the use case based on expected value, available data and risks.
- 02
Measured prototype
A first version on your real data, evaluated against test questions.
- 03
Production
Integration with your tools, access control, monitoring and cost control.
- 04
Continuous improvement
Quality tracking, user feedback and model updates.
What you get
- Use-case scoping and ROI estimate
- Working prototype on your real data
- Production solution integrated with your tools
- Evaluation sets and quality dashboards
- Documentation and security recommendations
Frequently asked questions
Yes. We use enterprise offerings with no training on your data, EU hosting, or self-hosted models when sensitivity requires it.
Whichever best fits your use case, budget and constraints: Claude, GPT, Mistral or open-source models. Our architectures make it easy to switch.
By grounding answers in your sources, measuring quality with test sets, and keeping a human in the loop for sensitive actions.
RAG (Retrieval-Augmented Generation) makes an AI model answer from your own documents: the system retrieves the relevant passages, then the model writes an answer citing its sources. It is the standard approach for an internal assistant or a customer chatbot.
Rarely. In most cases RAG is enough: it gives the model access to your documents without retraining, stays up to date and lets the assistant cite its sources.
Yes, through their APIs: CRM, ERP, email, databases, Google Drive, SharePoint or Notion. Sensitive actions still require human approval.
By choosing the right model for each task, limiting the data sent, caching frequent answers and tracking the cost per request on a dashboard.
It mostly depends on the volume and quality of your data. We set this out during scoping, with a clear goal: a measurable prototype before any larger commitment.
Our other services
Web development
Fast, secure websites, web apps and SaaS platforms built to convert.
- Next.js
- React
- TypeScript
- Node.js
Mobile apps
Native or cross-platform iOS and Android apps, from design to store release.
- React Native
- Expo
- Swift
- Kotlin
AI automation & workflows
Smart workflows that eliminate repetitive tasks and connect your tools.
- n8n
- Make
- Zapier
- Agents IA
Cloud & DevOps
Reliable infrastructure, continuous delivery and hosting for your applications.
- AWS
- Docker
- Terraform
- CircleCI
Have a project in mind? Let's talk.
Tell us what you need: we'll get back to you within one business day with an initial analysis and next steps.