AI for business processes: custom chatbots, RAG & process AI.
Most conversations about artificial intelligence stay abstract. What actually matters for your business is more specific: does a defined process get faster, more accurate, or less dependent on manual work? ARCALIS builds AI systems for three concrete purposes — and scopes each project to a specific problem, not as a general-purpose experiment.
AI Solutions from ARCALIS cover three areas.
Custom Chatbots
Automated conversation handling for customers and staff, built on your business information.
RAG Systems
AI that answers questions using your own documents and data, not generic knowledge.
Process AI
AI applied inside operational workflows to reduce manual data entry, classification, and monitoring work.
Custom AI chatbots for customer and team support.
A chatbot built on a generic AI model can hold a conversation. It cannot answer questions about your specific pricing, current stock, service policies, or internal procedures unless it is built to. ARCALIS develops chatbots trained on your actual business information — deployed on your website, WhatsApp, or internal tools — so responses reflect how your business actually operates.
Where standard processes currently take too long because someone has to manually look something up or answer the same question repeatedly, a chatbot removes that bottleneck without removing the option to reach a person when a request needs one.
Typical use cases
- Answering product, pricing, and service questions instantly, outside office hours
- Qualifying leads before they reach your sales team
- Checking order, delivery, or ticket status without a phone call or email
- Giving staff instant answers to recurring internal questions (HR policies, SOPs, IT requests)
- Escalating to a team member automatically when a request falls outside its scope
Chatbots and RAG systems can also be configured to communicate in the languages your customers and staff actually use, rather than defaulting to English only.
RAG systems: AI grounded in your own business data.
Retrieval-augmented generation (RAG) connects an AI model to your own documents and data — product catalogs, policies, past communications, technical documentation — instead of relying only on general training data. When someone asks a question, the system retrieves the relevant information from your sources first, then generates an answer based on it. This keeps responses grounded in what your business actually knows, and makes it possible to trace an answer back to its source.
For companies where information is currently scattered across spreadsheets, shared drives, and individual employees’ knowledge, RAG turns that scattered material into a single, searchable resource — accessible by staff or customers without someone having to search for the right file or remember who to ask.
Typical use cases
- An internal knowledge assistant so staff get sourced answers instantly, instead of searching folders or asking colleagues
- A customer-facing assistant that answers product or support questions based on your actual catalog and documentation
- Unified search across previously disconnected files, systems, and departments
- A system that draws on data already stored in Odoo or other business tools you use, instead of duplicating it elsewhere
Reducing manual work in daily operations.
Process automation and process AI are related but distinct. Automation executes defined rules reliably: if a specific trigger occurs, a specific action follows. Process AI adds a layer of interpretation on top — reading unstructured input, recognizing patterns, or making a judgment call within limits you define. In practice, the two are frequently combined: an automated workflow with an AI step at the point where rules alone cannot handle real-world variation.
Typical use cases
- Extracting data from invoices, receipts, and forms automatically, instead of retyping it by hand
- Classifying and routing incoming documents or requests to the right person or process
- Flagging unusual patterns in financial or inventory data before they turn into a larger problem
- Supporting demand and stock planning using historical patterns in your own data
AI creates the most value applied narrowly.
AI is frequently pitched as a solution for everything. In practice, it creates the most value applied narrowly — to a specific bottleneck where the effort of building it is clearly justified by the time, cost, or errors it removes elsewhere. Where a simpler, rule-based fix solves the same problem more reliably, that is what we recommend, even when it means proposing less AI rather than more.
This approach comes from experience beyond IT implementation. Our team has used ERP and business systems from the inside — as operators and managers, not only as system integrators — and applies that perspective when scoping what AI should and should not touch in your operations. As an Odoo partner, we also build AI systems to work alongside your existing Odoo setup rather than as a separate, disconnected tool.
Frequently asked questions.
What is the difference between a chatbot and a RAG system?
A chatbot is the conversational interface people interact with. RAG is one way of powering that interface — or a separate backend tool — so its answers draw on your own documents and data rather than general knowledge. Many of our chatbot projects use RAG underneath; not every RAG system needs a chat interface, since some function purely as internal search or reporting tools.
How is process AI different from process automation?
Process automation follows rules you define: a fixed trigger leads to a fixed action. Process AI adds interpretation — reading unstructured input, recognizing patterns, or making a judgment call within boundaries you set. Many operational workflows combine both.
Can AI systems connect to our existing Odoo setup?
Yes. Chatbots, RAG systems, and process AI applications can be built to read from and write to your Odoo data, so AI-generated information feeds directly into the system you already use for daily operations, rather than creating a separate data source to maintain.
Do we need an ERP system before implementing AI solutions?
No. Chatbots and RAG systems can be built around your existing documents and data sources independently of an ERP system. Process AI applications that touch operational data — inventory, finance, order processing — typically deliver more value once connected to a structured system such as Odoo, since there is more consistent data to work with.
Is our business data safe when used in an AI system?
Data handling depends on the specific setup: which models are used, where data is processed and stored, and what access controls are in place. We review these details for your specific case during the initial consultation rather than giving a generic answer that may not match your actual requirements.
How long does implementing an AI solution take?
It depends on scope. A chatbot covering a defined set of questions can typically be scoped and built faster than a RAG system connected to multiple data sources, or process AI that touches sensitive operational data. We provide a project-specific timeline after the initial consultation.
Explore AI for your operations.
Not every process benefits from AI. Identifying where it genuinely helps, versus where a simpler solution works better, is part of the first conversation — not an assumption we start from. Looking for a broader starting point? An Operations Audit maps where automation and AI could reduce manual work across all your processes, not only within one area.