The places where people do the same work every day that a system could handle: that’s where we start. A few real-world examples:
AI & process automation: scale without scaling manual work
Your organisation grows, but manual work often grows faster than your team. Your inside team retyps PDF orders, a technician searches through stacks of documentation for a single error code, an account manager skips their call report after a busy day. This happens every day.
Traditional automation breaks down the moment information arrives unstructured: a PDF, a photo, a free-text field. Welisa chooses what fits each situation, sometimes automation, sometimes AI, and always sets it up to be as efficient and scalable as possible: as a targeted layer on top of your existing architecture, without having to rebuild your processes from scratch.
Always in control of AI and automation
We don’t sell hot air. Our approach is grounded in practice: we already streamline complex, error-prone processes across manufacturing, logistics, commercial operations, and service. We bring that experience to every engagement.
- Human oversight: AI prepares the work (such as drafting concept orders), while your experts retain full control.
- Architecture over tooling: Whether we use Azure, OpenAI, Agentforce, or Google models, the technology always follows the business case. We keep the balance between computing power and operational costs (token usage).
- Data integrity: Your business data stays yours. We work with secure gateways so sensitive information never ends up in public models.
Where manual work blocks growth
Intelligent document processing: processing PDF orders automatically
Orders that come in as PDFs no longer need to be retyped manually. Our solutions recognise article numbers and specifications regardless of the customer’s layout. In the packaging industry, we used this to automate order entry, shifting the inside team from data entry to quality control and customer contact.
Smart service: making knowledge accessible for the field team
Searching through manuals or databases wastes valuable time. With vector search, we surface knowledge based on meaning rather than keywords. When a technician enters an error code, they immediately see the most likely solution from historical cases, which directly improves the first-fix rate.
Conversational intelligence: dictate your visit reports on the spot
Admin is often the last priority for account managers and technicians. We make it possible to simply dictate a report after a visit. The AI transcribes the audio, summarises the key points, and automatically fills in the right fields in Salesforce. Your CRM stays up to date without a keyboard in sight.
Data enrichment and predictive pricing: decisions based on facts
AI is only as good as the data behind it. We deploy AI agents that proactively enrich customer profiles using public business sources. This enriched data forms the basis for predictive pricing, so your team sets the optimal quote price based on facts and historical data rather than intuition.
Chat with your data: from question to chart
Instead of waiting for the BI team or building a report yourself, just ask a question: “Show me revenue trends by region for the last four quarters” or “What happens to our Q4 forecast if we close these three deals?” The AI pulls the data directly from the system and returns the answer as a chart, table, or summary, with no filtering or formatting on your end.
Custom-built: AI solutions for your specific process
AI capabilities grow by the day, but technology is never the starting point. Everything stands or falls with your specific process. That’s why we often start small with a targeted solution that delivers immediate results, then scale from there. Whether it’s automatically routing emails to the right team or forecasting inventory needs, we build what works for your organisation.
Field Service
We automatiseren terugkerende processen zodat snelheid en schaalbaarheid toenemen.
Start small, scale when it works
Introducing AI requires a pragmatic approach. We start small, validate the costs, and only scale once it works.
- Discovery: We identify processes with a high cognitive load.
- Prototype (proof of value): We build a working prototype on your own dataset. You see straight away whether the AI understands your orders or questions.
- Cost forecast (consumption): No surprises after the fact. We calculate exactly what the token usage per transaction is (e.g. per processed PDF). You know upfront what the operational costs will be at scale.
- Integration: Stable embedding in your existing IT architecture.
Ready for the next step in automation?
The question isn’t whether AI and automation will change your organisation, but which process to make profitable first. We’re happy to share our experience from complex projects to identify where your opportunities lie.
Schedule an AI deep dive to make the possibilities concrete for your organisation.
Frequently Asked Questions
When does AI automation become profitable?
Automation pays off when you deploy the right technology in the right place. AI doesn’t solve everything. For many structured steps, traditional automation is still the better choice: it gives you full control, is fully predictable, and costs no token usage. That’s why we use AI in a very targeted way, specifically at the points in a process where traditional software gets stuck on unstructured data (such as reading PDF orders or interpreting emails). The AI translates that unstructured input into clean, structured data, after which we continue the process using strict, traditional rules. This gives us the best of both worlds: maximum control, fewer unnecessary errors (hallucinations), lower token costs, and a pre-calculated estimate of the actual time savings.
How do you prevent sensitive data from ending up in public models?
This is non-negotiable. We work through secure gateways (such as the Einstein Trust Layer or Azure OpenAI). A critical part of this is prompt sanitisation. This means that every instruction (prompt) sent to the AI is automatically checked and stripped of sensitive personal and business information before it gets there.
Pas daarna doet de AI zijn werk. De data die de AI wél bereikt om een antwoord te genereren, wordt nóóit gebruikt om het publieke model te trainen. Het model zelf slaat na de interactie niets op (‘Zero Retention’). Jouw data verlaat de beveiligde omgeving dus niet en blijft volledig jouw eigendom.
What is the difference between RPA and AI processes?
RPA (Robotic Process Automation) simulates human clicks in an application. This is useful for automating legacy systems without modern APIs, but RPA cannot interpret documents. Traditional PDF reading has been possible for years, but in practice it often broke down the moment a layout or table changed. With AI (such as AI Vision), we read a PDF or email the way a human would. The AI interprets the data reliably regardless of the formatting, after which we automate the rest of the process in a structured way.
How does token pricing work in practice?
With generative AI, you typically pay per use (consumption). A ‘token’ is a fragment of a word. During the analysis phase, we calculate how many tokens an average task costs (e.g. processing one PDF). This gives you a clear price per transaction. For example: “Automatically processing an order costs €0.02 in token usage.” This makes operational costs fully predictable as you scale.
How quickly can you get started with a prototype?
We believe in “show, don’t tell”. A Proof of Value (PoV) is often up and running within days. We take one isolated process, build the solution in a sandbox environment, and test it with real data. You see quickly whether the technology works and what the impact is, without a large upfront investment.
Does this only work within Salesforce?
No. For many of our clients, Salesforce is the backbone of existing processes and data, and the day-to-day interface for users. But we’re flexible: we can call AI models directly from within Salesforce itself, or from platforms like AWS or Azure. We connect to these systems directly from Salesforce, or centrally through a middleware solution like MuleSoft. We build the architecture that fits directly into your existing landscape.
How do you handle 'hallucinations' (incorrect answers)?
We approach this carefully through observability and human-in-the-loop. First, we build extensive observability into our AI processes. This lets us trace exactly which output belongs to which specific input and context. It allows us to continuously test the AI system, measure quality, and adjust until the output is structurally reliable. This gives us control during development and keeps that control in place once the AI is live.
For processes with zero tolerance for errors, we implement a human-in-the-loop in the first versions. The AI does the heavy lifting and saves significant time, but a team member takes final responsibility in one short review step. In this phase, the organisation builds confidence in the technology. When the data shows the AI is consistently performing well, we often remove that human check to fully automate the process.