AI solutions for business

The first thing we do is sit down with you to understand your day-to-day operations: which repetitive tasks eat up the most hours and where information gets lost between departments. From there, we propose one or two concrete, measurable use cases rather than a huge project all at once. The idea is to start with something that delivers visible results within a few weeks, not with the flashiest technology.

We work with manufacturing, distribution and service SMEs on projects such as quote automation, customer email classification, automatic invoice processing and internal assistants for searching documentation. We can show you specific cases from the industry closest to yours and, if you like, put you in touch with a client so you can hear about their experience first-hand.

ChatGPT Enterprise and Copilot are excellent general-purpose copilots, but they don't connect natively to your ERP, CRM or internal databases, and they don't run business processes end to end. We build agents and automations that read from and write to your systems, apply your business rules and leave a full audit trail. In other words: ChatGPT answers; a custom solution takes action inside your workflows.

For individual tasks such as drafting, summarizing or analyzing documents, off-the-shelf copilots are usually enough. When the use case involves orchestrating several systems, applying your own logic, automating a recurring process or exposing the functionality to end customers, generic products fall short and custom development that fits the way you operate is the better choice.

On day one, your team keeps working as usual: the AI is deployed in parallel and only switched on for tightly scoped pilot cases. During the first few weeks, key people spend some extra time validating outputs and giving feedback so we can correct answers and refine the model. From month two or three, that workload drops and the team starts winning back time on the automated tasks. We plan the rollout in phases so it never competes with your busiest periods.

We design the system with guardrails: automatic validations, confidence thresholds and human-in-the-loop review for sensitive cases, so doubtful answers are escalated to a person before they go out. Every error is logged with its context in an incident dashboard, so we can reproduce it, fix the prompt or the source data and retrain if needed. We also agree with you which use cases can run autonomously and which always require human review.

A tightly scoped pilot is usually up and running in 4–8 weeks, with visible, if limited, results. A full rollout across your core processes stabilizes between months 3 and 6, once the model has been tuned with real data and the team has been trained. From year one onwards, we move into continuous improvement and extend the solution to new use cases. Any promise of a full rollout in just a few weeks usually hides technical debt down the line.

No. Everything we build is documented, and you own the software, the data and the workflows. We use open, standard technologies wherever possible and design with an abstraction layer that lets you switch models or providers without rebuilding the system. We want you to stay with Interconecta because of the results, not because of technical dependency.

We take a multi-model approach: OpenAI's GPT-4o and o1, Anthropic's Claude Sonnet and Opus, Google's Gemini, and open-source models such as Llama 3 or Mistral when the use case calls for it. The choice depends on the use case: complex reasoning, latency, context window, multimodal capabilities or the need for on-premise deployment. We design the abstraction layer so the model can be swapped without rewriting the business logic.

We build integrations via REST/GraphQL APIs, webhooks, message queues or native connectors for SAP, Dynamics, Salesforce, Odoo, Dolibarr and SQL/NoSQL databases. For RAG on company data, we use vector databases such as Qdrant, Weaviate or pgvector inside your own infrastructure. We set up SSO with your identity provider (IdP), encryption in transit and at rest, secrets management and full tracing with OpenTelemetry or Langfuse for auditing and debugging.

We work exclusively with enterprise endpoints (OpenAI API, Anthropic API, Azure OpenAI, AWS Bedrock), where the contract guarantees your data is not used to retrain models, and we keep processing in European regions whenever possible. For sensitive or regulated data, we deploy open-source models on-premise with vLLM, Ollama or TGI. We sign a data processing agreement (DPA) under Article 28 of the GDPR and, if there are international transfers, we apply Standard Contractual Clauses and a Transfer Impact Assessment in line with Schrems II.

We classify every system under Regulation (EU) 2024/1689 (the EU AI Act): if it is high-risk (Annex III), we apply the stricter obligations on risk management, data governance, technical documentation and human oversight; any practice prohibited under Article 5 is ruled out from the start. For decisions that affect people, we guarantee human intervention and the right to obtain an explanation and to contest the decision, in line with Article 22 of the GDPR. Where required, we carry out the Data Protection Impact Assessment (DPIA) mandated by Article 35 before deployment.