A chatbot works when three conditions are met: a structured knowledge base exists, users arrive with concrete questions, and the bot knows its limits. Most B2B organizations do not have a curated structured knowledge base; they have scattered documents on network shares and SharePoint pages in varying states of maintenance.
The bot then draws on content that is difficult to find even for humans and is partially out of date. The result is answers that users distrust — correctly.
A better sequence: first structure the knowledge base and make it searchable (valuable in any case). The conversational interface becomes worthwhile only afterward.
RAG combines two layers: a vector database indexing your documents semantically, and a language model that formulates an answer from relevant document excerpts. Unlike a pure chatbot, answers remain traceable — sources are returned with the response.
Typical applications. A sales representative asks "what terms did we agree with Customer X over the last three years?" and receives an answer linking back to the contract documents. A field engineer searches for installation detail on a product and receives the relevant manual paragraph with page number.
Effort for a productive first build: roughly eight to twelve weeks with a clean document base. Most of the work sits not in the language model but in document preparation — cleansing, format harmonization, metadata.
Often no generated answer is needed — a good search suffices. Classical full-text search finds only exact word matches. Semantic search recognizes phrasing variants: "how do I request leave?" finds a document titled "absence management".
Technically, semantic search also relies on a vector database, but without a downstream language model. That reduces cost and latency while delivering most of the practical value.
Example: a technical-support organization with fifteen thousand service reports across twenty years. Semantic search surfaces similar cases even when problem phrasing differs. Support staff save thirty to sixty minutes per case on average.
Prediction of machine failures from sensor data. Technically a time-series analysis problem, often combined with machine-learning models. No LLM required — the "AI" label here describes statistical learning rather than generative models.
Prerequisite: an existing data pipeline from machine to analysis tier (see the earlier piece on industrial IoT entry). Without at least six to twelve months of historical data, reliable predictive models cannot be trained.
Economic value is highest where unplanned downtime has substantial follow-on cost — typically in production lines with just-in-time delivery commitments or machines with long lead-time spare parts.
Automated extraction of structured information from unstructured documents. Verify incoming invoices, scan contracts for specific clauses, digitize forms. The category often markets as "intelligent document processing" and today rests on vision-language models.
The application space is broad; effort is manageable. A mid-market organization with several thousand inbound invoices per year can reduce accounts-payable effort by thirty to fifty percent with document AI — provided source documents arrive in acceptable quality.
Important: automation should always include an exception loop. Documents on which the model is uncertain route to a human. Pure full automation regularly fails on edge cases.
Each of the four applications processes proprietary and often personal data. The model-hosting choice is a data-protection decision:
For most mid-market applications, a European API or a US API with disciplined pseudonymization is the economically sensible path.
Commercial models with RAG form the default. Own fine-tuning becomes economically rational at roughly fifty thousand to one hundred thousand domain-specific data points combined with a recurring, well-bounded application.
Below that threshold, the effort — training-data preparation, compute cost, maintenance — exceeds the quality gain. Temptation is high; realistic return usually is not.
Standard entry: complimentary online scoping session for use-case clarification. Then a work contract with fixed price for a bounded MVP — typically RAG over a defined document base or semantic search over an existing database. Production data stays in EU regions; development uses synthetic or pseudonymized data; SCCs where external APIs are used. AI projects form part of our artificial intelligence and automation services, tightly integrated with classical software development and the cybersecurity requirements for production data.
Source code and usage rights transfer to the customer at acceptance. No dependency on a single model vendor — the integration is designed so providers can be swapped between OpenAI, Mistral, or self-hosting.
If an AI entry is on the next six-month agenda, do not start with model selection. Start with the question: which concrete problem does this solve, and do we have the data needed? Book a complimentary online scoping session; we sort the use case before anyone discusses GPU budget. Where personal data enters the workflow, the parallel read on GDPR-aligned product development helps — clean data handling is more critical than model selection. Where machine or sensor data is in scope, the piece on industrial IoT entry for mid-market manufacturers provides the data foundation.
For many simple internal productivity applications, yes. For an integrated B2B product with proprietary data, standard usage falls short; API integration and proprietary data storage are required. ChatGPT Enterprise is a complement, not a replacement, for a product-internal AI backend.
Technically, RAG functions from roughly a hundred documents. Economic sense emerges at several thousand documents that users search regularly. Below that, well-tuned full-text search often suffices.
Legally, the product operator — not the model vendor. Hence every AI-integrated B2B application requires a clean disclaimer layer and, for consequential decisions, a human-in-the-loop architecture.
Highly variable, dependent on model choice and query load. A typical RAG backend for a mid-market customer base sits in the low four figures per month. Specific figures emerge from the planned usage profile.
Yes — and this is the more common path than an AI-native rebuild. Existing applications are extended with a vector database and an API layer; the rest of the application remains unchanged. What matters is clean abstraction so the model stays swappable later.
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