Whether you need a rule-based chatbot, an LLM-powered AI chatbot or a customer service chatbot wired to CRM and ERP, the architecture matters: knowledge sources, chatbot integration, human handoff and measurable quality. We deliver chatbot development for web, Teams and ticketing—not an isolated widget—including LLM chatbot setups with RAG on your documents.
Consumer chat interfaces optimise for generic questions; a B2B bot must honour internal policies, price lists and ticketing workflows. We therefore design governance, data ownership and escalation paths first—not a one-size FAQ widget. That keeps you clearly separated from lightweight B2C offerings while staying audit-ready for ERP, CRM and helpdesk landscapes.
Approved sources—wikis, PDFs, structured master data from ERP and CRM—feed a retriever that grounds the language model. Hallucinations drop and answers stay explainable. Connectors to ITSM (ticket creation or triage), calendars and IAM are standard project scope; sensitive attributes can be filtered before embedding or read only via secured APIs.
Hosting may be on-premise (GPU or inference servers), in a trusted EU cloud or hybrid—aligned with your DPAs and audit expectations.
Budget depends on integration depth, languages, channels and operations; first production bots often land in the mid-to-high five figures in euros. Discovery yields a transparent proposal with scope, milestones and SLAs. For ballpark figures on related AI channels see also AI phone bot costs; we align roadmap and scope in a consultation.
An enterprise bot answers from your approved corpora and policies—not anonymous internet training. The matrix below shows the main differences that affect selection and operations.
Standard ChatGPT vs. a custom B2B AI chatbot (RAG)
How an enterprise bot differs from a consumer-style account
| Criterion | Standard ChatGPT account | Custom B2B AI chatbot (RAG)Empfohlen |
|---|---|---|
| Privacy / GDPR alignment | Vendor terms | DPA, EU / on-prem |
| Data basis | General web knowledge | ERP, CRM, documents |
| Hallucination risk (domain) | High without sources | Retriever-grounded |
| API / system integration | ITSM, CRM, IAM | |
| Hosting | Public cloud | EU cloud or on-prem |
Internal IT support and ticket triage: The bot handles repetitive first-line questions—VPN, password roles, known error patterns—and creates structured tickets with priority hints. First-level load drops measurably without automating complex escalations. ITSM integration ensures downstream teams see the same facts the bot captured.
Sales assistance and proposal preparation: For field and inside sales the assistant compresses product FAQs, configuration rules and approved snippets. It prepares call notes, suggests attachments and respects release boundaries—without inventing prices. Time-to-proposal shortens without extra marketing or legal cycles.
HR onboarding and employee services: New hires get guided flows for policies, hardware requests and training links; recurring HR questions (leave balances, reporting chains, benefits) stay consistent. Data is filtered by role; HR remains authoritative and the bot only exposes cleared information.
Voice channel: see AI phone bots. Customer-facing agency delivery is included here. Knowledge and agents: AI knowledge base, AI agents; governance via EU AI Act consulting.
Project references
Documented examples with transparent evidence types — browse matching references or open the full case study.
Agency delivery included
Companies that want an AI chatbot built receive more than an embedded chat widget. We define the use case, prepare approved knowledge, develop escalation paths, connect CRM or ticketing and train the team that owns the service after launch. The result is a scoped delivery with a test set, operating rules and measurable answer-quality targets.
Product FAQs, service questions and ticket triage are answered around the clock, with full context handed to staff when confidence, subject or customer request requires a human.
Manuals, wikis and process documents become searchable through role-aware RAG. Source references and approval states help employees distinguish verified guidance from missing knowledge.
Runbooks, monitoring, known-error guidance and ownership for knowledge updates are agreed before go-live. This prevents the chatbot from becoming an unowned experiment after the first release.
A generic tool may be sufficient for a static FAQ. A specialised chatbot agency is needed when answers must respect permissions, cite approved sources, update from business systems and hand cases to people without losing context. We define these controls before selecting a model or widget.
Answer recurring product and service questions with human handover.
Search manuals, policies and process documentation with source citations.
Guide employees through approved policies, forms and first-week processes.
Triage standard requests and route unresolved incidents with full context.
Support configuration and selection using catalogue, CRM or commerce data.
Combine consistent answers, ticket creation and measurable escalation quality.
1. Use-case analysis
Channels, question volume, human escalation and measurable success criteria.
2. Knowledge base
Approved documents, websites and system data with refresh and ownership rules.
3. Build and integration
RAG, website widget, CRM, ERP or ticketing plus permission checks.
4. Test and operate
Acceptance set, team enablement, monitoring and answer-quality reviews.
Website chatbots can run as a widget or native frontend while the same backend serves Teams, Slack or a service portal. CRM and ticketing integrations pass identity, intent and conversation context into the existing process. Personal data is minimised, retention and deletion are documented and EU hosting or on-premise operation is evaluated for the specific scenario.
A GDPR-aligned design still requires an individual legal assessment. We provide the technical controls, processing documentation and data-flow transparency needed for that assessment.
A rule-based bot follows fixed decision trees; a RAG chatbot answers from approved business sources; an AI agent for autonomous workflows can plan and execute multi-step actions. We select the smallest reliable architecture and expose realistic budget factors in the AI chatbot cost guide.
Analysis clarifies which intents the bot covers and which sources (manuals, FAQs, CRM exports) feed retrieval. Training structures your content for stable answers; integration covers embedding on the website plus CRM, ticketing and calendars so the bot can open tickets or suggest appointments.
This service delivers a B2B chatbot with retrieval, roles and CRM or ticket integration. RAG retrieves approved sources at runtime; it does not train the language model on your documents.
We inventory revisions, metadata, access rights and representative questions. A curated set of source-backed answers establishes the retrieval baseline.
Acceptance covers citations, refusal boundaries, role filters and system hand-off. Monitoring tracks unanswered questions, stale sources, retrieval failures, latency and human escalation.
The chatbot must not invent missing knowledge. Multi-step autonomous tool use requires a separate agent architecture. RAG-based knowledge search complements this delivery path.
B2B chatbot development means governed data, escalation and CRM sync—not a generic widget.
Overview: AI services overview.
Frequently asked questions
Customer service (FAQ, ticket triage), sales (lead capture, booking), HR (onboarding, internal Q&A) and internal knowledge.
We help you find the right use case and integrate with your systems.
Costs depend on complexity and integrations.
A first useful bot often starts in the mid five figures. We give a transparent quote after a short discovery call.
Yes.
We combine LLMs with your content—FAQs, products, internal knowledge—so answers are accurate and on-brand. Guardrails keep replies on topic.
We define scope and escalation to humans, monitor performance and iterate from real usage.
We can set KPIs (e.g. deflection rate, satisfaction) and tune the bot over time.
Agency delivery covers use-case scoping, knowledge preparation, dialogue and escalation design, integration, business acceptance, training and operational handover.
You receive a documented service rather than an isolated widget.
Depending on permissions, we connect PDFs, manuals, websites, SharePoint or Confluence and CRM, ERP or ticket data.
Refresh cycles, role filters and source citations are defined for each source.
We assign ownership for knowledge updates, track unanswered questions and escalations and maintain a regression set with approved expected answers.
Runbooks and monitoring make operations transparent to support and business teams.
Chatbot development fits written channels with CRM/ticket integration.
Phone bots cover voice. RAG-only suits internal search. Classical ML fits forecasting—not open dialogue. The overview on the main AI page helps you choose.
Development means technical depth: RAG, APIs, tenancy, monitoring.
The agency page targets transactional website support packages. Both can combine without duplicate SEO intent.
We analyse your use cases and show where a bot delivers the most value—no obligation.
Book a consultationAn AI chatbot pays off when recurring questions can be answered from maintained content and escalation to the team is defined.
| Use case | Benefit | Differentiation | Outcome | Next step |
|---|---|---|---|---|
| First level keeps receiving the same questions | Answers from approved sources with hand-off to humans | No autonomous actions in other systems | Measurable share of requests solved on first contact | Cost of AI projects |
| Content is scattered across wiki, DMS and SharePoint | Knowledge base with permissions and citations before rollout | No bot without a maintained knowledge source | Reliable answer base instead of demo prompts | AI knowledge base |
| The bot should create tickets or trigger orders | Move to agents with a tool set and approval points | Not part of a pure answering bot | Documented decision between bot and agent | AI agents |
B2B chatbot with agency delivery and integrations – distinct from a knowledge base without a chat experience.
Overview: AI & machine learning (overview).
Service overview: AI & machine learning (overview)
Use our funding calculator to see which government grants may apply to your project.
Björn Groenewold – Managing Director
Service cluster
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Quick orientation for AI and machine learning services—from first steps to production solutions, with governance and measurable outcomes.
From the field: KI-Dokumentation: Ambulante Pflege (scenario) and Chop-E KI-Kochassistent (in-house). All references.
Use our interactive calculator for a first budget indication—free and non-binding.
Thorsten Frieling – Projektmanagement
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