AI customer support
Deflect tickets and speed up service.
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Problem
Support teams often handle repetitive questions already answered in documentation. Knowledge may be fragmented, answer quality may vary and escalation can take time. We measure the baseline before setting a pilot target.
Solution
AI customer support responds instantly from your approved knowledge base (RAG technology), automatically routes tickets to the right owners with intent classification, and escalates complex cases with clear rules. The system stays consistent across all channels — chat, email, or embeddable widget. It learns continuously: missed intents and knowledge gaps surface in the audit dashboard, enabling strategic expansion of your knowledge base. Your support team can focus on complex cases that truly require human judgment.
How AI customer support helps
AI customer support removes repetitive tickets and frees the team for complex cases. When knowledge is scattered, answers vary and escalations slow down — this solution delivers consistent responses from an approved knowledge base.
The system improves in real use: missed intents surface quickly so you can expand the knowledge base. We use the same stack in our own support and with client teams.
Key Benefits
The pilot measures repetitive ticket volume, response time, answer quality and human escalation rate. Targets are set from the customer's own baseline rather than a generic percentage promise.
Implementation Timeline
Deployment ranges from 2 hours to a few weeks depending on complexity and integrations. At its simplest: upload documents, configure settings, embed widget on your site — ready same day. More complex scenarios involve CRM system integrations, calendar applications, or multiple data sources. A typical full production timeline with pilot is 4-8 weeks, including knowledge base construction, testing, and iterative optimization.
Technical Architecture
The technical implementation is selected based on the customer's data, integrations and operating environment. It may use RAG, vector search, semantic caching and different language models. Access control, logging and personal-data processing are designed for each solution and agreement.
Features
AI chatbot and agents built on LangChain/LangGraph framework deliver instant support from your own documents. The solution seamlessly supports English and Finnish, learns from usage, and ensures transparency with source citations.
Key outcomes
Process
Discovery
Pilot
Integrations
Rollout
Optimization
Data & integrations
- CRM and support systems
- Documents and knowledge bases
- APIs and data sources
Security & compliance
- Processing designed for agreed privacy requirements
- Audit trail and logging
- Clear boundaries and access control
Use cases
FAQ responses and routing
Multilingual support
Prioritization and escalation
FAQ
How quickly can we deploy?
A simple deployment (documents + widget) takes 2-8 hours. Full production with CRM integration and optimization takes 4-8 weeks. We recommend starting with a scoped pilot on one channel.
What documents and data are needed?
FAQ pages, product documentation, process descriptions, common support responses, and guides. Supported formats: PDF, Word, Markdown, HTML. We can also auto-sync cloud storage folders (e.g., Google Drive).
Can it integrate with our existing helpdesk?
Yes. We support integrations with popular CRM and helpdesk platforms. The chatbot can create tickets, fetch conversation history, or escalate cases via API.
How do you ensure answer accuracy?
The system uses only your approved knowledge base (RAG), shows source citations in every response, and detects hallucinations. The audit dashboard surfaces uncertain responses for review. Clearly responds "I don't know" when information isn't found.
What languages are supported?
Finnish and English are fully supported with automatic language detection. Additional languages (Swedish, German, etc.) are possible depending on the LLM model. The knowledge base can include multilingual documents.
Can we use local models without cloud APIs?
Yes. We support Ollama models for fully local operation without API keys. This ensures complete data control and zero cloud costs. Performance depends on server hardware.
How does the system learn and improve?
Intent classification identifies missed topics, the audit dashboard shows problematic responses, and knowledge gap tracking lists missing subjects. You use these insights to expand the knowledge base. The system doesn't learn automatically without approval.
What is the cost structure?
Costs consist of implementation, model usage, hosting and support. Caching or a local model may reduce some usage costs, but the impact is assessed with real workload rather than a generic percentage promise.
Let’s plan your service
Tell us your goals and process, we will propose a plan.
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