Integration
An AI that actually knows your data.
Agentic AI systems and retrieval-augmented generation pipelines that ground every answer in your business, and take action, not just respond.
The problem
Generic AI is not enough for your business.
Hallucinations on your data
Out of the box, Claude and ChatGPT know nothing about your products, policies, or processes. They generate confident-sounding answers that are factually wrong.
Unhelpful chatbots
Most AI assistant deployments fail because the model has no access to your real knowledge base. Your team stops using them within weeks.
Talk-only AI
A useful assistant should look up the account, check the contract, draft the renewal, and book the meeting. Not just describe how to do it.
What Kobol builds
RAG + agents that retrieve, reason, and act.
Kobol designs and deploys custom RAG pipelines and agentic AI systems on LangChain, LlamaIndex, and the leading reasoning models. Vector databases (Pinecone, Weaviate, ChromaDB) for semantic retrieval. Tool-use boundaries for safe action. Continuous accuracy monitoring.
Key deliverables
- Custom RAG pipeline architecture connecting your data sources to AI models for grounded, accurate responses
- Agentic AI systems with tool-use capabilities that reason, retrieve, and take action across your stack
- Vector database setup and optimization (Pinecone, Weaviate, or ChromaDB) tuned to your data shape
- Accuracy monitoring and continuous improvement dashboards tracking response quality
Process
From intelligence audit to deployed agents.
- Step 01
Intelligence Audit
We map your data sources, identify knowledge gaps, and define the AI use cases with the highest leverage.
- Step 02
Architecture Design
We design the RAG pipeline, choose embedding models and vector DBs, and define agent capabilities and safety boundaries.
- Step 03
Build & Train
We build retrieval, fine-tune agent behaviours, integrate with your tools, and test against real-world scenarios.
- Step 04
Deploy & Evolve
We deploy, monitor accuracy, and continuously add data sources and capabilities as needs evolve.
Engagement
Project-based
- ·RAG pipeline architecture and deployment
- ·Custom AI agent development
- ·Vector database setup and optimization
- ·Integration with existing systems
- ·Documentation and knowledge transfer
Engagement
Retainer
- ·Ongoing agent training and optimization
- ·New data source integration
- ·Performance monitoring and accuracy tuning
- ·Monthly capability expansion
- ·Priority support and response times
Ready to see what's actually worth automating?
Book a free discovery call. 30 minutes, no strings, no pitch deck. We'll talk through your operations and tell you what's worth building before we send a single proposal.