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.
What are agentic AI and RAG?
Retrieval-augmented generation, or RAG, is how you make an AI answer from your own data instead of guessing. The system retrieves the relevant documents from your knowledge base and hands them to the model before it answers, so responses are grounded in your products, policies, and records rather than the model's general training. An agentic AI goes a step further: it can use tools to take action, looking up an account, drafting a renewal, booking a meeting, within boundaries you set. We build both on LangChain, LlamaIndex, and vector databases like Pinecone, Weaviate, or ChromaDB.
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
Questions
Common questions
- What is the difference between RAG and just using ChatGPT?
- ChatGPT answers from what it learned in training, which includes nothing about your business. Ask it about your refund policy and it'll invent something plausible. RAG retrieves your actual policy first and has the model answer from that, so the answer is grounded in your data. It's the difference between a confident guess and a sourced answer.
- How do you stop the AI from hallucinating on our data?
- Two ways. RAG grounds answers in retrieved documents instead of the model's memory, and we monitor accuracy after launch rather than shipping it and walking away. We also set boundaries on what an agent is allowed to do, so it acts only within the limits you approve. No system is perfect, which is why we keep watching it.
- What is an AI agent allowed to do in our systems?
- Exactly what you approve, and nothing else. We define tool-use boundaries during the design phase: which systems the agent can read, which it can write to, and what needs a human to sign off. The agent takes routine action inside those limits and escalates anything outside them.
- Which vector database do you use?
- Whichever fits your data and scale. We work with Pinecone, Weaviate, and ChromaDB, and we choose based on how much data you have, how fast you need retrieval, and where it needs to run. We explain the trade-off rather than defaulting to the one we know best.
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.