Connect the right knowledge
Start with a bounded set of documents, SOPs, customer notes, and decisions. Every source has an owner and a reason to be there.
A read-only business intelligence layer brings approved documents and business records together. Your team and compatible AI tools can find useful context without direct access to production systems.
Platform pilot in development. Selected data is imported for review and controlled retrieval. The AIbleto Platform does not write back to your CRM or messaging tools. Automatic source-permission mirroring and hosted assistant sign-in are still being validated.
Your business intelligence layer should outlast any single model, app, or vendor. We design around portable data, documented connections, and the systems that already work for you.

Start with a bounded set of documents, SOPs, customer notes, and decisions. Every source has an owner and a reason to be there.
Keep source references, freshness, and permissions attached to answers. Give people a way to inspect, challenge, and improve what they find.
Ground workflows in your vocabulary, policies, and customer context. The knowledge layer provides read-only context; any separate automation needs its own permissions and review.
Choose an appropriate hosting model, document the architecture, and make exports part of the handover. Your team owns the operating knowledge.
The engagement builds a foundation around your business. The software mix is selected after discovery, with existing tools and suitable open-source options considered first. Hosting, model usage, and maintenance are included in the cost discussion.
No. Start with a small, useful scope. We decide what can stay in its current system, what should be indexed, and what should be excluded. Permissions and data retention are part of that decision.
Where they offer suitable interfaces and access controls, yes. Tool independence is an architecture goal: the knowledge layer should be usable across different models and workflows.
No. A source-backed answer still needs evaluation. We define when the system should show uncertainty, ask for help, or require human review, and test against real tasks before expanding.
Find a useful first project, or discuss a problem you already want to solve.