What grounded AI actually is
A general-purpose model is fluent but ungrounded: it produces a plausible answer from everything it has seen, not from your business. Grounded AI fixes that. Before the model responds, your real context — company profile, playbooks, and documents — is attached to the request, so the output is specific, on-brand, and tied to a source you can point at. The model stays the same; what changes is that it now reasons from your facts instead of guessing. For the full picture, read our pillar on grounded AI.
Why it matters
Ungrounded AI is the reason so much AI copy reads generic, drifts off-brand, or confidently states things that are not true for your company. That is fine for a brainstorm and dangerous for go-to-market work — outreach, positioning, and pipeline decisions need to match reality. Grounding closes the gap: answers reflect how your team actually operates, in your voice, with fewer rewrites and fewer corrections. It is also the safer foundation for autonomy, because an agentic system that acts on real context, behind a human-in-the-loop gate, is far more trustworthy than one improvising from a generic prompt.
How dolv does it
dolv is a grounded AI command center, and grounding is on by default — not a setting you remember to flip. On every LLM call across chat, configurable agents, and multi-agent campaigns, dolv assembles a grounding block from three real sources: your saved company profile, your playbooks, and a knowledge base. The knowledge base is built by crawling your website with a crawl4ai-powered engine, and the Settings “Website” field auto-saves those crawls into knowledge. Because grounding sits in front of action, the agents that call 25+ tools that execute real work across 20 read + write integrations stay on-brand and on-strategy. Anything public still routes through the Approvals inbox and its prepare → approve → executing → done flow, so grounded context and human sign-off work together. Opting out is the explicit choice for the rare case you want a raw response.
A short example
You ask dolv to “draft a follow-up to last week's webinar leads.” An ungrounded model would invent a tone and guess at your product. A grounded agent instead pulls your company profile to get the positioning right, reads the playbook that governs nurture sequences, and references the webinar page already crawled into your knowledge base. It drafts the email in your voice, with accurate claims, and queues it in the Approvals inbox — nothing leaves your domain until you click approve. You review a draft that is already on-brand and on-strategy, sign off, and watch it move to executing, then done. That is the difference between AI that sounds right and grounded AI that is right for your business. dolv it.
Go deeper in our pillar on grounded AI, the related term agentic AI, or the blog post how to ground AI in your company knowledge.
Grounded AI questions
Quick answers to what people ask about grounded AI.
What does grounded AI mean in simple terms?
Grounded AI means the AI answers from your real, verified knowledge instead of guessing from a general model. Grounding attaches your own context — company profile, playbooks, and documents — to each request, so the output is specific to your business and traceable to a source. In dolv, this happens on every LLM call by default, so chat, agents, and campaigns all reason from the same real sources.
Where does dolv get the knowledge it grounds on?
From three real sources assembled into one grounding block: your saved company profile, your playbooks, and a knowledge base. The knowledge base is built by crawling your website with a crawl4ai-powered engine (with a BFS fallback), and the Settings "Website" field auto-saves those crawls into knowledge. Point dolv at your site and your docs once, and every grounded answer draws from them after that.
Is grounded AI something I have to turn on per task?
No. In dolv grounding is on by default — it is the standard behavior for every LLM call across chat, configurable agents, and multi-agent campaigns. You do not enable it per task or remember to attach context. Opting out is the explicit choice for the rare case where you want a raw, ungrounded response, which is the opposite of bolt-on retrieval you have to wire up each time.
See grounded AI do the work
Grounded by default on your profile, playbooks, and crawled docs — then human-in-the-loop approvals on anything public, measured against one unified funnel. dolv it.