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A2V2.ai has a lot of features, but they’re all built on a few core concepts. Understand these and the rest of the product makes sense.

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Accounts & organizations

When you sign up you get an organization — a shared workspace that holds your agents, knowledge, contacts, billing, and team. Everything you do happens inside your organization, and data is isolated from every other organization on the platform. You sign in with email (a one-time passcode is sent to your inbox) or with Google. You can invite teammates and assign them roles:
RoleCan do
OwnerEverything, including billing and deleting the organization.
AdminManage agents, knowledge, contacts, and team members.
UserWork with agents and content, without organization-level admin.

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Agents

An agent is a single chatbot. Each agent has its own:
  • Knowledge base — the content it can answer from
  • Instructions — its persona, tone, and rules
  • Model & settings — which AI model powers it and how creative it is
  • Appearance — colors, avatar, and widget behavior
  • Conversations & contacts — the people it has talked to
You can create as many agents as your plan allows, and duplicate an existing agent to reuse its configuration as a starting point.
Agents are fully independent. Training one agent on a document does not make that document available to your other agents.

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Knowledge base

The knowledge base is the set of sources an agent is trained on. A source can be a file, a website, a YouTube video, or a Q&A pair.

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How processing works

When you add a source, A2V2.ai doesn’t just store it — it makes the content searchable by the agent:
1

Ingest

Your file or URL is read and the raw text is extracted.
2

Chunk

The text is split into small, overlapping passages so the agent can retrieve just the relevant parts.
3

Embed & index

Each chunk is converted into a numerical representation (an embedding) and stored in a vector index. This is what lets the agent find the right passage for any question.
A source moves through statuses as this happens — from unprocessed to processing to Completed. Only completed sources are used in answers. If something goes wrong a source shows Failed, and you can reprocess it.

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How answers are generated (RAG)

When a visitor asks a question, the agent doesn’t guess from general knowledge. It uses Retrieval-Augmented Generation (RAG):
  1. The question is used to search your knowledge base for the most relevant chunks.
  2. Those chunks are handed to the AI model as context.
  3. The model writes an answer grounded in that context — and A2V2.ai shows you the sources it used.
This is why answers cite their sources, and why adding better content directly improves answer quality.

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Credits

Usage on A2V2.ai is metered in credits. You consume credits when:
  • An agent generates a message (cost varies by model)
  • A document is processed into the knowledge base
  • Voice transcription is used
Your organization has two credit pools:
PoolHow you get itResets?
Plan creditsIncluded with your subscription each billing periodYes — refilled every period
Top-up creditsPurchased manually or via auto-rechargeNo — they carry over
Turn on auto-recharge in billing so your agents never stop answering when a busy period drains your plan credits.
If credits run out, agents stop generating new answers until the balance is topped up or the plan period resets. You can watch your balance on the Usage and Analytics pages.

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The build loop

Everything above comes together in the loop you’ll repeat for every agent:
1

Create

Create an agent
2

Train

Add knowledge
3

Shape

Write instructions and pick a model
4

Test

Sandbox and Playground
5

Ship

Customize and embed