Grounded answers
Every response is anchored to your datasets, documents and dashboards with inline citations.
Product
Conversational AI grounded in your enterprise data
Deploy assistants that answer questions in natural language, with citations, governance and observability built in.
Bring your own model
Use Avaloka inference, OpenAI, Anthropic or open-source models — switch with a config change.
Governed by design
Permissions, masking and PII redaction are enforced inside the agent runtime, not bolted on.
Production observability
Trace every prompt, retrieval and tool call. Score quality, latency and cost over time.
Conversational AI you can put in front of customers
95%+
Citation accuracy
Measured on internal benchmarks.
20+
Tool integrations
SQL, dashboards, Slack, Jira and more.
<2s
P95 response
On 10K-doc knowledge bases.
100%
Audited traffic
Every prompt logged and scoreable.
How it works
- 01
Connect knowledge
Point your assistant at datasets, file libraries and dashboards in Avaloka.
- 02
Configure behavior
Set tone, guardrails, escalation rules and which tools the assistant can call.
- 03
Test & evaluate
Run golden-question suites and human review on every release before shipping.
- 04
Deploy & monitor
Ship to web, Slack or your app and watch quality, latency and cost in real time.
Frequently asked questions
How do you prevent hallucinations?+
Responses are grounded in retrieved context, citations are required and a confidence threshold blocks low-quality answers.
Can the assistant take actions?+
Yes. Tools let it run SQL, file tickets or call your APIs — gated by the user's own permissions.
Which models are supported?+
Avaloka inference, OpenAI, Anthropic, Mistral, Llama and any OpenAI-compatible endpoint.
Is my data used to train models?+
No. Your data is never used to train shared models. All inference runs in your workspace boundary.
Launch your first AI assistant in days
See Conversational AI grounded in your real data — not a generic demo.