Lorikeet

Drive the product through a documented public API G

Agent access

Connect an agent via an official MCP server G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Operate the product with natural-language commands G

Agentic features

Set up automations that run autonomously in the background G

Agentic features

Issue scoped/least-privilege API credentials for an agent G

Agent access

Use an official CLI G

Agent access

Build against official SDKs G

Agent access

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Rely on versioned APIs with a documented deprecation policy G

Api quality

Run the product headlessly / in CI for automation G

Agent access

Subscribe to events via webhooks G

Agent access

Test against a sandbox environment without touching production data G

Api quality

Guardrails stop the agent from inventing policies, prices, or promises — off-knowledge questions get a safe decline, not a guess C

Hallucination

Prevent my data from being used to train AI models G

The agent ingests my help center, docs, past tickets, and internal wikis as knowledge sources without manual re-authoring C

Ingestion

The agent takes real actions through my APIs — refunds, order changes, subscription updates — with scoped auth per action C

Actions

Define rules that trigger actions automatically on events G

The agent fully resolves a meaningful share of conversations end-to-end — measured as resolutions, not mere deflections or bounces C

Resolution

When the agent escalates, the human gets the full conversation, a summary, and collected details — the customer never repeats themselves C

Handoff

Dashboards show resolution rate, CSAT, handoff rate, and cost per resolution — the numbers I report to my exec team C

Analytics

The agent runs inside my existing helpdesk — Zendesk, Salesforce, Intercom — or standalone, syncing tickets and context both ways C

Helpdesk

Every answer is grounded in my own content and shows which article or source it drew from C

Grounding

Export all of my data in open formats and leave G

Self-host the core product G

I test the agent against historical tickets or simulated conversations before it faces real customers C

Simulation

Answers use the customer's live data — plan, order status, account history — not just generic help articles C

Personalization

I encode standard operating procedures the agent follows step-by-step for known issue types, with deterministic branching C

Procedures

Perform bulk operations across many items at once G

Do everything through the API that I can do in the UI G

One agent covers chat, email, and in-app, plus the channels my customers actually use — Slack, WhatsApp, social C

Channels

The agent asks clarifying questions and works through multi-step troubleshooting instead of dumping one canned answer C

Reasoning

I configure when the agent must hand off — by topic, sentiment, customer tier, or explicit request — and it reliably obeys C

Rules

I mark topics as human-only — legal threats, cancellations, security — and the agent never freelances on them C

Topic controls

Control data retention and deletion G

The agent handles phone calls — speech in, speech out — with the same knowledge and actions as chat C

Voice

Pricing is outcome-based and published — I pay per resolution with caps and controls, not an opaque enterprise quote G

Pricing

Schedule recurring jobs or workflows G

Knowledge stays current automatically — the agent re-syncs sources on a schedule or on change, not via manual re-uploads C

Freshness

Launch in a supervised mode where the agent drafts replies for human approval before anything reaches a customer C

Supervision

Opt out of telemetry and usage tracking G

Choose where my data is stored (region/residency) G

Read the product's source under an open license G

The agent supports customers in many languages, even where my knowledge base exists only in English C

Languages

AI conversations get ongoing QA — scored samples, flagged failures, and a review loop that feeds fixes back into the agent C

Qa

The platform surfaces knowledge gaps and conflicting content that cause the agent to miss or fumble questions C

Gaps

I control the agent's tone and brand voice, and it stays consistent across topics and languages C

Voice

Version, review, and roll back my automations G

The platform clusters conversations by topic and surfaces emerging product issues before they spike ticket volume C

Insights