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LYRA/ Tools/ AI & Optimization/ AI Advisor (Chat Agent)
AI & Optimization AI-assistedChat-driven

Every Lyra tool, one chat.

AI Advisor is the chat surface of the Lyra Agent. The Agent reads your real account data, answers strategy questions, and when you ask for an action it dispatches the matching Lyra tool — bid optimisation, campaign creation, health analysis, change-history alerts. Read-only by default; nothing writes to your account without you saying yes.

Read-only by default · Approval gate on every action · Conversation history kept 30 days
Scope · what the advisor can read and run

Your account, in scope. 52 tools, ready to call.

AI Advisor reads from three places in your Lyra workspace and can dispatch any of 52 registered functions. It never invents context; everything in its responses is grounded in something you've configured or written.

Scope · sample · agency portfolio 4 inputs in scope · 52 dispatch targets
01 · Accounts Connected Google Ads accounts The advisor sees the same customers and campaigns you see in the customer-portal dashboard. Live metrics, last-sync timestamps, all of it. 5 connected · MCC 8873-197-205
02 · Diary Decision history account_diary_entries — every decision, note, and rationale you've logged. The advisor reads these for context before answering. 94 entries · last 12 months
03 · Onboarding Your preferences customer_onboarding_contexts — what you told us at setup. Goals, brand-protection rules, target ROAS, locations to never exclude. 5 contexts · 14 active rules
04 · Tools Function-call dispatch table Every Lyra capability the advisor can run on your behalf when you ask for an action. New functions get added as we ship new tools. 52 functions registered
Sample · advisor in action

What it looks like in chat.

Four real-shape scenarios. The advisor reads your account, reasons about the question, dispatches the matching Lyra tool, and waits for your approval before anything writes. Click a chip to switch.

Lyra Advisor · Basalt Running · 5-account portfolio demo · pick a scenario
Model evidence · three sources, one model

What the advisor reads, every turn.

Three DB sources ground every advisor response. No third-party data, no synthesised competitive intel, no scraped benchmarks — what you've stored is what the advisor reads.

Signal 01Account state

The same data your dashboard shows.

customer_cache holds the live account state — connected customers, campaigns, conversion data, last-sync timestamps. The advisor pulls from the same table the customer-portal dashboard reads, so the numbers it cites are the numbers you see.

Source · customer_cache · refreshed by data-sync engine
Signal 02Decision history

Every decision you've logged is in scope.

account_diary_entries holds the running record of decisions and rationale — what you paused, why, when. The advisor reads these for context, so questions like "remember when we restructured Brand · Search?" land somewhere real.

Source · account_diary_entries · per customer
Signal 03Your preferences

Hard constraints the advisor obeys.

customer_onboarding_contexts holds what you told us at setup — target ROAS, brand-protection rules, locations to never exclude. The advisor treats these as constraints; if a recommendation would violate one, it's flagged before the chat suggests it.

Source · customer_onboarding_contexts · 14 active rules
Product facts · how the advisor behaves

A chat, on a short leash.

What it produces A conversation, not a commit .

Every response is text plus an optional dispatch suggestion. Tool execution requires your explicit yes. Read-only at the model level — the Agent can't bypass the approval gate.

What it reads Your account, your diary , your rules.

Three DB sources; nothing else. No third-party benchmarks, no scraped competitive intel, no synthesised numbers. What's in your workspace is what the advisor knows.

What it dispatches 52 registered Lyra functions.

Bid optimisation, campaign creation, health analysis, change-history activation, search-terms management, and 47 more. The function table is the agent's tool surface — if a capability isn't there, the advisor can't run it.

What it persists Conversation history, 30 days.

Every chat is saved in chat_conversations. Resume tomorrow, share with a teammate, audit later. Nothing is shared across customers or accounts.

Frequently asked

The honest answers about the chat.

Can the advisor change my Google Ads account on its own?

No. Every action that mutates Google Ads — bid changes, campaign creation, applied recommendations — runs through an explicit approval step. The model cannot bypass it; the gate is enforced in the dispatch layer, not the prompt. Whatever the advisor proposes, you approve before it ships.

What's actually powering the chat?

The Lyra Agent — our name for the AI layer across the product. Today it runs on Claude Sonnet from Anthropic, via the function-calling pattern. The Agent receives your message, decides whether to read more from your DB, optionally proposes a function call, and returns text plus an optional dispatch suggestion. The orchestration code is ours; we may change the underlying model as the field evolves — the contract with you is the Agent's behaviour, not the model behind it.

What can the advisor not do?

Anything that doesn't have a registered function. The 52-function table is the boundary — if a Lyra capability isn't there, the advisor can't run it. New functions get added as we ship new tools.

Does the advisor remember our last conversation?

Yes, for 30 days — every conversation is saved in chat_conversations. The advisor reads recent history alongside your diary to answer follow-ups in context. After 30 days the chat is archived; the diary entries it produced stay in place.

Why use the advisor instead of running the tools directly?

Speed and synthesis. The advisor reads your account before answering, so you skip the pull-metrics → form-question → find-the-right-tool cycle. For an experienced operator, going direct to a tool can still be faster — both paths exist.

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