The governed intelligence layer

One governed intelligence layer for every system you run and every AI you use.

Ask a question in Claude, Copilot, ChatGPT, Slack or our own app. Optialy reads your systems live, computes every figure in code and cites where each number came from.

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Works inside ClaudeCopilotChatGPTSlackOptialy app32 live connectors
Optialy
Why did ROAS drop last week?
Understanding the question
Reading Meta Ads, Shopify and GA4 live
Computing in a secure sandbox
Checking and citing every figure
ROAS, last 8 weeksdaily, all channels

Sample data

Reads live from 32 connectors across ads, analytics, commerce, CRM, finance and files

Meta AdsGoogle AdsTikTok AdsAmazon AdsGoogle Analytics 4ShopifyAmazonKlaviyoGorgiasSalesforceHubSpotNetSuiteMeta AdsGoogle AdsTikTok AdsAmazon AdsGoogle Analytics 4ShopifyAmazonKlaviyoGorgiasSalesforceHubSpotNetSuite
QuickBooksBigQueryGoogle DriveOneDriveFile uploadsSlackFirefliesJiraGWIStatistaWeatherYahoo FinanceQuickBooksBigQueryGoogle DriveOneDriveFile uploadsSlackFirefliesJiraGWIStatistaWeatherYahoo Finance
The problem

The data is all there. Lining it up in time is the problem.

No single system knows all four. A question that spans them is assembled by hand, so it mostly goes unasked.

Ad platformsknow what you spent
CRMknows who converted
One question

Which campaigns kept spending on products we couldn't ship, and what did it cost us?

Spent $18.4k212 converted3 SKUs sold out$6.1k margin lost
Lined up by Optialy, cited, in minutes
Inventoryknows what can ship
Financeknows what it was worth

Sample figures

“Every report requires logging into 10+ platforms and exporting CSVs manually.”
“Leadership asks questions that require days of analysis but expects answers in hours.”
“I'm not confident in AI-generated insights without seeing sources.”

Pain points recorded from beta-user feedback and our persona research, October 2025.

The platform

One governed layer. Five things it does.

Start by asking. Grow into watching, forecasting, planning and acting, each on the same governed path.

Copilot$4.82M
Claude$4.82M
Slack$4.82M

Analyze Live

Cited answers across every connected system, in the app or your AI.

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$48.2k sales yesterday, +6%
Rain shell: 9 days to stock-out
Board chart refreshed

Monitor In build

Routines that watch for you, and charts that refresh themselves.

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Predict Pilot Oct 2026

Multi-step intelligence, and forecasting models built from a conversation.

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Google
Meta
TikTok

Decide Engine built

Ranked, costed plans that must beat “change nothing”.

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Create issuePending
Update dealApproved

Act In build

Write back to tools like Jira and the CRM, only after a person approves.

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How it works

Watch a question become an answer.

Six steps, every one visible. Agents plan and read; code does the math; nothing is acted on without a person.

Elicit 1

Asks before guessing

Why did our sales decline over the last two weeks?
Net sales or gross? → Net sales

Govern 2

Approved definitions, your access

“Net sales” = approved definition
Read as you, your access only
Same metric in every tool

Swarm 3

Agents read each source live, in parallel

Shopify
GA4
Klaviyo

Analyze 4

Math runs as code in a sandbox

# secure sandbox
d = join(shop, ga4, email)
d.decompose("net_sales")
→ 3 drivers found

Converge 5

One cited answer, with a chart

cited

Act 6

Drafts the fix; a person approves

Pause email flow BRestock alert to opsApprove
BeforeAn analyst exports from three tools, lines them up in a spreadsheet and replies days later.
With OptialyOne question, every relevant source read live, a cited answer and a drafted next step in minutes.

Scenario from our demo script, on an e-commerce environment with Shopify, GA4 and Klaviyo connected.

Measured, not claimed

Computed beats generated: 97.5% vs 65.8% of figures correct.

4,500 real marketing and commerce questions on live connected data, each graded blind by three AI judges against figures computed from the source systems, then checked manually by our team.

RAG pipeline0%
Optialy0%

Figures correct. Each square is 0.1% of graded figures.

Answer quality, out of 5

RAG pipeline0
Optialy0

Answers with a chart

RAG pipeline0
Optialy0

Why a RAG baseline

Plain ChatGPT or Claude can't connect to most of these systems, so many of the questions are impossible for them. The fair comparison is a pipeline that can.

Internal benchmark, September 2026. RAG pipeline: retrieval, then the language model reads the data and writes the figures itself (our earlier architecture).

Connect

Every system, read live. Nothing copied.

Sign in once per source. Optialy reads each system where the data lives, under each person's own access, custom fields included.

Ads and analyticsCommerce and CRMFinance and ERPWarehouses and filesResearch panels
Browse integrations
Results

What customers saw in the first months.

From our lead enterprise account's paid pilot, now in beta, and our beta customers.Read the customer story

Campaign efficiency lift

Acting on Optialy's recommendations, as reported by the customer

Analyst hours saved per question

Simple, multi-system and complex questions, across beta customers

Systems connected, one by one

From 1 to 4 system groups, each added at the customer's request

1Advertisingpilot
2Analyticsexpansion
3E-commerceexpansion
4CRMnow in beta

One question. Every relevant source. Trusted wherever work happens.

Start with one question that costs you money today. We'll answer it on your live data.

Start a pilotWatch the product demo