THE QUESTION LAYER FOR AGENTS

Your team only
finds the insights
it thinks to look for

Allyvate’s agents continuously ask the questions humans don’t—surfacing hidden growth opportunities across your customer data before anyone writes a query.

See what your data is hiding →How agentic discovery works

Always on

No prompt required

Context aware

Learns your metrics

Growth focused

Ranks material impact

AGENTIC DISCOVERY LOOP
GROWTH INSIGHT SURFACED

Customers buying Category A in their first 30 days have
2.7× higher 12-month value.

2.7xVALUE SIGNAL
RETENTION AGENT

Which first-30-day behaviors predict 12-month value?

EXPANSION AGENT

Which product combinations precede account expansion?

EFFICIENCY AGENT

Where is spend rising without incremental revenue?

QUESTION SPACE EXPLORED
QUESTION TESTEDSIGNAL DETECTEDHYPOTHESISMATERIAL INSIGHT
AGENTS GENERATE + TEST QUESTIONS
CONNECTED CUSTOMER CONTEXT
BUSINESS CONTEXT LAYER
WAREHOUSE
Amazon RedshiftBigQueryPostgreSQLSnowflake
PRODUCT
ShopifyQuickBooks
CRM
SalesforceHubSpot
KNOWLEDGE
ConfluenceGoogle Drive
QUESTION → TEST → VALIDATE → RANKNEXT SCAN IN PROGRESS ···

Generic AI only guesses
It can’t tell you which question will drive growth

Generic AI looking at transactions

UNGOVERNED

“Which early behavior predicts long-term value?”

CANDIDATE SEGMENTVALUELIFT
Category A ≤30d$1,7822.7×
Category B ≤30d$1,1161.7×
Promo users$7421.1×
All others$6601.0×
MANY POSSIBLE DEFINITIONS
2.7×ORDERS_ENRICHED
2.4×CUSTOMER_360
3.1×POS_TRANSACTIONS
2.2×CAMPAIGN_EXPORT
2.9×LTV_SNAPSHOT
WHICH IS RIGHT?

AI built to continuously discover insights

GOVERNED

“What behavior is actually driving durable growth?”

VALIDATED FINDING

2.7×12-month value

Customers buying Category A within their first 30 days
versus customers who do not.

VALUE WINDOW  12 MONTHSCOMPARISON  LIKE-FOR-LIKEMETRIC  NET VALUE

ANALYTICAL QUESTION

Does early Category A purchase predict durable customer value?

THREE CHECKS BEFORE ACTION

✓ Cohort created

FIRST 30 DAYS

✓ Value mapped

12-MONTH WINDOW

✓ Bias checked

LIKE-FOR-LIKE

THE LIMIT IS NOT YOUR DATA

You are not short on data. You are short on questions

Analysts are limited by time, hypotheses and attention. The questions nobody has time to ask are exactly where growth tends to hide.

BOUNDED BY HYPOTHESES

You only test what you already suspect

Dashboards answer the questions built into them, so unexpected drivers of value are never examined.

BOUNDED BY CAPACITY

Analysis is rationed to the loudest request

Analytical time goes to reporting and urgent asks, not open ended discovery across the customer base.

BOUNDED BY PROMPTS

Generic AI still waits to be asked

An assistant that answers prompts inherits the same blind spots as the person writing them.

HOW DISCOVERY WORKS

Agents ask the questions nobody got to, then prove which ones matter

Allyvate continuously turns business context into hypotheses, tests them with evidence, and surfaces growth opportunities.

HOW A QUESTION BECOMES AN ANSWER
Signals from product behaviour, acquisition channels, customer segments, revenue outcomes and experiment history feed a continuous insights engine that generates, prioritises, tests and learns, surfacing growth insights ranked by impact — the top one at 92% confidence: move the value moment earlier for mid-market teams
  1. 01 Signals: connected live — product behaviour, acquisition channels, customer segments, revenue outcomes and experiment history
  2. 02 Continuous insights engine: builds competing hypotheses, chooses the most informative test, and updates its understanding with every result, in a loop of generate, prioritise, test, learn — every result becoming context for the next investigation
  3. 03 Growth insights, ranked by impact — top validated opportunity at 92% confidence: move the value moment earlier for mid-market teams, since collaborative setup in session one is the strongest predictor of activation (impact high, confidence strong, priority 01); also refine paid acquisition by intent signal (67%) and target retention plays to at-risk customer cohorts (81%)
Postgres, BigQuery, Snowflake and SQL Server feeding the context layer
DATABASES YOU USE
Toggled runs resolving into charted, auditable outputs
AUDITABLE RESULTS
Query, insight, semantic and classification agents working in concert
AUTONOMOUS AGENTS
THE INSIGHT LAYER FOR AGENTS

Context is the foundation. Insight is the edge.

The context layer makes scattered enterprise data legible to AI. The insight layer sits on top of it and surfaces the questions nobody thought to ask. Read the official overview

Agent 1
Agent 2
Agent 3

The Insight Layer

Surfaces the questions nobody thought to ask

Context layer

Makes enterprise data legible to AI

Warehouse
CRM
Finance data
Sales data

Scattered across the business

A three-tier candy-striped ring, like a database symbol, in blue, blush and mint

Grounded in business context

Builds metrics, entities, and definitions autonomously.

A braided ring in blue, blush and mint shaped like a clock, with a braided hour and minute hand

Continuously tested

Generates hypotheses and tests them against your data.

Three braided bars of rising height, like a bar chart, in blue, blush and mint

Ranked by impact

Ranks findings by measured effect on your core metrics.

USE CASES

Agents test insights,
then uncover growth drivers

RETENTION AGENT

Discover the early signals of long-term value

Surfaces the first actions & metrics that separate durable customers from one-time buyers.

EXPANSION AGENT

Find the patterns that precede account growth

Identifies product combinations and usage shifts that reliably lead to larger relationships.

EFFICIENCY AGENT

Find where spend stops producing revenue

Flags segments and channels where ad costs are rising without a matching incremental return.

  • Data Context: connect a warehouse and build governed context — metrics, entities and definitions — from dbt manifests.
  • Insights feed: surfaced findings tagged opportunity, anomaly or forecast, each with a confidence score.
  • Insight detail: one finding opened to its evidence, a trend chart and three ranked recommendations.
Allyvate's Data Context screen: connecting a warehouse and building governed context from dbt manifestsA feed of surfaced insights, each tagged opportunity, anomaly or forecast with a confidence scoreOne insight opened to its evidence: a trend chart and three ranked recommendations
CONNECTS TO THE SYSTEMS YOUR CUSTOMER DATA ALREADY LIVES IN
SnowflakeDatabricksSalesforceHubSpotShopify
BigQueryBigQuery
Amazon RedshiftRedshift
PostgreSQLPostgreSQL