The offer's agents
Amir and Nadia at work
One stage of the CRO cycle each: what the agent does, what it is trained on, what it delivers, and what the expert decides.
Data & Analytics
AI Data Insights
Query your data and turn every insight into action
Connect an AI analytics layer to your data warehouse, based on a shared semantic model and verified by test questions, then link every useful insight to your test backlog.
The need
What you tell us
« My dashboards exist but no one looks at them, and my data platform's AI gives figures that no one validates. I want to query my data in natural language with accurate answers, and have every insight lead to action. »
- « I want to query my data in natural language »
- « Our AI gives different numbers than the dashboards »
- « Should you activate Cortex, Genie or Copilot? »
- « No one looks at my dashboards »
Our response
- Semantic model: metrics defined once
- Native conversational analytics or custom agents
- Insights pushed forward and converted into tests
Method
Our method in 5 steps
Our stance
From question to action
Your AI reports on metrics defined once and verified through test questions. Every useful insight goes into your test backlog.
- Semantics
- Test questions
- Action
Step 01 : Scoping
Selection of priority business questions, mapping of your warehouse and access, inventory of AI tools already available on your platform, and selection of the program's value KPIs.
- Priority business questions
- Mapping of the warehouse and access
- Native AI tools already available
- Value KPIs of the setup
Step 02 : Semantic model
Definition of metrics and dimensions, building the semantic layer (dbt, LookML or semantic views), governance of the exposed scope, and a test question set with known answers.
- Metric and dimension definitions
- Semantic layer (dbt, LookML, semantic views)
- Governance and exposed scope
- Test questions with known answers
Step 03 : AI Analytics
Trade-off between your platform's native tool and custom agents, deployment of conversational analytics and anomaly detection, tracking of an accuracy score on test questions.
- Native arbitration or custom agents
- Conversational analytics connected
- Anomaly detection configured
- Score on test questions
Step 04 : Distribution
Targeted digests and alerts, integrated into your teams' rituals, training on asking the right question, and a user feedback loop.
- Targeted digests and alerts
- Integration into team rituals
- Training on asking the right question
- User feedback loop
Step 05 : Action
Converting insights into prioritized hypotheses in your test backlog, measuring usage and value, with test questions replayed at every evolution.
- Insights converted into hypotheses
- Prioritisation in the test backlog
- Measuring usage and value
- Test questions replayed at every release
Conversion Intelligence
Our experts, backed by a fleet of AI agents
Nadia vérifie la collecte avant que l'IA ne s'y branche, Amir transforme les insights en hypothèses priorisées dans votre backlog. Le modèle sémantique et l'analytics IA sont portés par nos consultants data.
Agents on a daily basis
Describe the need, Claude calls the agent
A request in plain language or a "/" command: the right agent takes over, you keep the decision.
Architecture and fleet
A CRO architect manages a fleet of agents
The same CRO process, with and without agents: each agent takes over, the waiting disappears and the learning loop speeds up.
Deployment
Agents trained like experts, updated like products.
Each agent is a versioned plugin on GitHub. Three ways to make the most of it.
During a mission
During the engagement
Our consultants call on the agents at every stage of the CRO cycle. You receive the deliverables, without installing anything.
Via the Welyft platform
Remote, at Welyft
The fleet runs on our platform, connected to your tools with your consent. An expert validates every deliverable.
Internalized within your organization
Within your organization
We build your agents using our method, customize them, then a GitHub plugin marketplace delivers their updates.
Deliverables
What you get
Semantic model
Metrics and dimensions documented, shared by AI and dashboards.
AI Analytics in production
Native or custom, connected to the warehouse.
Digests, alerts and hypotheses
Pushed to teams and added to the test backlog.
Test question set
Accuracy score replayed with each update.
Who is it for?
Who is this offer for?
Tools used
- BigQuery
- Snowflake
- Databricks
- dbt
- Looker
- Claude
- MCP
- Slack
- Company with a data warehouse (BigQuery, Snowflake, Databricks)
- Native AI tools activated or planned, low adoption
- Sponsor: Head of Data, Digital Director, CRO Manager
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