Everything between the source and the answer
DataLens is organised around the lifecycle a piece of data actually travels, not the org chart of the team that owns it. Each stage below is a working surface in the product.
The seven stages
📥 Data
Land rows from files, databases, APIs, CRMs, lakes and document stores.
🔄 Prepare
Fix nulls, duplicates, types and names — with the AI proposing the fixes.
🏗 Model
Reverse-engineer a source model, design a target, and map between them.
⚡ Orchestrate
Build the flow on a canvas, run it, version it, promote it.
📊 Analyse
Charts, pivots and scenario simulation over the data you just prepared.
🚚 Deliver
Publish governed data products and access-controlled shares.
🛡 Govern
Profiling, quality rules, lineage, PII detection and AI cost observability.
Capabilities that cut across every stage
Ari, the data assistant
Reachable from every screen. It has the profile of the active dataset in front of it, so its answers are about your columns rather than about data in general.
Workspaces
A workspace holds the datasets, models, flows and releases for one piece of work, and switching between them swaps the whole context at once.
Versioned releases
A release seals models, mappings, prompts, golden datasets and flows together, then promotes them through environments as a unit.
AI operations
Cost, token use, latency, failure taxonomy, cache hit rate and estimate-versus-actual for every AI step the platform runs for you.
Prompt library
Prompts are versioned artefacts bound to flows, not strings pasted into a node and forgotten.
Document generation
Turn a governed dataset and its lineage into a document — a data dictionary, a quality report, a handover pack.
Or start from the problem
The same platform, described by the job rather than the stage.
See it on your own data
DataLens is in private beta. Bring a file, a database or an API and work through the whole lifecycle in one sitting.
Request beta access