Your data science companion

Messy data in.
Trusted models out.

Guided ML and self-service features. Runs in your cloud. Your data never leaves.

Start a 2-week pilot See how it works
feature-store › requestlive
$ get CASA_ALL_BAL_AMB_AVG_AMT_M03
✓ approved · served at cust_id
$ get CASA_SA_BAL_AMB_AVG_AMT_M04
◐ feasible · computed once, sent for approval
$ get CASA_ALL_BAL_AMB_SLOPE_AMT_M06_QOQ
✓ approved · backfilled 24 months
$
90%of ML time lost to data prep
13guided steps to a model
148features from one config block
0raw rows leave your cloud
The problem

The model is the easy part.

Everything around it is where ML projects stall. We take on the highlighted parts.

Configuration
Data collection
Feature extraction
Data verification
ML code
Analysis
Process mgmt
Scoring
Monitoring
Resources
DS CompanionThe model
  • no mapNobody knows what data exists.
  • 6–18 moFeature stores take forever.
  • backlogEvery new feature is a ticket.
  • backfillHistory is rebuilt by hand.
  • driftModels decay quietly.
How it works

Source to decision. Inside your walls.

Every highlighted box is DS Companion: it creates the features, builds the models and keeps them running, all on your compute. Tap a stage.

Data flows from sources through normalization, the feature engine, the workbench and monthly scoring to business systems, with monitoring looping back. Only stats leave the environment. YOUR CLOUD OR ON-PREM · YOUR COMPUTE STATS ONLY SOURCES Core systemsaccounts, ledgers CRM & eventslogins, usage Warehousefiles, vendor data 1Normalizeone standard schema 2Intermediate layer+ metadata DS COMPANION · CREATE FEATURES 3Feature engine ✓ grammar check✓ computable?◷ Time MachineΣ aggregate⟲ checkpoint CREATEDFeature tablespoint-in-time 4ML Workbench13 guided stepsconfig + notebook CREATEDModel bundlemodel + report 5Monthly scoringsame recipe OUTCOMEBusiness systemsCRM, campaigns, risk Who to call.Who to approve.Who might leave. DS COMPANION · BUILD & RUN MODELS Monitoringdrift (PSI) RETRAIN WHEN THINGS SHIFT OUTSIDE DS Companion teamupdates, support,usage logs Never sees rowsor values.
DS Companion modulesYour systems and the outputs you keep

All. Raw tables in, decisions out, with monitoring closing the loop.

The stack

One companion. Create features. Build models.

Build models · start here

ML Workbench

From raw table to trusted model, step by step.

  • No-code, fully guided
  • Scores and monitors monthly
  • Exports a notebook you own
Try the 13 steps
Create features

Elastic Feature Store

Name a feature. Get it. No ticket.

  • Self-explaining feature names
  • Backfills with no ticket
  • Any grain, account to region
Build a feature
ML Workbench

13 steps. Zero guesswork.

Click through. Watch the config write itself.

    config.yaml
    
      
    
    
    
    
    
      Interactive decide as you go
      Full pipeline set and run
      Score every month
      Monitor catch drift
      Notebook the code is yours
    
    
  
    

    



    
  
    
    
    
      Elastic Feature Store
      
    Name it. Get it.
    
      
    One grammar for every feature. Request by name, and the engine does the rest.
    
    
    
    
    
      
    
        
    Build a name
    
        
    
          
          
        
    
        
    
        
    
        
    
        
    148features from one config block
    
      
    
      
    
        
    Request one
    
        
        
        
    
          
          
          
          
        
    
        
    
        
    Demo assumes 24 months of history.
    
      
    
    
    
    
    
      
    Time Machine
    Point-in-time history. No leakage.
    
      
    Any grain
    Account → customer → region.
    
      
    Checkpoints
    Failed jobs resume, not restart.
    
      
    Any platform
    Databricks, Azure, AWS, on-prem.
    
    
    
  
    

    



    
  
    
    
    
      Your data
      
    Your data stays put. We come to it.
    
    
    
    
    
      
    
        Your environment
        
    All the data
    
        
    • Source tables
    • Feature tables
    • Training data
    • Models and scores
    
      
    
      
    
        Yours to keep
        
    All the work
    
        
    • Models
    • Notebooks and code
    • Config and decision log
    • Reports
    
        
    Leave anytime. Keep everything.
    
      
    
      
    
        Reaches us
        
    Just stats
    
        
    • Usage logs
    • Summary stats (opt-in)
    
        
    Never rows, names or values.
    
      
    
    
    
  
    

    



    
  
    
    
    
      What you get
      
    Nine files. All yours.
    
    
    
    
    
      
    yamlfeature_catalog.yamlGoverned catalog
    
      
    pqfeatures_2026_09.parquetPoint-in-time features
    
      
    yamlconfig.yamlEvery decision, replayable
    
      
    pklmy_model_v1.0.joblibScoring bundle
    
      
    htmlmodel_report.htmlMetrics, calibration, SHAP
    
      
    nbpipeline.ipynbEditable notebook
    
      
    pqscores_2026_10.parquetMonthly scores
    
      
    psidrift_report_2026_10Drift check
    
      
    logrequests.logAudit trail
    
    
    
  
    

    



    
  
    
    
    
      Scale
      
    Scale output, not headcount.
    
    
    
    
    
      
    Faster models
    weeks of setupguided path, reusable config
    
      
    More features
    ticket → backlogrequest → compute
    
      
    One language
    every team, its own namesone grammar, shared
    
      
    Every skill level
    rigor depends on whorigor by default
    
      
    Models that last
    drift found too latePSI on every score
    
      
    Audit-ready
    data shipped to vendorsruns on your compute
    
    
    
    
    
      Banking who takes a deposit?
      SaaS who churns?
      Telecom who’s slipping?
      Lending who needs cash?
    
    
  
    

    



    
  
    
    
    
      Where we fit
      
    The whole loop, not one piece.
    
    
    
    
    
      
    
        
    DS CompanionDrag-and-drop MLCloud ML platformsFeature registries
    
        
          
    Guided for non-experts✓some——
    
          
    Scoring + monitoring✓—extra setup—
    
          
    Notebook you own✓—some—
    
          
    Creates features✓—someregisters only
    
          
    Data stays home✓dependstheir cloud✓
    
            
      
    
    
    
    
    General categories. Individual products vary.
    
  
    

    


    



    
  
    
    
    
      Pilot
      
    One model.
    Two weeks.
    Yours to keep.
    
      
    Bring a model you’ve been meaning to build. We build it with your team.