✨Platform Features
Everything you need,
from data to deployed decisions.
One platform for training, understanding, deploying, and improving predictive models.
Auto-train production-ready models
Drop in your dataset, choose the target, and Xplainable trains a model with the right preprocessing and sensible defaults - no notebooks required.
- Built-in preprocessing + feature handling
- Repeatable training runs and model versions
- Human-in-the-loop control when you need it
AutoTrain
BETA
customer_churn.csv
churn_flag
Binary Classification Detected2 unique values in target column
Recent Models
churn_model_v3
revenue_predictor
lead_scorer
🛠️Practical use
How teams use Xplainable
in practice
Start with a proven use case. Bring your own data. Deploy a model the business can trust.
Prioritise leads that are most likely to convert
- Score and rank leads using your CRM + marketing signals
- Explain why a lead is high priority with drivers + contributions
- Share reports with sales leadership to align on scoring logic
Lead Scores
LeadCompanyScoreStatus
SC
Sarah Chen92
Hot
JW
James Wilson78
Warm
ML
Maria Lopez65
Warm
AP
Alex Park41
Cold
RK
Rachel Kim87
Hot
DN
David Nguyen53
Cold
EF
Emma Foster71
Warm
TB
Tom Bradley34
Cold
Score Breakdown
Sarah Chen — Acme Corp
92
revenue_potential+28%
engagement_score+22%
company_size+18%
email_opens+15%
days_since_contact-8%
industry_match+12%
Conversion likelihood: High
💡Why Xplainable
Move from black-box prototypes
to decision-grade models
Without Xplainable
Building models
Notebooks, manual setup
Explainability
Post-hoc guesswork
Deployment
Custom infra + MLOps tickets
Iteration
Hard to track changes
Sharing
Static decks
Optimisation
Manual "what-ifs"
Governance
Scattered documentation
Collaboration
Siloed notebooks
Integration
Custom API wrappers
Time to production
Weeks to months
With XplainableAutoTrain in-app
Explainable by default
One-click deploy + keys
Versioning + visibility
Live interactive reports
Scenario + batch optimisation
Built-in audit trails
Team workspaces + comments
REST API + Python SDK
Hours to days
Free to start
Building modelsAutoTrain in-app
ExplainabilityExplainable by default
DeploymentOne-click deploy + keys
IterationVersioning + visibility
SharingLive interactive reports
OptimisationScenario + batch optimisation
GovernanceBuilt-in audit trails
CollaborationTeam workspaces + comments
IntegrationREST API + Python SDK
Time to productionHours to days
Free to start
🔍Explainability
Every prediction comes with a clear "why"
Xplainable makes model behaviour visible—so teams can trust outcomes, audit logic, and improve decisions together.
- Global drivers: what matters across the dataset
- Individual explanations: why this row scored the way it did
- Scenario testing: change inputs and see the impact
- Collaboration: comments + shared context for decisions
Waterfall Chart
Bar Chart
Table
Base Value
+14.0%
Gender
-0.3%
Smoker
-0.3%
State
-3.0%
Body Location
-0.3%
Activity Levels
-0.5%
Wellbeing Score
-0.3%
Severity (best)
+4.0%
Severity (worst)
+8.0%
No. of Sessions
+8.0%
Duration
+14.0%
Other
+16.0%
Total
+31.0%
0.000.050.100.150.200.250.30
💻Very simple setup
From data to deployed model
in four steps
in four steps
Connecting...
Data synced
Dashboard
API Calls
Success Rate
Workflows
🔧Platform overview
Xplainable does it all,
without the complexity
Industry agnostic
Solve scoring, forecasting, risk, and optimisation across teams.
🏦Finance
🏥Healthcare
🛒Retail
🏭Manufacturing
⚡Energy
📡Telecom
🏠Real Estate
🚗Automotive
✈️Travel
🎓Education
🛡️Insurance
📦Logistics
🎮Gaming
🍽️Hospitality
💊Pharma
🌾Agriculture
⚖️Legal
🎬Media
🔬Research
🏗️Construction
💼Consulting
🛍️E-commerce
🌐SaaS
📊Analytics
Cutting-edge AI technology
Auto-training + optional AI-assisted workflows for faster iteration.
Integrations to multiple sources
Bring CSVs today and connect data platforms as integrations expand.
Visualise in app
Charts, diagnostics, explainers, and reports built in.
Jan 20Jan 25Feb 1Feb 10Feb 15Feb 20
SourceConversationsGrowth
🌐Website
2,5317.3%
💬WhatsApp
1,2913.5%
Comprehensive dev kit
API-first deployment, keys, and clear request/response patterns.
API KEY
xpl_sk_••••••••••••••••REQUEST
RESPONSE
POST /predict
{
"data": [...]
}
200 OK
{
"score": 0.87
}
API Connected
MCP Server (External Integrations)
Connect external tools and workflows through a modern integration layer.
❓Common questions
Frequently asked questions.
Do I need a data science team to use Xplainable?
What types of problems can I solve with Xplainable?
How do I connect my data?
How does Xplainable explain predictions?
Can I deploy models to production?
How do you handle access and control?
Is Xplainable suitable for compliance-driven teams?
How does pricing work?
📚Learn more
Recent posts
💬Get in touch
Ready to see Xplainable
in action?
Leave us a message and we'll get back to you as soon as we can. Start exploring explainable AI for your team.
Free to start - no credit card required
Onboarding support included
Enterprise-ready security
