Enterprise Software Development
Build software around the way your organization actually works. We develop web applications, backend services, APIs, data-processing tools, and workflow solutions for clearly defined business and operational requirements.
Typical engagements include:
• Custom web applications
• Backend systems and APIs
• Workflow and process automation
• Data integration and processing
• Reporting portals and internal tools
• Enhancements to existing applications
Custom ML Products
Build the model your problem actually requires
Custom ML development is appropriate when an organization has a valuable decision to automate or improve, but no off-the-shelf product fits its data, operating environment, or constraints.
We begin by testing whether the proposed use case is technically and commercially viable. If it is, we develop the solution around agreed business and model-performance requirements.
A typical engagement includes:
- Business-problem and decision framing
- Data-readiness assessment
- Baseline development
- Feature and model development
- Backtesting and failure analysis
- Acceptance criteria agreed before the full build
- Production pipeline or integration support
- Technical documentation and handover
- Retraining and monitoring guidance
Advanced Analytics and Reporting
Turn complex data into clear, decision-ready information. We build automated reporting systems, analytical dashboards and management-information solutions that help teams monitor performance, identify patterns and act with confidence.
Typical engagements include:
• Executive and operational dashboards
• Automated MIS and periodic reporting
• KPI design and performance monitoring
• Data consolidation and transformation
• Trend, variance and root-cause analysis
• Advanced statistical analysis
• Planning and decision-support reports
Independent Model Validation
Know how a model behaves before relying on it
Internal development teams are often too close to a model to provide a genuinely independent assessment.
Cristo Labs reviews models developed internally or by third parties and documents whether their
assumptions, implementation, and reported performance are supported by reproducible evidence.
Validation can cover:
- Independent replication of reported results
- Data lineage and preprocessing review
- Target leakage and backtest-integrity testing
- Benchmark and challenger-model comparison
- Stability across time periods and operating conditions
- Sensitivity and failure-boundary analysis
- Class-imbalance and rare-event performance
- Explainability and documentation
- Review against the organization’s model-risk policy
Forecasting and Demand Intelligence
Move from forecasts to operational decisions
Forecasting has limited value when it ends with a prediction that planners must interpret manually.
We build forecasting and decision systems that connect expected demand or operating conditions with specific actions involving inventory, capacity, staffing, allocation, or replenishment.
Suitable applications include
- Demand forecasting
- Inventory and replenishment planning
- Capacity and workforce planning
- Item- and location-level forecasts
- Allocation and prioritization
- Anomaly and state-change detection
- Ranked operational action lists
Our engagement principles
Fixed scope: Each engagement has defined deliverables, acceptance criteria and an end point.
Evidence before expansion: We use a focused feasibility phase before recommending a larger production build.
Transparent modeling: Assumptions, limitations and failure conditions are documented—not hidden behind a score.
Your data environment: Deployment is designed around your security, infrastructure and data-governance requirements.
Practical handover: Documentation and operational guidance are part of the delivery, not optional extras.