Machine Learning That Earns Its Place in Daily Decisions
Kramora builds machine learning systems that fit the decisions and workflows your teams already own. Our engineers start from the question a model needs to answer, test whether your data can support it, and then design forecasting, language, vision or classification capabilities that connect to existing applications. Evaluation and monitoring are planned from the first sprint, so models stay measurable once real traffic arrives.
What We Deliver
ML modeling
Supervised and unsupervised models for classification, scoring, ranking and anomaly detection, trained on your historical data and validated against a clear baseline.
- Lead and churn propensity scoring
- Anomaly detection on transaction or sensor data
- Ranking for search and recommendations
Forecasting
Time-series models that predict demand, volume or capacity, with uncertainty ranges planners can actually use.
- Inventory and demand forecasts by location
- Staffing and service volume projections
- Cash flow and billing forecasts
Natural language processing
Text classification, entity extraction, sentiment and summarization for tickets, emails, contracts and other unstructured text.
- Support ticket routing and tagging
- Clause and entity extraction from agreements
- Theme analysis across customer feedback
Computer vision
Image and video models for inspection, counting, identification and layout understanding where cameras or scans already exist.
- Visual defect detection on production lines
- Site photo classification and tagging
- Product image quality checks
AI integration
Models packaged as versioned services and wired into CRMs, ERPs, portals and internal tools, with fallbacks when a prediction is uncertain.
- Prediction APIs behind existing applications
- Batch scoring jobs feeding reports
- Confidence thresholds that route cases to people
Evaluation and monitoring
Offline test sets, live performance tracking, drift detection and retraining triggers that show whether a model is still doing its job.
- Holdout and backtest evaluation reports
- Data and prediction drift alerts
- Model registry with version history
Business Problems & Meaningful Outcomes
Solution Architecture
Illustrative reference architecture showing how a predictive model moves from source data to a decision inside a business application.
Predictions are exposed through documented APIs or scheduled batch outputs, so consuming applications never depend on model internals. Each response carries a model version and confidence value.
Training data access, retention and masking rules are agreed during discovery. Sensitive attributes are reviewed for both privacy exposure and unwanted bias.
Model outputs are designed to land inside existing CRMs, ERPs or internal tools. Low-confidence cases follow a defined fallback path instead of failing silently.
How We Work
Frame the decision a model should support, audit the available data and agree how success will be measured.
- Use-case brief and success criteria
- Data availability and quality review
- Baseline to beat
Choose modeling approaches, feature pipelines and serving patterns that fit your infrastructure and latency needs.
- Model and feature design
- Serving and integration plan
- Evaluation protocol
Train candidate models, compare them against the baseline and test the integrated service end to end.
- Evaluation report
- Model service in staging
- Known limitations log
Release behind monitoring, watch real-world behavior and schedule retraining based on drift and feedback.
- Monitoring dashboard
- Retraining runbook
- Improvement backlog
Technology Choices
Example Scenarios
Demand forecasting for a regional distributor
A regional distributor plans stock from last year's sales and manager intuition, and often overstocks slow items while running short on popular ones.
Kramora would start by backtesting simple statistical baselines against the distributor's sales history before trying more complex models. The chosen forecast would feed the existing planning tool by location and week, with ranges and a regular accuracy review.
Visual inspection on a packaging line
A packaging manufacturer relies on manual spot checks to catch label and seal defects, and inspectors cannot review every unit.
Kramora would collect and label images from existing line cameras, then train a vision model that flags likely defects for an inspector to confirm. Flagged images and inspector decisions would be stored to measure precision and improve the model over time.
Frequently Asked Questions
Discovery includes a data review covering volume, history, labeling, gaps and how well the data reflects the decision you want to support. Sometimes the honest answer is that a rules-based approach or better data collection should come first. You get that assessment before any model build begins.
Related Solutions
Adjacent Services
Industries We Serve
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Share what you are trying to achieve with AI & machine learning and the systems involved. We will suggest a practical next step.