[{"data":1,"prerenderedAt":204},["ShallowReactive",2],{"svc-detail-data-analytics-predictive-ai":3,"sub-work-data-analytics-predictive-ai":162,"ill-services\u002Fdata-analytics-predictive-ai":203},{"slug":4,"short":5,"title":6,"tagline":7,"answer":8,"summary":9,"icon":10,"subServices":11,"useCases":65,"benefits":81,"process":86,"tools":102,"integrations":117,"industries":124,"faq":131,"relatedWork":150,"relatedServices":154,"seo":159},"data-analytics-predictive-ai","Data & Predictive AI","Data Analytics & Predictive AI services","Forecast, score and detect with models you can trust, on data you can defend.","Data analytics and predictive AI services turn your operational data into forecasts, risk scores and dashboards that drive decisions: demand and revenue forecasting, churn prediction, fraud and anomaly detection, customer analytics and real-time BI. Built for finance, retail, SaaS and operations teams that want calibrated predictions and one trusted view of the business rather than competing spreadsheets.","Predictive analytics, time-series forecasting, churn and fraud models, recommendation engines, customer analytics and BI dashboards, built on your data, validated honestly and deployed with monitoring so accuracy holds up in production.","chart",[12,16,20,24,29,33,37,41,45,49,53,57,61],{"slug":13,"name":14,"text":15},"predictive-analytics","Predictive analytics","Models that estimate what will happen next (demand, payments, failures, conversions) with calibrated probabilities and explanations.",{"slug":17,"name":18,"text":19},"demand-forecasting","Forecasting & demand planning","Sales, demand, capacity and cash-flow forecasts using classical and deep-learning time-series methods.",{"slug":21,"name":22,"text":23},"recommendation-engines","Recommendation engines","Product, content and next-best-action recommendations from behavioural data and embeddings.",{"slug":25,"name":26,"text":27,"page":28},"churn-prediction","Churn prediction","Identify customers likely to leave, why, and which intervention is most likely to keep them, delivered into your CRM or success tooling.",true,{"slug":30,"name":31,"text":32,"page":28},"fraud-detection","Fraud & anomaly detection","Real-time and batch detection of fraudulent transactions, accounts and unusual behaviour with explainable alerts and review queues.",{"slug":34,"name":35,"text":36},"customer-analytics","Customer analytics","Segmentation, lifetime value, cohort and journey analysis to focus acquisition and retention spend.",{"slug":38,"name":39,"text":40},"bi-dashboards","BI dashboards","Power BI, Looker Studio, Metabase or Plotly dashboards designed around decisions, not vanity metrics.",{"slug":42,"name":43,"text":44},"real-time-analytics","Real-time analytics","Streaming pipelines and live views for operations, fraud and monitoring use cases.",{"slug":46,"name":47,"text":48},"ai-dashboards","AI dashboards & natural-language BI","Ask questions of your data in plain language and get charts, answers and explanations.",{"slug":50,"name":51,"text":52},"financial-modelling","Financial modelling","Scenario, risk and pricing models with clean assumptions and audit-ready outputs.",{"slug":54,"name":55,"text":56},"time-series-forecasting","Time-series forecasting","Multi-horizon forecasting for prices, load, traffic and sensor data with uncertainty intervals.",{"slug":58,"name":59,"text":60},"data-pipelines-warehousing","Data pipelines & warehousing","ETL\u002FELT with Airflow or Prefect into Postgres, BigQuery or Snowflake: the foundation every model depends on.",{"slug":62,"name":63,"text":64},"exploratory-data-analysis","Exploratory data analysis","Fast, rigorous investigation of your data to answer specific questions and find what to build next.",[66,69,72,75,78],{"title":67,"text":68},"Reduce churn","Score accounts weekly, surface risk drivers and trigger retention plays before customers leave.",{"title":70,"text":71},"Catch fraud early","Flag suspicious transactions and accounts in real time with a review queue and low false-positive rates.",{"title":73,"text":74},"Plan inventory and capacity","Forecast demand by product and location to cut stock-outs and waste.",{"title":76,"text":77},"One source of truth for KPIs","Replace competing spreadsheets with governed pipelines and dashboards everyone trusts.",{"title":79,"text":80},"Personalise offers","Recommend products, content and prices from behaviour and context.",[82,83,84,76,85],"Decisions backed by calibrated probabilities","Earlier detection of risk and anomalies","Less inventory and capacity waste","Models that keep working after launch",[87,90,93,96,99],{"step":88,"text":89},"Data audit and feasibility","Inventory sources, assess quality and answer concretely whether the data supports the prediction you want.",{"step":91,"text":92},"Pipeline and features","Reliable ingestion, cleaning and feature engineering into a warehouse or feature store.",{"step":94,"text":95},"Model development","Baselines first, then candidate models with time-aware validation, explainability and a written model card.",{"step":97,"text":98},"Deployment","Serve predictions by API, batch or directly into CRM and BI tools with monitoring.",{"step":100,"text":101},"Monitoring and retraining","Drift detection, performance tracking and a retraining schedule your team can run.",[103,104,105,106,107,108,109,110,111,112,113,114,115,116],"Python","scikit-learn","PyTorch","XGBoost","Prophet","Pandas","Airflow","Prefect","BigQuery","Snowflake","PostgreSQL","Power BI","Plotly","AWS SageMaker",[118,119,120,121,122,123],"Salesforce","HubSpot","Stripe","Shopify","Google Analytics","Segment",[125,126,127,128,129,130],"finance","retail-ecommerce","saas-technology","manufacturing","logistics","healthcare",[132,135,138,141,144,147],{"q":133,"a":134},"How much data do we need?","Less than most teams think for a first model, but it must be relevant and reasonably clean. Churn models need a history of customers who left; forecasting needs enough seasons to learn from. The feasibility study answers this concretely for your case before you commit to a build.",{"q":136,"a":137},"How do you avoid over-promising accuracy?","Time-aware validation, honest baselines, confidence intervals and a written model card. If a model does not beat the simple baseline meaningfully, we say so and recommend fixing data or process instead. Accuracy is reported on held-out data, not training data.",{"q":139,"a":140},"Do we need a data warehouse first?","Not always. Many engagements start with a targeted analysis on exported data, and we only propose infrastructure once the value is proven. When you do need pipelines, we build them with Airflow or Prefect into Postgres, BigQuery or Snowflake. A modest, well-governed warehouse usually pays for itself quickly once two or more teams depend on the same numbers.",{"q":142,"a":143},"Can predictions feed directly into our tools?","Yes. Churn scores can land in Salesforce or HubSpot, fraud alerts in a review queue, forecasts in your planning system or BI dashboards. We deliver predictions where decisions are made rather than in a separate report. Each integration includes a score explanation, so a salesperson or analyst understands why a record was flagged and what to do next.",{"q":145,"a":146},"How do you keep fraud models from blocking good customers?","We optimise for the business trade-off, not just accuracy: tune thresholds against the cost of false positives, use explainable features so reviewers understand alerts, and add feedback loops from analyst decisions so the model improves. We also report the false-positive rate as a first-class metric alongside catch rate, because both drive the real cost of the system.",{"q":148,"a":149},"Will the model still work next year?","Only if it is monitored. Customer behaviour, prices and fraud patterns drift, so we ship monitoring and a retraining playbook with every model, and can operate it for you under a retainer. A retraining cadence (monthly for fast-moving fraud patterns, quarterly for stable forecasts) is agreed at handover, with alerts for anything that drifts sooner.",[151,152,153],"stock-market-deep-learning","healthcare-data-analysis","marketing-eda",[155,156,157,158],"mlops-ai-infrastructure","nlp","ai-consulting-strategy","ai-automation",{"title":160,"description":161},"Predictive Analytics, Churn & Fraud AI | AIYatch","Predictive AI and analytics: forecasting, churn prediction, fraud detection, recommendation engines, customer analytics and BI dashboards on your data.",[163,177,190],{"path":164,"title":165,"description":166,"industry":167,"services":168,"results":169,"cover":176,"coverAlt":176},"\u002Fwork\u002Fstock-market-deep-learning","Deep-learning market analysis for a fintech","Time-series and sentiment models feeding a portfolio-monitoring platform for an investment-focused fintech.","Finance & Fintech",[4,156],[170,173],{"value":171,"label":172},"22%","improvement in model-guided returns vs. baseline strategy",{"value":174,"label":175},"Daily","automated retraining and reporting",null,{"path":178,"title":179,"description":180,"industry":181,"services":182,"results":183,"cover":176,"coverAlt":176},"\u002Fwork\u002Fhealthcare-data-analysis","Clinical data analysis for a hospital network","Machine-learning analysis of patient records that surfaces patterns and risk indicators for clinicians.","Healthcare",[4],[184,187],{"value":185,"label":186},"30%","improvement in early risk-flag accuracy vs. manual triage",{"value":188,"label":189},"Minutes","not days, for cohort analysis",{"path":191,"title":192,"description":193,"industry":194,"services":195,"results":196,"cover":176,"coverAlt":176},"\u002Fwork\u002Fcustomer-feedback-nlp","NLP for customer feedback analysis","Topic, sentiment and urgency models over reviews and tickets, feeding a weekly voice-of-customer report.","SaaS & Technology",[156,4],[197,200],{"value":198,"label":199},"50k+","reviews and tickets classified per month",{"value":201,"label":202},"Weekly","automated voice-of-customer report","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Cpath d=\"M100 90V390H560\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M549.6 396L560 390L549.6 384\"\u002F>\u003Cpath d=\"M106 100.4L100 90L94 100.4\"\u002F>\u003Crect x=\"140\" y=\"320\" width=\"46\" height=\"70\" rx=\"8\" stroke-width=\"3.5\"\u002F>\u003Crect x=\"210\" y=\"270\" width=\"46\" height=\"120\" rx=\"8\" stroke-width=\"3.5\"\u002F>\u003Crect x=\"280\" y=\"290\" width=\"46\" height=\"100\" rx=\"8\" stroke-width=\"3.5\"\u002F>\u003Crect x=\"350\" y=\"220\" width=\"46\" height=\"170\" rx=\"8\" stroke-width=\"3.5\"\u002F>\u003Crect x=\"420\" y=\"180\" width=\"46\" height=\"210\" rx=\"8\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M163 300C200 280 210 262 233 262S290 275 303 270S350 215 373 200S420 165 443 160\"\u002F>\u003Cpath d=\"M443 160C480 150 500 130 540 100\" stroke-dasharray=\"2 9\"\u002F>\u003Cpath d=\"M535.5 111.1L540 100L528.1 101.7\"\u002F>\u003Cpath d=\"M443 160L540 62M443 160L540 130\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"233\" cy=\"262\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"373\" cy=\"200\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"443\" cy=\"160\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003C\u002Fg>\u003C\u002Fsvg>",1792973685796]