The fastest, most secure operating system ever built. A bare-metal 32-bit x86 OS engineered from the ground up — no Linux kernel, no borrowed code, no compromises. Next-gen security and performance.
A 7-agent AI call center platform — call handling, knowledge base retrieval, sentiment analysis, QA scoring, caller simulation, training engine, and resource optimization. Full telephony integration with native desktop and web deployment. Built to eliminate API costs entirely.
A 6-agent HR automation platform — recruitment, onboarding, compliance monitoring, employee pulse tracking, performance management, and resource optimization. Includes integrated telephony for voice-based HR workflows.
An enterprise competitive intelligence platform with six specialized agents that autonomously monitor competitors, detect market shifts, and deliver prioritized intelligence briefs. Web, desktop, and API deployment options.
A 6-agent AI sales lead generation platform — prospecting, enrichment, qualification, outreach, analysis, and resource optimization. Engineered for zero API costs at scale. Available as an agent system, web app, and desktop application.
Turn historical data into forward-looking certainty. Our forecasting models quantify risk, project outcomes, and surface decision-critical signals before they surface in your reports.
Go beyond dashboards. We build end-to-end analytics pipelines — from raw data ingestion to statistical modelling — delivering insights that drive measurable business outcomes.
Custom-built ML models trained on your data. From gradient boosting and ensemble methods to deep learning architectures — engineered for accuracy, interpretability, and scale.
Clean, structured, reliable data is the foundation of every great model. We architect robust data pipelines, warehouses, and feature stores that make your data model-ready.
Models degrade silently. We instrument your production models with real-time performance tracking, data drift alerts, and automated retraining triggers to keep accuracy sharp.
Rigorous statistical thinking behind every model. From experimental design and A/B testing to causal inference and hypothesis validation — we ensure your conclusions are defensible.
Autonomous AI agents that plan, reason, and act across your systems. From single-task automation to multi-agent orchestration pipelines — we design, build, and deploy agents that execute complex workflows without human intervention.
Bridging intelligent software with the physical world. We develop AI-driven robotics solutions — from perception and planning systems to autonomous navigation and manipulation — integrating ML models directly into robotic platforms for real-world decision-making.
AI agents represent the next frontier of enterprise automation — systems that don't just answer questions, but autonomously pursue goals, use tools, make decisions, and adapt to changing conditions. At Cognivarys, we design and engineer production-grade AI agents built on the latest large language model architectures, integrated directly into your existing data infrastructure and business processes.
Unlike traditional automation, our agents combine the analytical precision of our ML models with the reasoning capability of foundation models — giving them the ability to interpret unstructured data, plan multi-step workflows, call external APIs, and escalate to humans when uncertainty is high.
An agent that autonomously monitors market signals, competitor filings, news feeds, and internal data — synthesising a daily briefing with prioritised insights, flagged anomalies, and recommended actions. Replaces hours of manual analyst time every morning.
Connects to your data warehouse, runs predefined queries, interprets the results against prior periods, writes narrative commentary, and distributes formatted reports to stakeholders — on schedule, every time, without a human touching it.
Continuously monitors customer behaviour signals across CRM, product usage, and support channels — proactively identifying churn risks, upsell opportunities, and support escalations, then drafting personalised outreach for CS teams to review and send.
Monitors your data pipelines in real time, detects anomalies in schema, volume, or distribution, diagnoses root causes by querying upstream systems, and either auto-remediates known issues or raises structured incident reports for engineers.
Extracts, classifies, and validates information from unstructured documents — contracts, invoices, compliance filings, clinical notes — at scale. Cross-references extracted data against internal systems and flags discrepancies for human review.
Combines your predictive models with an LLM reasoning layer to provide contextual, explainable recommendations to decision-makers. Presents the model's forecast alongside supporting evidence, confidence levels, and relevant precedents — in plain language.
Predictive analytics is the practice of extracting patterns from historical and real-time data to forecast future events, behaviors, and outcomes. At Cognivarys, we don't just apply off-the-shelf models — we engineer bespoke predictive systems calibrated to the statistical properties of your specific data.
Whether you need to forecast demand, anticipate customer churn, score credit risk, or predict equipment failure — our models are designed to be accurate, explainable, and operationally deployable.
Machine learning at Cognivarys is a disciplined engineering practice — not a black box. Every model we build is accompanied by a model card documenting its intended use, performance characteristics, known limitations, and fairness evaluation.
We work across the full ML stack: from exploratory data analysis and feature engineering through to cross-validated model selection, interpretability analysis, and production-grade deployment with monitoring.
"We don't build demos. We build systems that ship, scale, and get smarter over time."
Cognivarys Industryes started with a simple frustration: too many AI projects die in notebooks. Great models get built, impressive demos get shown, and then nothing ships. We started this company to change that — to build AI systems that actually run in production, make real decisions, and deliver measurable results.
Today we build across the full spectrum of intelligence — from predictive analytics and machine learning models to autonomous multi-agent systems, operating systems, and robotics. We've shipped an OS from scratch, deployed 6-agent platforms for HR, sales, and competitive intelligence, and built a suite of live prediction products serving real users every day.
Where we're going is bigger. We're building the infrastructure for businesses that think — systems where AI agents don't just answer questions but autonomously plan, decide, and execute. Every solution we ship comes with explainability, audit trails, and monitoring. Because intelligence without trust isn't intelligence — it's a liability.
We audit your data sources, evaluate quality and completeness, and identify the analytical problems with the highest business impact to solve first.
Our data scientists engineer features, benchmark algorithms, and select the optimal model architecture — balancing accuracy, interpretability, and computational efficiency.
Rigorous cross-validation, bias testing, and performance benchmarking ensure your model generalizes reliably — not just on training data, but in the real world.
We deploy models into production with full observability — tracking drift, performance degradation, and data pipeline health continuously over time.
Gradient boosted models trained on payment history, behavioural signals, and macroeconomic indicators to predict default probability with calibrated confidence intervals — replacing rigid rule-based scorecards with dynamic, data-driven credit decisions.
Real-time anomaly detection using isolation forests and autoencoders that flag suspicious transactions in under 50ms. Models continuously retrain on new fraud patterns, adapting faster than rule-based systems ever could.
Ensemble time series models combining ARIMA, Prophet, and LightGBM with external economic signals to produce rolling 12-month revenue forecasts with quantified uncertainty bands — enabling finance teams to plan with confidence.
Reinforcement learning and mean-variance optimisation models that dynamically rebalance portfolios based on predicted return distributions, volatility regimes, and correlation shifts — going well beyond static Markowitz allocation.
SKU-level demand models incorporating seasonality, promotions, weather, and competitor pricing to predict future sales volumes. Integrated directly into inventory management systems to automate replenishment decisions.
Survival analysis and gradient boosting models that score every customer's 30/60/90-day churn risk based on purchase recency, browsing behaviour, support interactions, and engagement patterns — enabling targeted retention interventions.
Reinforcement learning models that optimise real-time price decisions across thousands of SKUs — balancing margin, volume, and competitive positioning. Models account for price elasticity, inventory levels, and demand signals simultaneously.
Probabilistic CLV models (BG/NBD, Pareto/NBD) combined with spend prediction to score every customer's long-term revenue potential — powering smarter acquisition spend allocation and personalisation prioritisation.
Logistic regression and gradient boosted models trained on clinical, demographic, and social determinant data to flag patients at high risk of 30-day readmission — enabling proactive discharge planning and targeted follow-up care.
Longitudinal ML models that track biomarker trajectories over time to predict disease onset and progression — enabling earlier interventions and more personalised treatment protocols for chronic conditions.
ED attendance, surgical volume, and bed occupancy forecasting models that give hospital operations teams advance notice of demand surges — enabling proactive staffing, resource allocation, and patient flow management.
Unsupervised and semi-supervised models that detect unusual billing patterns, duplicate claims, and potential fraud in healthcare claims data — protecting payers and providers from leakage without disrupting legitimate claims processing.
Sensor-based ML models that predict equipment failure 72+ hours in advance using vibration, temperature, pressure, and operational cycle data. Deployed at the edge for real-time inference without cloud latency dependencies.
Statistical process control augmented with ML anomaly detection on production line sensor streams — identifying quality drift and defect patterns in real time, before defective units reach downstream assembly or customers.
Multi-factor models that score supplier risk, forecast component shortages, and simulate supply disruption scenarios — enabling procurement teams to diversify suppliers and build strategic buffers before disruptions materialise.
Gradient boosted regression and neural network models that identify the process parameter combinations driving maximum yield — enabling continuous improvement without costly physical experiments across production lines.
Product usage, support ticket, and engagement signal models that score every account's renewal and expansion probability — giving CS teams a prioritised list of accounts to focus on at every point in the renewal cycle.
Gradient boosted classification models trained on firmographic, behavioural, and intent data to score inbound and outbound leads by conversion probability — enabling sales teams to focus time where it converts best.
Bayesian time series models that forecast usage-based revenue at the account and cohort level — giving finance and GTM teams accurate ARR projections that account for expansion, contraction, and seasonal usage patterns.
Unsupervised ML models that monitor product telemetry streams and flag anomalous usage patterns — surfacing early signals of user struggle, potential security incidents, and infrastructure degradation before they escalate.
Traditional credit scoring systems react to delinquency after it occurs. We built a gradient boosted ensemble that ingests 140+ behavioural and transactional features — including spend velocity shifts, missed minimum payment patterns, and balance utilisation trends — to flag accounts at elevated default risk up to 90 days in advance.
The model outputs calibrated probability scores, not binary flags, allowing collections teams to triage outreach intensity proportionally to risk level. Integrated directly into the bank's CRM via a real-time API, the system scores every active account nightly and surfaces a daily action list for relationship managers.
A mid-market retailer was managing inventory decisions manually using spreadsheet-based heuristics, resulting in chronic overstock on slow movers and persistent stockouts on high-velocity SKUs. We replaced this with a hierarchical time series forecasting system — training individual LightGBM models per product category augmented with shared global patterns via cross-learning.
The system ingests promotional calendars, weather data, local event signals, and competitor pricing feeds alongside historical sales to produce daily 28-day rolling forecasts per SKU per location. Forecast outputs integrate directly into the ERP system to automate replenishment purchase orders, with human override capability preserved for edge cases.
A precision parts manufacturer was experiencing unpredictable CNC machine failures causing costly unplanned downtime. Existing maintenance schedules were calendar-based and took no account of actual machine condition. We instrumented 47 machines with vibration, temperature, spindle load, and acoustic sensors — streaming 14 signals at 100Hz per machine.
An LSTM autoencoder trained on normal operating signatures learns each machine's healthy baseline. Reconstruction error spikes signal anomalous degradation patterns. A secondary XGBoost classifier, trained on 18 months of labelled failure events, then predicts failure type and remaining useful life. Models run at the edge on Raspberry Pi 4 compute nodes, with alerts pushed to maintenance tablets in real time.
A B2B SaaS company with 2,400 enterprise accounts had no systematic way to identify which accounts were at risk before they submitted a cancellation notice. We built a composite account health score from 60+ product telemetry, support, and engagement signals — login frequency, feature adoption depth, support ticket sentiment, executive sponsor engagement, and NPS trajectory.
A survival analysis model (Cox PH) estimates each account's probability of churning within 30, 60, and 90 days. A separate expansion model identifies accounts showing signals of readiness for upsell. Both models feed a Customer Success dashboard that re-scores all accounts daily, giving CS managers a prioritised daily action queue ranked by revenue at risk and expansion opportunity.
An NHS trust was struggling with reactive staffing in its Emergency Department — frequently understaffed during surge periods and overstaffed during quiet periods, resulting in both patient safety risks and avoidable labour costs. We built a 14-day rolling ED attendance forecast using a Temporal Fusion Transformer model trained on 4 years of hourly attendance data.
The model integrates public holiday calendars, local event data, seasonal respiratory illness trends, and historical day-of-week and hour-of-day patterns to forecast attendances at 4-hour resolution. Forecasts are published automatically each morning to rota managers via a Tableau dashboard, with confidence intervals and anomaly flags for unusual predicted surges. Staffing recommendations are generated automatically from forecast outputs using a separate optimisation layer.
Six AI-powered prediction platforms — completely free, no signup required. Real-time data. Production ML models. Built by Cognivarys Industryes for everyone.
Just pick a tool and start using it.
Visit PredictionAgent.ai ↗All PredictionAgent tools are 100% free — no signup, no credit card, no limits. Built on production ML models and deployed on Vercel infrastructure by Cognivarys Industryes.
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