32+ projects delivered across six industry sectors. From predictive models trained on 9 million data points, to smart mobility platforms running across three metro cities in India, to hyper-logistics forecasting for a unicorn achieving 90%+ accuracy. Every project on this page was built and delivered by our team.
Client names withheld where commercially sensitive. Outcomes, methodologies and technologies are accurate. Full references available in active discussions.
What we built: An adaptive peak identification engine trained on a cloud library of chromatogram patterns. The platform classifies haemoglobin peaks automatically, flags unknowns for clinician review, and integrates into existing laboratory workflows. Explainability layer shows clinicians why each result was classified as it was — critical for clinical trust and audit trail.
What we built: A unified analytics platform pulling from scheduling, billing, and EHR systems into a single real-time operational view. Dashboards surface billing gaps before they age, flag appointment non-compliance, and benchmark provider productivity against targets — enabling daily operational decisions instead of monthly retrospectives.
What we built: A HIPAA-compliant cloud submission platform handling CMS 1500 and dental claim formats. Automated validation catches errors before submission. Full audit trail on every claim movement. Generates both electronic submissions and printable forms for payers that require paper. Reduced claim rejection rates and compliance risk simultaneously.
What we built: A multi-payer data integration layer combining clinical records, insurance claims, and patient self-reported data. ML risk stratification models identify high-cost, high-risk patients before acute episodes occur. Integrated care coordination tools allow intervention teams to act on predictions rather than react to outcomes.
What we built: A performance benchmarking dashboard comparing individual practitioner vaccination rates against national and regional targets. Integrates with CME/learning systems to auto-suggest educational interventions when rates fall below threshold. Visualises trends by cohort, age group, and vaccine type to support population-level programme decisions.
What we built: Multi-modal risk prediction combining genomic markers, lifestyle questionnaire responses, and behavioural patterns. Risk scores presented through a gamified engagement layer to improve adoption beyond the clinical audience. Deployed for insurer and corporate wellness programmes as a proactive, pre-symptom intervention tool.
What we built: An AI-driven care coordination platform integrating with at-home diagnostic kits to capture readings in real time. Risk stratification models prioritise high-risk pregnancies for immediate clinical review. Built-in communication tools allow care teams to respond within the platform — replacing disconnected phone and email workflows.
What we built: A hyperlocal data integration layer combining air quality sensor feeds, patient-reported symptoms, and lifestyle inputs. Predictive models identify individuals at elevated pulmonary risk before symptoms escalate to acute events. Deployed across hospital and insurer wellness programmes as a population-level early warning system.
What we built: A causal matrix approach applied to 9 million historical loan data points, identifying leading indicators that predicted default 3–6 months before they materialised. Integrated into the loan approval workflow as a real-time risk scoring signal — not a post-hoc report — giving approvers actionable intelligence at the point of decision.
What we built: A cloud-based risk forecasting platform trained on multi-year borrowing history with causal feature engineering identifying non-obvious default patterns. 72% classification accuracy — a material improvement on the baseline — translating directly into measurable NPA reduction and released capital for higher-performing lending.
What we built: A multi-source churn prediction model pulling from contact centre transcripts, branch visit frequency, social media sentiment, and transaction velocity. Surfaces VIP customers on the 90-day pre-churn path to relationship managers — with the most likely churn reason and recommended intervention — achieving up to 40% churn reduction.
What we built: An automated KYC/AML pipeline with jurisdiction-aware rule engines, behavioural risk scoring, and a case management interface for compliance teams. Real-time sanctions screening, PEP checks, and adverse media monitoring integrated throughout the onboarding journey — reducing onboarding time while closing compliance gaps.
What we built: A real-time price aggregation and arbitrage detection engine monitoring 530 exchanges and 1,170 digital asset pairs simultaneously. Sub-second latency on opportunity identification. Signal generation feeds execution infrastructure with configurable risk limits. Handles exchange API failures gracefully via circuit breakers and fallback pricing sources.
What we built: A propensity-to-renew model mapping historical payment behaviour, product holdings, and engagement signals to renewal probability. Output segments drove specific outreach strategies — digital nudges for high-propensity, agent calls for borderline. Result: 26% lift in positive renewal decisions and significant reduction in campaign cost per conversion.
What we built: A portfolio of AI capabilities deployed across a financial services group — fraud detection on transaction data, churn scoring for retail banking, arbitrage signals for the exchange business, and LLM-powered claims triage for the insurance arm. Each solution purpose-built to the domain's specific data and decision context.
What we built: Credit scoring incorporating non-traditional signals — utility payments, mobile usage patterns, behavioural data — alongside traditional bureau data. Enabled the lender to extend credit to thin-file borrowers with lower actual risk than bureau scores indicated, while identifying cross-sell opportunities within the existing portfolio.
What we built: A differential privacy layer applied at ETL stage, injecting calibrated Laplace noise into sensitive fields before data reaches the analytics environment. Privacy budget configurable per dataset and use case. Analysts retain full statistical utility for aggregate analysis while individual records are provably protected — enabling compliance without sacrificing data science capability.
What we built: A procurement analytics platform mapping spend across vendor relationships, categories, and time. Trend analysis identified the specific vendor and item combinations driving cost inflation — enabling category managers to renegotiate contracts with data rather than instinct. 12% cost and delay reduction achieved in the first department before rollout to the full estate.
What we built: Multi-method segmentation applying CHAID decision trees, K-means clustering, and classification models across transactional and behavioural data. Output segments enriched with lifetime value projections and mapped to specific campaign and loyalty strategies — with a financial case for the intervention cost per segment.
What we built: Apriori and FP-Growth association rule mining on item-level transaction data. Rules ranked by lift, confidence, and support — filtered for actionability (minimum basket size, category relevance). Output fed directly into planogram design, promotional bundle creation, and digital recommendation engine logic.
What we built: Hybrid forecasting combining SARIMA for seasonal patterns with gradient boosting for promotional and external signal effects. Forecasts at SKU × store × week granularity, feeding replenishment triggers and warehouse staffing models. Uncertainty intervals allow buyers to make risk-adjusted decisions rather than relying on point estimates.
What we built: Monte Carlo simulation fitting probability distributions to historical demand and lead time variability per SKU. Safety stock recommendations generated at configurable service level targets — allowing merchandisers to explicitly trade off stock holding cost against stockout risk by category.
What we built: Price elasticity modelling across product categories, identifying where discounting drives incremental volume vs where it cannibalises margin. Optimisation layer finds the discount depth that maximises gross profit contribution per category — replacing gut-feel promotional decisions with data-backed discount architecture.
What we built: An uplift modelling approach isolating customers who genuinely change behaviour due to coupon stimulus — versus those who convert regardless. Combined logistic regression and gradient boosting ensemble trained on test-and-control campaign data. Reduced wasted coupon spend while maintaining conversion volume.
What we built: Brand affinity scoring combining purchase frequency, basket share, and cross-brand switching patterns across the full customer base. Scores personalise digital and in-store marketing, allocate brand investment more precisely, and design loyalty programme rewards that reinforce high-affinity behaviours.
What we built: An attrition prediction model combining HR system data (tenure, performance, leave patterns, role changes) with engagement survey signals. Weekly risk scores surfaced to line managers with the most likely contributing factors — enabling targeted interventions before resignation, not after. 30% attrition reduction measured over 12 months post-deployment.
What we built: NLP pipeline processing employee survey free-text and internal social channel data to extract sentiment, topic clusters, and motivational themes at team and location level. Dashboards show HR and store managers what's driving engagement at their specific location — moving from annual survey cycles to near-real-time signals.
What we built: Developer productivity analytics combining code commit patterns, PR review velocity, collaboration graph analysis, and incident data. Identifies bottlenecks, knowledge silos, and training needs at individual and team level. Privacy controls ensured aggregate insights rather than individual surveillance for most metrics — critical for team trust and adoption.
What we built: Real-time mobility intelligence ingesting GPS feeds from the public transport fleet, passenger counting sensors, and mobile check-in data. Predictive algorithms anticipate congestion and crowding 20–30 minutes ahead, feeding dynamic scheduling and routing adjustments. Deployed and operating across three major metro cities in India — commute time savings of 15–20 minutes per journey post-deployment.
What we built: Multi-constraint route optimisation applying heuristic and ML approaches across employee home locations, shift start times, vehicle capacities, and traffic patterns. Dynamic re-optimisation handles last-minute changes. Reduced per-employee transport cost while improving on-time pickup rates — critical for shift-based operations where late transport directly affects SLA performance.
What we built: Multi-channel incident reporting via mobile app, wearable triggers, and SMS. Geo-location attached to every report, streamed in real time to control room dashboards. Enables immediate dispatch decisions based on incident type, location, and nearest available resource — used by law enforcement and field safety teams.
What we built: ANPR-based compliance system integrated with the VAHAN national vehicle database. Real-time plate recognition triggers automated checks for pollution certificate validity, road tax status, and insurance. Compliance alerts routed to the relevant authority with full audit log and evidence capture for enforcement proceedings.
What we built: Logistics planning combining route optimisation, trailer assignment, and Hours of Service-compliant driver scheduling. Real-time GPS integration lets planners see actual vs planned position and adjust dynamically. Role-based access serves planners, dispatchers, and drivers with the view each needs. Fuel savings and distance reduction measured from baseline in the first quarter of operation.
What we built: Real-time demand and supply forecasting at hyper-local zone level using stream processing and adaptive ML models that continuously update on live data. Dynamic pricing engine adjusts rates based on predicted supply-demand imbalances — improving both driver allocation efficiency and financial yield. 90%+ accuracy sustained across diverse zone types and demand patterns.
What we built: A freight flow modelling platform built in collaboration with the World Bank Group for urban logistics planning. Aggregated data from customs, port authorities, road sensors, and carrier manifests to map freight movement patterns. Network analysis identified chronic congestion nodes and under-utilised corridors. Warehouse location optimisation gave city planners evidence-based infrastructure investment alternatives.
What we built: GPS telemetry-driven pay calculation replacing manual timesheet processes. Driving hours, waiting time, loading time, and distance calculated automatically from device data and verified against dispatch records. Pay disputes — historically the largest source of driver relations issues — dropped significantly once drivers could see the same data underlying their pay.
What we built: A dispatch interface designed around how planners actually work — visual, real-time, keyboard-minimised. Drag-and-drop order and driver assignment with live constraint checking (capacity, hours, route feasibility) on every action. Integrates with TMS and ERP via APIs to keep system-of-record data consistent without double-entry.
If a project above looks like a challenge you're sitting inside, let's talk. We don't pitch speculatively — we run a 90-minute scoping session where we tell you honestly what's achievable and what it will realistically take.