Custom AI &
Machine Learning
Bespoke AI systems built around your unique data, use cases, and business objectives. No templates. No off-the-shelf. Just solutions that fit exactly what you need.
When off-the-shelf AI
isn't enough
Generic AI tools are built for the average problem. Your problem isn't average. YIME designs and builds machine learning systems from scratch — shaped by your data, your workflows, and the outcomes your business actually needs to move.
Whether it's a recommendation engine, a fraud classifier, a churn predictor, or something that doesn't have a name yet — if it can be solved with data and intelligence, we'll build it.
Custom AI is the
answer
when you hear this
If any of these sound familiar, off-the-shelf tools aren't enough — and a custom ML system built on your data is what will actually move the needle.
"Our data is too specific for general AI tools to understand our domain."
"We tried a SaaS AI product — the accuracy isn't good enough for production."
"Our data can't leave our infrastructure — we need a self-hosted solution."
"There's no existing product that solves our exact problem."
"We need the model to improve automatically as we collect more data."
"We need to understand why the model made a decision — not just what it decided."
Pick your problem.
We'll
build the solution.
Select a solution type to see how we approach it, what we build, and what outcomes to expect.
Recommendation Engine
Hyper-personalized recommendations that surface the right product, content, or action for each user — increasing engagement, conversion, and revenue without increasing ad spend.
- 20–40% increase in click-through rate
- Handles cold-start for new users and items
- Contextual signals (time, device, session) baked in
- Sub-100ms serving for real-time experiences
- A/B testing and multi-armed bandit optimization
Fraud Detection System
Real-time transaction scoring that catches fraud before it happens — without generating false positives that hurt genuine customers. Built to handle high-throughput streaming data at millisecond latency.
- 99%+ precision with low false positive rate
- <50ms decision latency on live transactions
- Behavioral fingerprinting for account takeover
- Explainable decisions for compliance and review
- Continuous learning from analyst feedback
Churn Prediction Model
Predict which customers will leave — 30 to 90 days before they do — and trigger automated retention actions while there's still time to act. Turn churn from a lagging metric into a leading signal.
- 15–30 day advance warning on churn risk
- Segment-level and individual risk scoring
- Feature importance explaining why each customer is at risk
- Integration with CRM for automated outreach
- Proven 15–25% retention improvement
Process Optimization with RL
Reinforcement learning agents that learn the optimal policy for complex sequential decisions — pricing, routing, scheduling, bidding — in environments where rules-based systems break down.
- Dynamic pricing that adapts to demand in real time
- Route optimization beyond what heuristics can achieve
- Safe exploration with constraint satisfaction
- Simulation-first training before live deployment
- Continuous improvement as the environment changes
Anomaly Detection System
Detect unusual patterns in operational, financial, or IoT data — without needing labeled examples of what "wrong" looks like. Unsupervised intelligence that flags what falls outside the expected envelope.
- Works on time series, logs, transactions, and sensor data
- Configurable sensitivity and alert thresholds
- Explainable anomaly scoring with contributing features
- Integrates with PagerDuty, Slack, or custom webhooks
- Learns normal behavior seasonality and trends
Graph Machine Learning
When your data has relationships — users and products, transactions and accounts, devices and networks — Graph ML unlocks patterns that tabular models can't see. Built for fraud, recommendations, and knowledge graphs.
- Fraud ring detection in financial transaction networks
- Social graph recommendations beyond collaborative filtering
- Entity resolution across disparate data sources
- Knowledge graph construction and querying
- Scales to billions of nodes and edges
Tell us what data you
have.
We'll tell you what's possible.
Custom ML always starts with data. Here's how we map common data types to the AI solutions that unlock them.
Fraud detection, churn prediction, CLV modeling, purchase propensity, anomaly detection.
Recommendation engines, churn scoring, personalization, conversion optimization.
Predictive maintenance, anomaly detection, equipment failure prediction, quality control.
Fraud ring detection, social recommendations, knowledge graphs, entity resolution.
AI that fits
your industry's
reality
Fraud Detection
Real-time transaction scoring with <50ms latency and 99%+ precision across card, wire, and account takeover fraud.
Credit Risk Modeling
Explainable credit scoring models that improve approval rates while reducing default risk for lenders.
Algorithmic Trading Signals
ML-powered market signal generation and portfolio optimization beyond rule-based strategies.
Personalized Recommendations
Real-time product and content recommendations that increase basket size and session depth.
Dynamic Pricing
RL-based pricing engines that respond to demand, competition, and inventory signals automatically.
Churn & Retention AI
Customer-level churn risk scoring with automated CRM triggers for retention campaigns.
Clinical Risk Stratification
ML models that predict patient deterioration, readmission, or disease progression from EHR data.
Diagnostic AI Support
Imaging and NLP models that assist clinicians with diagnosis and documentation — not replace them.
Drug Discovery Acceleration
Graph ML and molecular property prediction to accelerate compound screening and prioritization.
Route Optimization
RL agents that optimize last-mile delivery routing dynamically, adapting to traffic and constraints.
Predictive Maintenance
Sensor data models that forecast equipment failures before they cause downtime and supply chain disruption.
Demand Forecasting
SKU-level forecasting that reduces overstock by 25–35% while maintaining target service levels.
Content Recommendations
Session-aware recommendation systems that maximize engagement for streaming and publishing platforms.
Audience Segmentation
Unsupervised clustering and lookalike modeling for hyper-targeted content and ad campaigns.
Content Moderation AI
Multimodal classifiers that detect harmful, abusive, or policy-violating content at scale.
From problem to
production
model
We run sprint-based delivery with stakeholder demos at each stage — so you always know what's being built and why.
Problem Framing & Data Audit
We define the ML problem precisely, audit your data for quality and coverage, and validate feasibility before writing a line of code.
Baseline & Experiment Design
We establish a performance baseline, define success metrics, and run structured experiments to find the best model architecture for your data.
Model Development & Validation
Iterative development with rigorous offline and online evaluation — including bias audits, explainability analysis, and edge case testing.
Production Deployment & Handoff
We deploy to your infrastructure with monitoring, drift detection, and automated retraining hooks — then hand off full ownership and documentation to your team.
Your problem is specific. Your solution should be too.
Bring us your data, your use case, and your goals. We'll tell you exactly what's possible — and build it.
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