AI Development Services: Custom Models to Production

Pre-built AI APIs (like OpenAI, Google Vision) offer quick time-to-market for common use cases but lack customization for your specific data and business logic. Custom AI development involves training models on your proprietary data, optimizing for your unique requirements, and maintaining full control over the solution. We recommend custom AI when: you have domain-specific data that provides competitive advantage, you need fine-grained control over model behavior, you require on-premise or private deployment, or when pre-built solutions don't meet accuracy requirements. For simpler use cases or MVPs, we often start with pre-built APIs and evolve to custom models as needs grow.

Timelines vary significantly based on complexity: Simple POC (proof of concept) takes 4-8 weeks with existing clean data and straightforward problem. Production MVP takes 3-4 months including data preparation, model development, and basic deployment. Enterprise-grade solution takes 6-12 months with MLOps, monitoring, and full integration. Complex AI systems (multi-model, real-time) can take 12-18+ months. Key factors affecting timeline: data availability and quality, problem complexity, integration requirements, performance targets, and compliance needs. We use agile methodology with 2-week sprints, providing regular model checkpoints and iterative improvements.

Data requirements depend on your use case: For supervised learning, you need labeled examples (inputs with correct outputs). For unsupervised learning, unlabeled data is sufficient. Typical requirements: minimum 1,000-10,000 examples for simple models, 100,000+ for deep learning, balanced representation of all classes/scenarios, historical data for time series, and representative of production conditions. Data quality matters more than quantity: accurate labels, minimal missing values, diverse examples, and recent/relevant data. We conduct data audits to assess readiness, help with data collection strategies, provide labeling tools and services, and can use techniques like synthetic data generation, transfer learning, and few-shot learning when data is limited.

We implement comprehensive validation strategies: Train/validation/test split to prevent overfitting, cross-validation for robust evaluation, multiple metrics (accuracy, precision, recall, F1, AUC), business-relevant metrics beyond ML metrics, and A/B testing before full deployment. Reliability measures include: bias and fairness audits, robustness testing (edge cases, adversarial examples), model uncertainty quantification, shadow mode deployment (running parallel to existing system), canary releases (gradual rollout), and continuous monitoring post-deployment. We also provide model interpretability reports showing what drives predictions, confidence intervals on predictions, and regular retraining schedules to maintain performance.

MLOps (Machine Learning Operations) is a set of practices for deploying and maintaining ML models in production reliably and efficiently. It's the ML equivalent of DevOps. Key components: Version control for models, data, and code; automated testing and validation; CI/CD pipelines for model deployment; monitoring and alerting for model performance; automated retraining pipelines; and infrastructure as code. Benefits include: faster time to production (weeks vs months), reliable deployments with rollback capability, early detection of model degradation, reproducibility and auditability, easier collaboration across teams, and reduced operational overhead. Without MLOps, organizations struggle with model deployment, face production incidents, and can't iterate quickly. We implement MLOps from day one to ensure your AI systems are production-ready.

We implement comprehensive monitoring: Real-time metrics (latency, throughput, error rates), model performance metrics (accuracy, precision, recall), business metrics (conversion, revenue impact), data drift detection (input distribution changes), concept drift (relationship between inputs/outputs changes), and custom alerts for anomalies. Retraining strategies: Scheduled retraining (weekly/monthly based on stability), triggered retraining (when performance drops below threshold), continuous learning (incremental updates), and A/B testing of retrained models before full deployment. We use tools like MLflow, Weights & Biases, and custom dashboards. Monitoring includes automated reports, incident response procedures, and rollback capabilities.

Yes, we support multiple deployment options: On-premise deployment (your own servers/data centers) for sensitive data, air-gapped environments, and regulatory requirements. Private cloud (dedicated VPC, private endpoints) for enhanced security. Hybrid deployment (training in cloud, inference on-premise) for optimal cost-performance. Edge deployment (IoT devices, mobile, embedded systems) for low-latency applications. We handle infrastructure setup (GPU servers, Kubernetes clusters), containerization (Docker, Kubernetes), model optimization (quantization, pruning for edge), secure model serving, and offline operation support. For regulated industries (healthcare, finance), we ensure HIPAA, PCI-DSS, SOC 2 compliance with on-premise solutions.

We implement responsible AI practices throughout development: Data analysis for representation bias, label bias, and historical bias. Model testing across demographic groups, edge cases, and protected attributes (with proper de-identification). Fairness metrics including demographic parity, equal opportunity, and equalized odds. Bias mitigation techniques: data rebalancing, fairness constraints during training, and post-processing adjustments. Explainability tools (SHAP, LIME) to understand decision factors. Regular audits and stakeholder review. Documentation of limitations, known biases, and intended use. Human oversight and human-in-the-loop workflows. We follow frameworks like NIST AI Risk Management, EU AI Act guidelines, and industry best practices for responsible AI.

AI development costs vary based on scope: Discovery/POC (4-8 weeks) ranges from $25K-75K for problem validation and feasibility. MVP Development (3-4 months) ranges from $100K-300K for working model with basic deployment. Production System (6-12 months) ranges from $300K-1M+ for enterprise-grade solution with MLOps. Cost factors include: problem complexity, data preparation needs, model sophistication, infrastructure requirements, integration complexity, and compliance/security needs. We offer flexible engagement models: fixed-price for well-defined POCs, time-and-materials for exploratory projects, dedicated team (monthly retainer) for ongoing development, and success-based pricing for some use cases. Infrastructure costs (cloud, GPUs) are separate and depend on training and serving needs.

Yes, we offer comprehensive post-deployment support: Basic Support (business hours, incident response, monthly reviews), Standard Support (24/7 monitoring, proactive optimization, quarterly retraining), Premium Support (dedicated ML engineer, continuous improvement, weekly reviews), and Custom Enterprise (embedded team, strategic innovation, real-time support). Services include: performance monitoring and alerting, model retraining (scheduled or triggered), data pipeline maintenance, infrastructure management, incident response and debugging, feature updates and improvements, and compliance/audit support. Most clients choose Standard or Premium support to ensure models maintain performance and evolve with business needs. We also offer knowledge transfer and training so your team can eventually manage independently.