Machine learning and AI
We develop AI-powered applications and ML pipelines using state-of-the-art models, frameworks, and MLOps best practices to deliver intelligent software solutions.
Technical realities in AI
95% of enterprise generative AI pilots fail to deliver a measurable business impact, primarily due to the accumulation of technical debt, inadequate infrastructure design, and insufficient operational frameworks. The transition from proof-of-concept to production-ready systems represents the most critical technical challenge facing CTOs today.
Only 20% of organizations successfully scale AI across multiple departments.
Critical Technical Challenges
Machine learning systems accumulate technical debt through complex data dependencies, entanglement, and configuration complexity. Unlike traditional software debt, ML technical debt affects system boundaries, making changes exponentially more expensive over time.
- Pipeline jungles: Complex preprocessing pipelines that become unmaintainable.
- Dead experimental codepaths: Unused feature extraction logic increases complexity.
- Glue code: Ad-hoc integration between ML libraries and business logic.
- Configuration debt: Complex parameter management without proper versioning.
- We implement automated technical debt detection.
- Deploy AI-powered refactoring systems that analyze code patterns and optimize performance.
- Enforce modular architecture patterns with clear separation of concerns.
Model drift affects 93% of deployed systems, causing silent failures that degrade business performance without apparent symptoms. Traditional monitoring approaches are insufficient for ML systems due to their stochastic nature.
- Statistical drift detection: KL-divergence, Energy Distance, Population Stability Index (PSI).
- Performance-based monitoring: real-time accuracy, precision, and recall tracking.
- Feature distribution analysis: automated comparison against training baselines.
- Automated retraining triggers: model refresh based on the severity of drift.
MLOps architecture patterns
We help organizations progress from ad-hoc experiments to reliable, scalable AI by guiding them through a pragmatic MLOps maturity journey—from Minimum Viable ML to Production MLOps and ultimately Enterprise Scale.
- Manual model training in Jupyter notebooks
- Basic model export and deployment
- Single-product, single-model architecture
- Suitable for proof-of-concept validation
- Automated training pipelines
- Model versioning and A/B testing capability
- Performance monitoring and alerting
- Single-product, multi-model support
- Multi-product, multi-model orchestration
- Centralized feature stores and data governance
- Advanced monitoring and compliance frameworks
- Cross-team collaboration and resource sharing
Feature stores address the fundamental challenge of feature consistency between training and serving environments, eliminating training-serving skew that affects 78% of production ML systems.
- Feature registry: Centralized metadata and lineage tracking.
- Offline store: Historical feature values for training.
- Online store: Low-latency feature serving.
- Computation engine: Transform raw data into features.
Effective model versioning requires handling significant binary artifacts (models), complex dependencies, and reproducible training environments.
For our clients, we usually deploy the following:
- Git-based code versioning for training scripts and configurations.
- DVC/MLflow for model artifacts versioning and metadata tracking.
- Docker containerization for environment consistency.
- Automated testing pipelines for model validation.
In ML/AI, we frequently rely on
Our MLOps and AI application delivery pipelines are built on top of a modern Kubernetes foundation. Kubernetes provides the scalability, resilience, and modularity required to transition from proof-of-concept to enterprise-scale AI systems.
Seldon
Kuberflow
Nvidia Operator
AI services we offer
- What we do: Use-case discovery, ROI modeling, risk & compliance baseline (GDPR, SOC2), data/infra gap assessment.
- Deliverables: 6–12-month roadmap, value cases, governance guardrails, TCO & FinOps plan.
Data quality profiling, lineage, feature standardization, schema governance, and synthetic data where appropriate.
Feature contracts, data SLAs, catalog/registry, training/serving schemas to prevent skew.
Classical ML & deep learning, multimodal perception, predictive analytics, legal/contract AI.
Reproducible notebooks/pipelines, baselines + lift, inference services.
Registry, offline/online stores, transformations, low-latency serving.
Reference implementation, SLAs for freshness/latency, governance policies.
CI/CD for models, artifact/versioning (DVC/MLflow), A/B & canary, drift detection, observability.
GitOps repositories, model registry, monitoring dashboards, and auto-retrain triggers.
On-device/near-edge inference, micro-K8s at the edge, device identity & OAuth, self-healing meshes.
Edge deployment blueprints, OTA update system, secure device onboarding.
Exactly-once stateful stream processing, online inference, IoT telemetry at 10ms-class latency.
Stateful pipelines with incremental checkpoints, autoscaling, and back-pressure controls.
Let’s discuss how our Kubernetes-first MLOps architectures and AI development services can accelerate your transition from experimental pilots to scalable, production-grade AI systems.
