Our Services

AI Business Strategy

We identify opportunities and define your AI road map based on value generated and your risk tolerance.

  • Lean on our industry leading technology strategy expertise.
  • Benefit from our ValueLeaks™ process to define your roadmap using value and risk as key drivers.
  • Separate hype from reality using our deep AI/ML expertise.
  • We analyze your specific business opportunities in the context of your industry trends using mature technology strategy frameworks to develop your AI roadmap.

Risk Free Solution Build:​

We assume 100% of the techical risk in our projects, you pay for results not effort.

  • Custom AI Model Development – Build and fine-tune deep learning models using a wide range of architectures. Have us invent an architecture for you if you have an interesting problem.
  • Retrieval-Augmented Generation (RAG) – Enhance LLM performance with real-time, context-aware retrieval using vector search for improved accuracy and relevance.
  • Vector Databases – Implement scalable vector search with FAISS, Pinecone, Weaviate, or ChromaDB for high-speed similarity search and embedding storage.
  • Graph Databases for AI – Integrate knowledge graphs (Neo4j, ArangoDB, TigerGraph) with AI models for enhanced reasoning, entity linking, and contextual understanding.
  • LLM Fine-Tuning & Optimization – Optimize large language models with LoRA, QLoRA, and RLHF techniques for domain-specific performance improvements.
  • Hybrid AI Architectures – Combine symbolic AI, knowledge graphs, and deep learning to create explainable and robust AI systems.
  • AI-Powered Search & Retrieval – Build advanced semantic search pipelines leveraging embeddings, hybrid search (BM25 + dense retrieval), and knowledge-enhanced LLMs.
  • Multi-Modal AI Models – Develop and integrate AI models that combine text, image, and structured data processing for complex AI applications.

Cost effective solution management

Our Cloud, MLOps, MLDevSecOps and Governance expertise makes managing your AI infrastructure enterprise quality and cost efficient.​

  • Model Lifecycle Automation – Streamline model development, training, deployment, and monitoring.
  • Cross-Team Collaboration – Integrate ML engineers, DevOps, and data scientists in unified workflows.
  • CI/CD for ML – Automate model versioning, testing, and continuous integration/delivery.
  • Data & Feature Engineering Pipelines – Automate data preprocessing, feature selection, and transformation.
  • Model Monitoring & Observability – Track model drift, performance metrics, and real-time inference logs.
  • Model Retraining & AutoML – Enable periodic retraining with automated data updates and hyperparameter tuning.
  • Adversarial ML Defense – Detect and mitigate data poisoning, model evasion, and adversarial attacks.
  • Bias & Explainability (XAI) – Ensure fairness, interpretability, and ethical AI compliance.
  • Compliance & Governance – Enforce GDPR, HIPAA, and SOC2 compliance with audit-ready ML workflows.

Continuous Evolution and Value Delivery

Our Data platform engineering and data science expertise keeps you AI infrastructure continuously evolving and delivering new value.​

  • Automated Model Updates – Implement retraining pipelines to keep models accurate with fresh data.
  • Incremental Model Improvements – Deploy A/B testing, shadow deployments, and canary releases for continuous enhancement.
  • Real-Time Feedback Loops – Use user interactions and model performance metrics to drive adaptive learning.
  • Feature Store & Reusability – Centralize and standardize features for consistent ML model performance across use cases.
  • ML Monitoring & Performance Tracking – Continuously track accuracy, drift, and inference latency to maintain reliability.
  • Business Impact Metrics – Align ML model KPIs with business goals for measurable value generation.