Our Services - Strategic, Riskless, Value Delivery
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.
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.
Our Cloud, MLOps, MLDevSecOps and Governance expertise makes 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.
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.
