End-to-end AI systems engineering
From initial prototyping to production deployment and ongoing optimization, we provide comprehensive services to build, scale, and maintain high-performance AI infrastructure.
Core Services
Production-grade solutions for every stage of your AI journey.
AI Systems & Model Engineering
- Custom LLM fine-tuning & instruction tuning
- Agentic AI & multi-agent orchestration
- Multi-modal end-to-end pipeline development inclucing, Computer Vision, Natural Language Processing, Audio processing and Objective specified Model development.
- Custom model architectures & research
- Transfer learning & domain adaptation
- Model evaluation & benchmarking
Deployment & Inference
- Triton Inference Server deployment & optimization
- vLLM deployment for LLM inference
- FastAPI & scalable API gateways
- Dynamic batching, request optimization and continous batching
- Model quantization (INT8, FP16, GPTQ)
- GPU pooling & resource management
- Load balancing & autoscaling
- A/B testing infrastructure
Big Data & Distributed Compute
- PySpark & distributed ETL pipelines
- Kubernetes & container orchestration
- Data pipelines for billion-row datasets
- Real-time streaming (Kafka, Flink)
- Data lake & warehouse architecture
- Distributed training infrastructure
- GPU cluster management
- Cloud & on-premise hybrid solutions
Specialized Services
Advanced capabilities for complex AI challenges.
Vector Databases & Search
- High-performance vector DB implementations
- Disk-based indices for billion-scale vectors
- Hybrid search (vector + keyword)
- Real-time indexing & updates
- Multi-modal embedding search
- Semantic search optimization
Model Optimization
- Quantization & pruning techniques
- TensorRT & ONNX optimization
- Knowledge distillation
- Low-latency inference optimization
- Memory footprint reduction
- Throughput maximization
MLOps & Infrastructure
- CI/CD pipelines for ML models
- Experiment tracking & versioning
- Model registry & governance
- Monitoring & observability
- Automated retraining pipelines
- Feature stores & data versioning
RAG & Knowledge Systems
- Retrieval-augmented generation systems
- Document processing pipelines
- Chunking & embedding strategies
- Context window optimization
- Multi-source knowledge integration
- Query optimization & caching
Performance Engineering
- Latency optimization (P50, P95, P99)
- Throughput & capacity planning
- Cost-performance optimization
- Profiling & bottleneck analysis
- Stress testing & load simulation
- Failure recovery & resilience
Consulting
- Architecture review & recommendations
- Technical audits & assessments
- Best practices documentation
- Technology stack evaluation
- Strategic AI roadmap planning
Technology Stack
We work with industry-leading tools and frameworks to build scalable, production-ready AI systems.
ML Frameworks
Deployment
Infrastructure
Data Processing
Vector Databases
MLOps
How We Work
Flexible engagement models to match your needs.
Project-Based
Fixed-scope projects with defined deliverables and timelines. Ideal for specific implementations or migrations.
- Clear scope & deliverables
- Fixed timeline & budget
- Complete documentation
- Knowledge transfer included
Retainer
Ongoing partnership for continuous development, optimization, and support. Perfect for evolving AI systems.
- Dedicated team allocation
- Flexible scope adjustments
- Priority support & response
- Monthly optimization reviews
Consulting
Expert guidance for architecture, strategy, and technical decisions. Great for planning and audits.
- Architecture review & design
- Technology recommendations
- Performance audits
- Team training & workshops
Ready to Get Started?
Let's discuss your AI infrastructure needs and create a solution that works for you.