Our Technical Specializations

AI & Machine Learning

We design, train, and deploy production-grade machine learning systems — from fine-tuned large language models to custom classifiers, recommendation engines, and computer vision pipelines. Our ML engineers have shipped models serving millions of users across a range of industries.

• ✅ Large language model fine-tuning (OpenAI, Anthropic, open-source)
• ✅ Retrieval-Augmented Generation (RAG) architecture design and build
• ✅ Custom NLP pipelines — classification, NER, sentiment, summarisation
• ✅ Computer vision models for defect detection, OCR, and image analysis
• ✅ ML model evaluation, monitoring, and continuous retraining pipelines
• ✅ Vector database design and integration (Pinecone, Weaviate, pgvector)

Multi-Provider LLM Router

Stop depending on a single AI provider. We architect intelligent routing layers that automatically direct each request to the best-suited model — balancing cost, latency, capability, and compliance — with seamless fallback if any provider goes down.

• ✅ Route across OpenAI, Anthropic, Google, Mistral, and open-source LLMs
• ✅ Rules-based and ML-based routing by task type, cost, and SLA
• ✅ Automatic failover — zero downtime when a provider has an outage
• ✅ Unified API — one endpoint for all providers, no code changes needed
• ✅ Cost optimisation — route simple tasks to cheaper models automatically
• ✅ Full observability — latency, cost, and quality metrics per provider

MCP Servers

We build and deploy Model Context Protocol (MCP) servers that give your AI agents structured, secure access to your internal tools, databases, and APIs. MCP is the emerging standard for connecting AI models to the real world — and we are early specialists in production MCP implementations.

• ✅ Custom MCP server design and implementation from scratch
• ✅ Tool and resource exposure — databases, APIs, file systems, calendars
• ✅ Secure authentication and authorisation layers for every tool
• ✅ Prompt engineering for MCP-enabled agent workflows
• ✅ MCP server hosting, monitoring, and version management
• ✅ Integration with Claude, GPT-4, and any MCP-compatible agent framework

Cloud & Platform

Deploying AI at scale requires rock-solid cloud infrastructure. We architect, build, and manage cloud platforms on AWS, Azure, and GCP that are optimised specifically for AI workloads — high throughput, low latency, and built to scale automatically under load.

• ✅ AI workload infrastructure design on AWS, Azure, and GCP
• ✅ Kubernetes orchestration for scalable, resilient model serving
• ✅ Serverless AI pipelines with automatic scaling and cost controls
• ✅ Infrastructure as Code using Terraform and Pulumi
• ✅ CI/CD pipelines for model training, evaluation, and deployment
• ✅ GPU cluster setup and management for high-throughput inference
• ✅ Security hardening, VPC design, and compliance (SOC2, ISO 27001)

Data & Analytics

AI is only as good as the data it runs on. We build the data foundations that make AI reliable — from ingestion pipelines and data warehouses to real-time analytics dashboards and business intelligence systems that give you genuine insight into what your AI is doing and why.

• ✅ End-to-end data pipeline design and implementation (ETL/ELT)
• ✅ Data warehouse and lakehouse architecture (Snowflake, BigQuery, Redshift)
• ✅ Real-time streaming pipelines using Apache Kafka and Flink
• ✅ AI agent performance dashboards and analytics
• ✅ Business intelligence and reporting (Metabase, Superset, Looker)
• ✅ Data quality frameworks — validation, lineage, and monitoring
• ✅ Feature stores for ML model training and serving

RAG Pipeline & Document Intelligence

Make your AI agents genuinely knowledgeable about your business. We design and build Retrieval-Augmented Generation (RAG) pipelines that connect LLMs to your own documents, knowledge bases, and internal data — so answers are accurate, grounded, and up to date rather than hallucinated.

Whether you have hundreds of PDFs, a SharePoint full of policies, a product catalogue, or years of support tickets — we turn your unstructured data into an intelligent, searchable knowledge layer your AI can reliably reason over.

• ✅ End-to-end RAG pipeline design, build, and deployment
• ✅ Multi-format document ingestion — PDF, Word, Excel, HTML, Markdown, CSV
• ✅ Intelligent chunking strategies tuned for retrieval accuracy
• ✅ Vector embedding with OpenAI, Cohere, or open-source models
• ✅ Hybrid search — combining semantic (vector) and keyword (BM25) retrieval
• ✅ Re-ranking and context compression for higher-quality answers
• ✅ Citation and source attribution — every answer references its source
• ✅ Incremental document updates — new docs indexed automatically
• ✅ Evaluation pipelines to measure retrieval accuracy and answer quality