AI services built for enterprise scale

We design and deploy autonomous multi-agent AI systems that eliminate manual processes entirely. Our agentic workflows use large language models (LLMs) to orchestrate complex, multi-step business processes — from data ingestion and analysis through to decision-making and execution — without human intervention. Clients typically see 10x productivity gains within the first 90 days.

Capabilities

• Multi-agent orchestration with LangChain, CrewAI, and custom frameworks
• LLM-powered decision engines (OpenAI, Anthropic, Google Gemini)
• Automated task pipelines with real-time monitoring and alerting
• Integration with existing ERP, CRM, and data warehouse systems
• Human-in-the-loop escalation paths for high-stakes decisions
• Full audit trails and explainability for regulated industries

We build enterprise-grade AI chatbots that go far beyond scripted FAQ responses. Using Retrieval-Augmented Generation (RAG), our chatbots are grounded in your proprietary knowledge base, integrated with your live systems, and capable of multi-turn reasoning across complex queries. Deployed across web, mobile, Slack, Teams, and custom channels.

Capabilities

• RAG-powered knowledge bases built on your internal documentation
• Multi-channel deployment: web widget, Slack, Microsoft Teams, API
• Deep CRM and ERP integration (Salesforce, SAP, ServiceNow)
• Multilingual support and sentiment-aware response tuning
• Analytics dashboard with conversation quality scoring
• Continuous fine-tuning from real user interactions

As GCP specialists, we architect and deploy AI solutions that leverage the full depth of Google Cloud's AI stack. From Vertex AI model training and deployment to BigQuery ML for in-database analytics, we build GCP-native systems that scale with your data and integrate seamlessly with your existing Google Workspace and Cloud infrastructure.

Capabilities

• Vertex AI model training, evaluation, and production deployment
• BigQuery ML for in-database predictive analytics
• Gemini API integration for multimodal AI applications
• Cloud Run and GKE-based AI microservices architecture
• Dataflow and Pub/Sub pipelines for real-time AI inference
• Google Workspace AI automation (Docs, Sheets, Gmail)

We build production-grade AI systems on AWS using the full breadth of Amazon's AI and ML services. Whether you need a custom model trained on SageMaker, a foundation model deployed via Amazon Bedrock, or a serverless AI inference pipeline on Lambda, we architect solutions that are scalable, cost-optimised, and enterprise-ready.

Capabilities

• Amazon SageMaker model training, tuning, and deployment
• Amazon Bedrock for foundation model access (Claude, Llama, Titan)
• AWS Lambda and Step Functions for serverless AI pipelines
• Amazon Kendra and OpenSearch for enterprise search and RAG
• Amazon Lex for conversational AI and voice interfaces
• AWS IAM and VPC security architecture for AI workloads

We deploy AI solutions on Microsoft Azure that integrate natively with your Microsoft 365 and Dynamics 365 ecosystem. Leveraging Azure OpenAI Service, Azure AI Search, and the full suite of Azure Cognitive Services, we build intelligent applications that work within your existing Microsoft security and compliance framework.

Capabilities

• Azure OpenAI Service deployment (GPT-4o, o1, custom fine-tuning)
• Azure AI Search with vector and hybrid search for RAG applications
• Azure Machine Learning for custom model training and MLOps
• Microsoft Copilot Studio for low-code agent building
• Power Automate AI Builder for business process automation
• Azure Active Directory integration and enterprise security

Forward Deployed Engineering is our most impactful engagement model. We place senior AI engineers directly inside your organisation — working alongside your teams, understanding your systems, and shipping production AI in weeks rather than months. This model eliminates the communication overhead of traditional consulting and ensures solutions are built for your specific context, not a generic template.

Capabilities

• Senior AI engineers embedded on-site or remotely within your team
• Direct access to your codebase, data, and subject-matter experts
• Rapid prototyping to production in 4–8 week sprints
• Knowledge transfer and internal capability building
• Ongoing support and iteration post-deployment
• Flexible engagement: 3-month minimum, ongoing retainer available

Most enterprises operate across multiple cloud providers. We design AI architectures that are portable, resilient, and cost-optimised across GCP, AWS, and Azure — avoiding vendor lock-in while maximising the best-in-class capabilities of each platform. Our multi-cloud strategy engagements include a full AI readiness assessment, cloud cost modelling, and a phased implementation roadmap.

Capabilities

• Multi-cloud AI architecture design and documentation
• AI readiness assessment across your existing cloud estate
• Cloud cost modelling and optimisation for AI workloads
• Vendor-agnostic model serving with Kubernetes and Terraform
• Cross-cloud data pipelines and feature stores
• Governance, compliance, and security across all three clouds