Enterprise AI
From AI to SI on enterprise-ready foundations.
Turn promising AI use cases into systems your organization can secure, evaluate and operate. We design across AWS, Microsoft Azure, Google Cloud and hybrid environments, connecting models, enterprise data and applications with clear controls for access, quality, cost and ownership.
Enterprise challenges we address
- Model choices and AI runtime environments lack clear ownership, repeatable deployment or lifecycle controls.
- Data, knowledge sources and RAG retrieval do not preserve permissions or provide dependable grounding.
- Agents, orchestration and MCP tools can exceed intended permissions or perform actions without clear approval.
- Evaluation, observability and LLMOps do not surface quality changes, failures or usage patterns in time.
- Security, governance and AI FinOps controls are unclear, leaving risk, accountability and costs difficult to manage.
Capabilities
- Model and AI runtime patterns for selecting, accessing, deploying and managing provider or hosted models.
- Data, knowledge and RAG pipelines with access-aware retrieval and governed index lifecycle.
- Agents, orchestration and MCP integrations with bounded tools and explicit approval paths.
- Evaluation, observability and LLMOps for quality, safety, performance, release and incident feedback.
- Security, governance and AI FinOps controls for identity, data handling, oversight and cost ownership.
Architecture concerns
- Set model access, hosting, environment and lifecycle boundaries for the selected runtime.
- Define data sources, permissions, ingestion, retrieval, grounding, deletion and knowledge refresh.
- Scope agent tools and orchestration with least privilege, validation, human approval and audit trails.
- Version models, prompts and evaluation sets; connect test results to release gates and rollback decisions.
- Collect traces, quality signals, failures and usage while protecting sensitive prompts and responses.
- Set governance for security, privacy, retention, human oversight and cost allocation across AI workloads.
Typical deliverables
- Model and AI runtime architecture, access patterns, deployment definitions and lifecycle guidance.
- Data and knowledge flow diagrams, source permissions, RAG patterns and index-management procedures.
- Agent and orchestration reference patterns with MCP or other tools, scoped access and approval controls.
- Evaluation datasets and release gates, LLMOps dashboards, incident guidance and model-change procedures.
- Security, governance and AI FinOps recommendations for oversight, privacy, operations and cost ownership.