AI Native Transformation
Shift from isolated point tools to a secure, self-optimizing digital workforce. nClouds pairs high-touch executive strategy with single-tenant AWS architecture—delivering live code, unified data fabric, and governed token spend in 30 days.
75%
Inference Cost Cut With Smart Token Routing
4x
Workflow Conversion Lift via agentic semantic search
30
Days Time to Production Code Live code in your cloud
100%
Single-Tenant Perimeter Your data never leaves your premises
Most corporate AI initiatives stall because tools are dropped into legacy operating models. We solve the 20% (Data Fabric) and 70% (People & Process) so your 10% (AI Logic) delivers measurable EBITDA expansion.
Codifying your business logic into role-based Digital Workers onboarded like employees—complete with job descriptions, named human supervisors, and audit trails.
Connecting scattered CRM, ERP, and document silos via open-standard Model Context Protocols (MCPs) without moving your underlying data.
Protecting your P&L with automated prompt caching and dynamic model routing.
We operate as an extension of your engineering team, transferring full knowledge and Jira-ready backlogs so you own 100% of your IP.
A technical and business diagnostic where a 3-person Transformation Pod embeds in your trenches to assess schemas, test live models, and deliver production-ready code inside your secure cloud perimeter in 30 days.
Key Deliverables:
Digital Workforce Map, Data Fabric Schema, 3-Year EBITDA Model, and Jira-ready backlog.
Deploy autonomous workflow agents that handle end-to-end operational tasks along a 4-step Trust Ladder (Observe → Draft → Execute → Own).
Key Deliverables:
Deployed single-tenant directly inside your perimeter on your technology stack.
Architect secure, high-accuracy GenAI solutions or natural language search, automated document extraction, and complex summarization using standard Model Context Protocols (MCPs) and agentic vector search without training underlying data.
Key Deliverables:
Single-tenant cloud boundary ensuring your proprietary data is never used to train external vendor models.
Eliminate API cost sprawl with built-in token economics. Enforce dynamic model routing, prompt caching, and context trimming so spend stays governed as usage grows.
Key Deliverables:
Cut LLM inference costs by up to 76% with transparent, itemized run receipts.
