Enterprise AI Factory: Build and run generative AI at scale.
DS Stream AI Factory is a proprietary operating model for scaling enterprise GenAI delivery. We combine a production-grade AI platform reference architecture, governance, reusable architecture components, and an ROI-tracked use case pipeline to convert business problems into production AI solutions. This approach is 2-3× faster and costs 60% less per use case at scale.
Build a reusable AI engine you can actually control. Start with one use case. Then grow it across your whole company. The infrastructure is production ready. Compliance is built in. You see ROI from day one.
The AI Factory delivers production-grade GenAI infrastructure, governance, and delivery pipeline that transforms isolated experiments into enterprise-wide industrial scale with measurable financial outcomes.
Why Do Most Enterprise AI Programs Stall Between Pilot and Production?
Most organizations have run AI pilots, but fewer than 30% make it to production. The problem is usually not the model. It's a lack of shared tools, proper governance, repeatable processes, and a way to track value. Money gets spent on AI with no clear return and no way to grow.
Enterprise-Grade GenAI Delivery Capabilities
AI delivery on AWS, GCP, or Azure. Multi-cloud architecture from day one.
No vendor lock-in. Infrastructure works across all major cloud providers.
AI governance and compliance are integrated into every delivery phase, not retrofitted after deployment
ISO/IEC 42001-aligned AI Management System ensures regulatory readiness
Full pipelines with drift detection, evaluation tools, and AIOps monitoring for production models.
CI/CD for AI, experiment tracking, model registry, and prompt versioning.
First production use cases go live within 8 weeks. ROI baseline is captured before any build work starts.
Every use case has a financial value hypothesis from the moment it's submitted.
Reusable pattern library and AI Centre of Excellence enable independent AI delivery
Knowledge transfer built into every phase, not deferred to the end
How We Build Your AI Factory: From Discovery to Scale
Phase 1 runs weeks 1 through 8 on a fixed fee. The governance framework goes live in the first week. A multi-cloud sandbox is set up within 2 weeks. The AI Platform Blueprint is designed for your specific architecture. The first two production use cases are scoped, profiled, built, and deployed. The LLMOps and AIOps pipeline is running with evaluation tools and drift detection. ROI baseline is captured before building starts. Output includes UC1 and UC2 in production with governance signed off.
Phase 2 runs weeks 9-20 on a T&M with Cap basis. Use cases 3 and 4 are delivered to production using patterns extracted from Phase 1. Reusable pattern library is created from Phase 1 components. AI CoE structure is established with defined roles and operating model. Knowledge transfer to client AI team begins with hands-on workshops. Executive portfolio dashboard goes live with real-time AI spend and ROI tracking. Stage-gate ROI review is completed. Output includes 4 use cases in production, AIOps active, and CoE established.
Phase 3 runs weeks 21-36+ on an options-based model. Scale to 5-10+ use cases via proven pattern library and reusable platform components. AI literacy programme is delivered for technical and business teams. CoE becomes self-sufficient in AI delivery. DS Stream transitions to advisory and support role with ongoing guidance. Annual ISO 42001 review is conducted to maintain compliance. Output includes 5-10+ use cases in production, CoE operating independently, and value realization dashboard live.
Ongoing AIOps monitoring ensures production model performance remains optimal. Drift detection and automated alerts keep models aligned with business outcomes. Stage-gate reviews govern scale, pivot, iterate, or stop decisions for every use case. Pattern library grows with each new use case delivery, continuously accelerating delivery velocity and reducing cost per use case.
An industrialized AI delivery model makes it easier to build and manage AI systems at scale. It uses repeatable processes and standard tools, so teams can focus on solving problems instead of reinventing the wheel each time. This approach cuts down on errors, speeds up delivery, and helps businesses use AI more consistently across projects.
2-3× faster use case delivery using reusable building blocks, ready-made LLMOps workflows, and a tried-and-true delivery playbook.
60% lower cost per use case at scale as the pattern library and shared infrastructure reduce build-from-scratch effort on every new use case
100% governance coverage by design. Every use case is governed from intake, not patched in after deployment.
First production use cases live within 8 weeks. You get value while governance and platform are built in parallel.
AI CoE self-sufficient by end of Phase 3. Knowledge transfer happens in every phase, not just at the end.
Full ROI traceability per use case , You can track revenue impact, cost reduction, or risk mitigation against a pre-agreed financial hypothesis.
Who This Technical Service Is For
Use Cases
The AI Factory capability can be applied across industries to solve specific operational and business challenges with production-grade GenAI delivery.
Most Common Questions
An AI Factory is an operating model with five integrated parts that make AI delivery faster and more reliable over time. Each new use case built on this model gets cheaper, quicker, and easier to manage than the one before.
Unlike a pilot, which is a one-time test, an AI Factory is a repeatable system. It includes five connected pieces: a platform blueprint, governance rules, a delivery pipeline, team structure, and a way to track value. Every new project that runs on the factory platform costs less, takes less time, and has better oversight than the last one. That is the factory effect at enterprise scale.
The first two use cases are usually in production within 8 weeks. A multi-cloud sandbox goes live in 2 weeks. Governance and platform work happen at the same time as delivery, so you get value while the foundation is being built. This keeps things moving without delays between platform setup and use case rollout.
AWS, GCP, Azure, and Databricks with the same governance controls and LLMOps tooling across all clouds.
The AI Factory supports AWS with Bedrock and SageMaker, GCP with Vertex AI, Gemini, and BigQuery ML, Azure with Azure OpenAI and Purview, and Databricks. The same governance controls and LLMOps tooling apply across all clouds. Migration paths are designed in from day one for clients with pending approvals or multi-cloud strategies.
ISO/IEC 42001 AIMS is implemented from the start. It's not added later. Every use case is risk-tiered before development begins.
For multi-jurisdiction deployments, per-country compliance layers like DPIA, data localization, and regulator registration are built into the cloud architecture from day one. Governance is embedded, not bolted on.
Yes, knowledge transfer is non-negotiable. The AI CoE is self-sufficient by the end of Phase 3, with delivery playbooks, AIOps and LLMOps runbooks, and an AI literacy programme. DS Stream then acts as an advisor, giving ongoing guidance while your internal team works on its own.
Every use case starts with a Financial Value Hypothesis tied to revenue, cost, or risk impact with real-time executive dashboard tracking.
Every use case starts with a Financial Value Hypothesis tied to revenue, cost, or risk impact. KPI baselines are captured pre-deployment. An executive dashboard tracks AI spend and ROI in real time, with stage-gate reviews governing scale, pivot, iterate, or stop decisions. This ensures every AI investment is financially accountable from day one.
Ready to make AI work across your whole company?
Whether you are moving from isolated AI pilots to a production program with clear rules, or scaling an existing platform across your company, DS Stream can help define the fastest path from architecture to production. Governance, ROI tracking, and knowledge transfer are built in from the start.
AI Maturity Assessment
A no-obligation working session focused on your current AI maturity, cloud architecture, use case priorities, and governance posture. We assess your readiness for industrial-scale AI delivery and identify the fastest path to production.
AI Platform Blueprint
Design your AI Factory platform reference architecture with multi-cloud support, governance framework, and delivery pipeline. This blueprint becomes the foundation for all subsequent use case delivery.
Use Case Pipeline
Scope, profile, build, and deploy your first two production use cases with pre-defined financial value hypotheses, KPI baselines, and stage-gate decision framework.
AI CoE Setup
Establish your AI Centre of Excellence with defined roles, operating model, delivery playbooks, and knowledge transfer programme to enable independent AI delivery.
Value Realization Dashboard
Executive dashboard tracking AI spend and ROI in real time across all use cases, with stage-gate reviews governing capital reallocation decisions and portfolio optimization.