Score

AI Factory Readiness Assessment

Evaluation datasets are versioned, documented, and tied to specific tasks

Teams agree on shared evaluation principles, even if individual metrics differ

Regression tests run automatically for the relevant parts of the system

LLM outputs are evaluated with validated judge models or human-in-the-loop for sensitive cases

Agentic workflows include the appropriate level of online evaluation

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Pipelines are explicit, multi-step, and environment-isolate

Notebooks are used for exploration, and production logic is captured in pipeline steps

Each major workflow (RAG ingestion, embedding generation, CV preprocessing, training, evaluation, agent tests) has a pipeline or reusable pipeline nodes once the steps become stable enough to benefit from caching and standardization

Pipelines can run any language or framework (Python, R, bash, HF, CV libs) consistently because execution is containerized or isolated

Teams share pipeline templates that are actively maintained and versioned

Re-running a pipeline with the same inputs yields the same outputs because datasets, code, dependencies, and configs are versioned

Pipelines include observability: logs, metrics, artifacts, and lineage are tracked automatically

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All datasets used for training, validation, and evaluation are versioned or snapshot-managed

Embeddings and vector DB entries have lineage to both the source data and the embedding model version

RAG pipelines have defined freshness policies

Multimodal datasets (images, audio, video, text) track both the raw assets and the derived artifacts

You can answer “Which data produced this output?” without searching Slack, S3 buckets, or notebook history

Models, prompts, embeddings, tools, configs, and agent definitions are versioned in a consistent, queryable system

Every artifact (model, embedding index, dataset, prompt, agent graph, tool schema) links back to its pipeline run or creation event

Promotion and deployment flows include lightweight approvals appropriate to the risk level

Audit logs exist for how key AI decisions were made

Security and privacy controls are enforced inside workflows, not just in documentation

Changes to critical artifacts (models, prompts, agent graphs, RAG indices) trigger automatic re-evaluation

Your organization can answer “Who changed what, when, and why?” across the AI lifecycle

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SMEs build safely using templates, guardrails, and pre-defined data access patterns

Data Scientists focus on improving models and evaluations, not reconstructing pipelines or untangling inconsistent data prep

AI Engineers focus on workflow logic, orchestration, tool integration, and LLM/system behavior, not cleaning up brittle demos

MLEs own reliability, infrastructure, scaling, and productionization, not post-hoc debugging

Platform / Infra teams have visibility into lineage, costs, environment usage, and risk signals

Product teams receive predictable deliverables because the workflow is standardized, not reinvented per feature

Everyone shares a common workflow vocabulary (datasets, versions, metrics, pipelines, evaluations)

You can switch between LLM providers with minimal changes because prompts, configs, and system instructions are abstracted and versioned

Agent frameworks (MCP, LangGraph, custom orchestrators) connect into pipelines through clear interfaces, not hard-coded integrations

Observability tools integrate at the workflow level — capturing traces, lineage, inputs/outputs, latency, cost, and step-level metadata

Evaluation frameworks (DeepEval, custom judge models, offline metrics) are interchangeable because evaluation is pipeline-driven, not library-driven

CV, classical ML, LLM, RAG, and multimodal workflows run on the same execution backbone even if their frameworks differ

You can add new model families, tools, or frameworks without redesigning your entire pipeline architecture