ScriptSense

The problem

Entertainment organizations hold enormous volumes of valuable intellectual property, but much of the intelligence inside those assets remains trapped in unstructured text. Workflows such as script coverage, draft comparison, production breakdowns, catalog research, and content discovery are slow, specialized, and difficult to scale.

The opportunity was not to replace creative judgment. It was to give development and production teams a faster, more consistent way to surface evidence, structure information, compare material, and work across large content libraries without abandoning the processes they already trusted.

My role

I led engineering and served as the platform’s primary architect from early product development through production scale. I owned technical direction across the application, AI pipelines, retrieval systems, workflow orchestration, infrastructure, model strategy, and engineering delivery.

My role included deciding what should remain deterministic, where models created real leverage, how specialized workflows should coordinate, which providers were appropriate for each task, and where human review had to remain part of the product.

I also led the practical decisions required to turn an ambitious AI concept into a production platform: sequencing the roadmap, controlling inference cost, managing latency, evaluating model behavior, building recoverable long-running jobs, and ensuring the architecture could expand beyond its original use case.

System architecture

Content ingestion and normalization

Scripts, books, and related content entered through ingestion workflows, where documents were parsed and normalized into a representation that supported both deterministic processing and model-assisted analysis. Long-form documents required contextual chunking that preserved structure.

Intelligence and retrieval

Structured data supported repeatable downstream workflows, while vector retrieval — using metadata and document structure — surfaced relevant passages, so entire documents did not need to be loaded into every model call.

Workflow orchestration

Distinct workflows handled different tasks. Long-running jobs required state, retries, progress tracking, and recoverability, and the system coordinated retrieval, model calls, validation, and persistence.

Model abstraction

The system was not tied to one model provider; models could be selected by quality, context, latency, and cost, and stronger models could be adopted without rebuilding the product.

Application and tenancy

Outputs were persisted and revisitable. Users needed projects, permissions, and shared workflows, and the system was designed for organization-level use.

The AI pipeline

ScriptSense did not treat every analysis task as the same prompt applied to a different document. Each workflow had different output requirements, context needs, evaluation criteria, and tolerance for ambiguity.

Script coverage required a coherent editorial assessment. Draft comparison required alignment across versions and evidence tied to specific changes. Production breakdowns required structured extraction that could support downstream planning. Content discovery drew on vector retrieval across the analyzed library, while conversational access grounded its answers in the structured analysis the platform had already produced.

The orchestration layer coordinated specialized model calls, deterministic processing, retrieval, validation, and persistence, so each workflow could be optimized independently while still sharing a common platform foundation. The important achievement was not merely running many model calls — it was designing the system around the distinct requirements of each workflow.

Production considerations

Getting the system into production required more than making model calls succeed. Reliability, evaluation, evidence, cost, latency, recoverability, and human review all had to become first‑class architecture concerns.

Reliability

Long‑running jobs needed persistent intermediate state, retry and recovery behavior, and safeguards against silent downstream corruption.

Evaluation

Completion did not equal quality. Outputs had to be assessed for completeness, evidence, consistency, and usefulness — and each workflow needed its own evaluation criteria.

Cost and latency

Long documents and multi‑stage workflows were expensive. Models were selected by task, lower‑cost models were used where quality held up, and parallelism was controlled.

Human review

The system accelerated experts rather than replacing them. Outputs stayed inspectable and editable, and evidence and structured data supported validation.

Model independence

Provider abstraction reduced vendor dependency; routing could weigh quality, cost, latency, and context limits, and the platform could adopt stronger models over time.

Key decisions

Model‑agnostic architecture

The platform could route work based on quality, context, latency, and cost rather than tying the product to a single model provider.

Workflow‑specific systems

Script coverage, draft comparison, production breakdowns, and content discovery required different context, validation, and output structures. A single universal agent would have hidden those differences rather than handling them well.

Structured intelligence alongside narrative output

ScriptSense preserved queryable data in addition to generated prose, so analysis could support later workflows, search, comparison, and product features.

Asynchronous processing for long‑form analysis

Large documents and multi‑stage workflows required persistent job execution, progress tracking, retries, intermediate state, and recoverability.

Human review as part of the product

The system accelerated expert judgment rather than attempting to replace it. Outputs needed to be inspectable, editable, and tied back to supporting evidence.