MCN 2026

In early 2026, LACMA shipped Collections Online — a public search and discovery site for the museum's collection — in roughly 3.5 months from kickoff to launch. The team was small (LACMA's digital team of two) and the work was deeply collaborative across overlapping skills: LACMA's Senior Director of Publishing — portfolio across editorial, design, and web/digital — anchoring the experience perspective and the internal advocacy that kept the work aligned across the organization; an independent backend engineer on the data platform application; the Folder Design team behind the visual design system and reference demos; the Urban Insight team behind the Netlify-hosted Next.js front end — search integration, frontend architecture, performance, and the discovery experience; and LACMA's Director of Digital Strategy & Infrastructure, who architected the overall environment and built the systems around the data platform (image, AI, search, monitoring, admin tooling, data flows), iterated on intent, and held day-to-day validation, troubleshooting, and coordination.

At the center sits a custom data platform — a FastAPI on ECS application backed by PostgreSQL/RDS — that ingests eleven TMS SQL views (about 1.1 million rows per cycle) plus 119,000 AI-enriched records from DynamoDB, applies user-defined transformation rulesets, and publishes through REST, GraphQL, and a bulk OpenSearch index. Two VPCs joined by peering, schema/rules admin UI, 6-hour ingest cadence with admin-triggered refresh.

A Lambda + SQS image processing pipeline moves 68,000 image-bearing objects (and derivatives) into KMS-encrypted S3. An AI enrichment layer — a SageMaker-hosted SigLIP 2 vision model run ephemerally on g5.xlarge, plus a long-running EC2 service for visual descriptions, embeddings, and search-time text encoding — produces the metadata for hybrid vector search and accessibility. An OpenSearch cluster handles faceted keyword search. A pair of admin tools — diagnostic Lookups and editorial Overrides — keep curatorial, engineering, and the data pipeline honest about what is surfacing on the site.

On the frontend, Next.js + Netlify deliver the public experience, CDN-optimized for scale.

- End-to-End Architecture: the AWS/FastAPI stack, frontend delivery, and operations — with cost and performance numbers for each piece.

- The Reality of AI-Assisted Engineering: where Claude and Gemini delivered 10x acceleration, where they introduced subtle costly bugs, and where we reverted to traditional engineering.

- Operationalizing AI: prompt engineering, agent-based workflows, and the scaffolding that makes AI-assisted work repeatable across a hybrid team.

In this session, we break down both the technical architecture and the collection discovery experience behind the build, sharing key architectural decisions, tradeoffs, lessons learned, and our playbook, including:

The goal is to share a working playbook, with the rough edges visible, for any institution considering a similar build.