We build end-to-end multimodal AI platforms from zero. RAG and vector search, agentic workflows on LangChain and LangGraph, video and audio intelligence pipelines, document extraction. Anchored in our production work for SponsorUnited.
Multimodal AI platforms are where most enterprise GenAI projects either succeed or quietly die. The technology works in demos. It breaks at scale, where you have video that needs to be processed at TB-per-day, audio that needs entity extraction with high precision, documents that need RAG with citations, and all of it needs to be queryable, monitored, and continuously evaluated.
The hard part isn't picking a model. It's the pipeline behind it: ingestion, normalization, vector indexing, agentic orchestration, validation, monitoring, iteration. Most teams underestimate this and end up with brittle systems that work for the launch demo and break the week after.
We design and build production multimodal AI platforms end-to-end. Our reference engagement: SponsorUnited's multimodal AI platform — built from scratch across video, audio, and document intelligence, reducing manual review by 90%+ in production.
End-to-end data architecture across Redshift, S3, Airbyte, NiFi, Kafka, CDC, and ETL/ELT workflows. Multimodal ingestion pipelines that survive scale and schema drift.
Production RAG pipelines using vector search and semantic retrieval. Modular AI workflows with LangChain and LangGraph. Tool use, agent orchestration, and the operational scaffolding that makes agentic systems actually reliable.
Video intelligence using computer vision combined with LLM validation — reducing manual review by 90%+ in production. Document intelligence including transcript entity extraction and enrichment. Audio intelligence with speaker diarization and content extraction.
End-to-end AI lifecycle: ingestion, orchestration, inference, monitoring, evaluation, iterative improvement. We don't ship-and-leave. We operate the platform with you until it's stable, and then continue if you want.
The architecture pattern we deploy for multimodal AI platforms — refined across SponsorUnited and other production engagements. Each stage modular, monitored, and replaceable as models and tools evolve.
Where Claude fits: long-context document processing, multimodal validation, agentic orchestration with tool use, and the reasoning steps that previously required brittle rule-based logic.
// pattern
01 · Multimodal ingestion — Video, audio, document streams. Kafka, NiFi, Airbyte. Schema-aware. Resilient to source variability.
02 · Indexing & embeddings — Vector search, semantic retrieval. Hybrid lexical/semantic ranking. Continuously updated as content changes.
03 · Agentic orchestration — LangChain, LangGraph. Tool use, planning, evaluation. Modular workflows that compose rather than monolithic chains.
04 · Reasoning & validation — Claude validates CV outputs, reasons over long-context documents, generates structured outputs. The reasoning layer that makes the rest reliable.
05 · Monitoring & iteration — Continuous evaluation. Drift detection. Human review loops. The unsexy infrastructure that determines whether the platform survives year two.
This is the engagement we've shipped most. We can talk through the architecture decisions that matter, the ones that don't, and where we'd recommend Claude versus alternatives based on your specific workload.