Home / Services / Multimodal Enterprise AI Platforms
Pillar 03 · Services

Video, audio, documents — one production platform.

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.

From media to useful context

Make every modality
part of the same answer.

01 / Prepare

Prepare

Ingest video, audio, and documents with timestamps and metadata.

02 / Understand

Understand

Extract the relevant speech, visual signals, and document content.

03 / Retrieve

Retrieve

Find the supporting segments for the task at hand.

04 / Evaluate

Evaluate

Review grounding, retrieval quality, latency, and cost.

Explore grounded retrieval ↗
Multimodal AI / At a glance
01SourcesVideo
02EngineeringMultimodal AI
03DeliveryProduction systems in your environment
Source context · Human oversight · Measurable outcomes
Explore solutions ↗How it fits the AI operating layer ↗
→ The problem

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.

What we do

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. See how we approach enterprise RAG platform builds specifically.

1. Data architecture & ingestion

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.

2. RAG & agentic workflows

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.

3. Multimodal intelligence pipelines

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.

4. AI lifecycle ownership

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.

Where Claude fits

Claude is the reasoning backbone for the tasks that make multimodal AI platforms actually reliable in production. Long-context document processing — reading full contracts, reports, or transcripts that smaller models truncate. Multimodal validation — Claude validated CV frame detections at SponsorUnited, which is the mechanism behind the 90%+ manual review reduction. Enterprise RAG — Claude generates grounded, structured outputs from retrieved context across large document corpora, with the citation fidelity that enterprise buyers require. In agentic workflows, Claude is the planning and reasoning model in LangGraph orchestration, handling multi-step tasks that previously required brittle rule-based logic. We are an Anthropic partner.

→ Reference architecture

The pipeline that actually scales.

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.

01 · INGEST Unified intake video · audio · docs Kafka · NiFi · Airbyte 02 · INDEX + EMBED Semantic search vector · hybrid ranking continuously updated 03 · ORCHESTRATION Compose workflows LangChain · LangGraph tool use · planning 04 · REASONING Claude validates long-context · structured multimodal reasoning 05 · MONITORING Production health drift · eval · review human review loops
[ Outcomes ]

Production multimodal, in numbers.

What our reference deployments have delivered.

90%+
Manual review reduction
Achieved at SponsorUnited via CV + LLM validation pipelines for brand presence detection.
end-to-end
AI lifecycle ownership
Ingestion, orchestration, inference, monitoring, iterative improvement — one team, one architecture.
multimodal
Video, audio, documents
One platform, three modalities, production-grade across all of them.
[ FAQ ]

Common questions about multimodal AI platforms.

What does it actually take to reduce manual review by 90%+ using multimodal AI?
The 90%+ reduction at SponsorUnited came from combining computer vision detection with LLM validation — CV found candidate frames, Claude validated them in context. The key was a confidence-stratified review queue: high-confidence results went straight to output, borderline cases went to human review. That stratification layer, not the models themselves, is what drives the reduction number. Getting there took about 6 months from first pipeline to stable production performance.
How do you build a RAG system that's actually reliable in production?
Reliable production RAG requires: chunking strategy matched to your document structure, hybrid lexical/semantic retrieval (not just vector search), metadata filtering to scope retrieval, a reranking layer, and continuous evaluation against a golden dataset. The vector index is the easy part. The hard parts are retrieval quality evaluation, handling schema drift as documents change, and building the human feedback loop that improves recall over time.
LangChain vs LangGraph — which do you use and when?
LangChain for linear pipelines and chains where execution is predictable and branching is minimal. LangGraph for agentic workflows that require state, loops, and conditional branching — multi-step reasoning, tool use sequences, human-in-the-loop checkpoints. Most production agentic systems we build use LangGraph as the orchestration layer with Claude as the reasoning model, because LangGraph gives you the execution graph visibility that debugging agentic flows requires.
How do you handle multimodal data at scale — video, audio, and documents in one platform?
The architecture is modality-specific ingestion feeding a unified metadata and vector layer. Video goes through CV pipelines for frame-level structuring before anything touches an LLM. Audio goes through ASR and speaker diarization. Documents get chunked and embedded with structure preserved. Each modality has its own processing path; they converge at the retrieval and orchestration layer. The key is not trying to build one pipeline for all modalities.
How long does it take to build a production multimodal AI platform from scratch?
Our reference engagement (SponsorUnited) ran 12+ months end-to-end. A scoped platform for one modality can reach production in 4–6 months. The phases are: month 1–2 data architecture and ingestion, month 2–4 indexing and initial pipeline, month 4–6 production inference and monitoring, month 6+ iteration and lifecycle management. Clients who try to compress the data architecture phase usually regret it by month 4.
Do you operate the platform after launch, or hand it off to our internal team?
Both options are available. We can operate the platform alongside your team through a managed services arrangement, or do a structured handoff with documentation, runbooks, and a transition period. Most clients start with joint operation and transition over 3–6 months as their team builds confidence in the system. We don't ship and disappear on the first day it goes live.

→ Related reading

Enterprise RAG platform → Agentic operations → DehazeLabs vs Big 4 → DehazeLabs vs in-house AI team → Data center & infrastructure AI →

Building a multimodal AI platform from scratch?

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.

Schedule a conversation → Read our case studies