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Deep Hope S1 · unified inference–training LLM platform

An industry LLM platform that trains while it serves

DH S1 uses a “train while serving” architecture. It includes every Anaconda algorithm package and lets statistical, machine-learning, deep-learning and large models — and strategies — be deployed and trained on one platform, on-premises, with inference and training unified. Models built in SAM deploy to DH S1 in one click and run in real time inside any industry’s business systems.

Trains while servingAll Anaconda packagesIndustry LLM distillationOn-premises in any industry
A translucent glowing brain with a neuron network floating above lines of code and data
Home of our industry LLMs

Three foundation services for the agents above

Using industry-model distillation, DH S1 builds specialized large-model capabilities: in finance it already delivers models specialized for banking, and the same method applies to knowledge and data in FMCG, pharmaceuticals and other industries.

RAG

RAG retrieval

Company policy, industry regulation, product material and cases as the factual base. Every answer cites its source — no source, no answer.

MULTIMODAL

Multimodal document understanding

Moves beyond OCR’s “seeing text” to “understanding the file” — the business meaning between accounts and between documents — and reasons on top of it.

GEN

Generation and understanding

Generation and comprehension for intelligent marketing, digital operations, intelligent customer service and more.

Every industry

What DH S1 does in each industry

A humanoid robot at an office desk works on a floating data screen, with dashboards on the wall behind
Banking

Real-time credit and risk decisions

Real-time application scoring, fraud interception and RAG over policy and regulation, with models and strategies updated online.

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A tilted large display shows marketing charts for targeted ad delivery and consumer behavior analysis
FMCG & Marketing

Rolling forecasts and content understanding

Rolling store and regional demand forecasts, help drafting campaign copy, and understanding shelf photos and policy documents.

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Lab researchers review molecular structures on tablets and a large screen while a robotic arm works nearby
Pharmaceuticals

Regulatory Q&A and report understanding

RAG over labels and regulations, multimodal understanding of adverse-event reports and real-time checks on sales-flow data.

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The inference–training loop

SAM, DH S1 and RIDE: online and offline, in both directions

SAM, DH S1 and the RIDE real-time intelligent decision engine form one inference–training loop. The online path makes real-time inferences and decisions; the offline path builds models in batch; the two feed each other so models keep improving and update online.

Online pathDH S1 / RIDE real-time inference and training
  1. Streaming dataEnterprise service bus · applications / orders · credit bureau / tax · power / registry / telecom · third party
  2. Data ingestion front-end
  3. Real-time cleansing
  4. Real-time variable derivation (VCD)
  5. LLM inference–training engine × RIDE decision engineMetric monitoring · A/B testing
  6. Online / offline ensemble
  7. Decision output front-end
  8. Business systemsMarketing · anti-fraud · credit · risk · supply chain · service · mobile
Models / strategies updated onlineSamples and outcomes flow back to drive offline iteration
Offline pathBatch modeling · continuous iteration
  1. Batch dataData warehouse · third-party data
  2. Pre-processing
  3. Feature engineering
  4. Model training
  5. Model evaluation
  6. Score conversion
The online path runs ingest–cleanse–derive–infer–decide–output in milliseconds, while the offline path runs five batch modeling steps. Linked in both directions, they close the inference–training loop.
See how SAM deploys models to DH S1 in one click
Get started

Start with one business question. Let SAM deliver a model you can sign off.

Whether you are in banking, FMCG and marketing, pharmaceuticals or another industry, try SAM online first — or talk to our modeling experts about your use case.

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