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mODEL RELIABILITY

Optimize model reliability for enterprise-grade applications

Minimize errors and innacuracies in your AI applications. Boost your model’s reliability with TitanML's best-in-class controllers and RAG integrations.

Retrieval augmented generation (RAG)
Securely enrich Generative AI models with your data

Use TitanML to build Enterprise Retrieval Augmented Generation (RAG) applications, enriching Generative AI models with your data. 

Integrate effortlessly with all major vector databases. TitanML's integrations support all leading embedding models, meaning you can build entire RAG applications within a single private inference server. 

Our Enterprise Inference Stack runs locally, so your sensitive data never leaves your secure perimeter.

Structured outputs
Reliably and effortlessly convert unstructured text into structured information
01
Convert unstructured text into structured data effortlessly with our JSON and REGEX controllers. Ensure models can only output JSON / REGEX in the correct form.
02
Integrate diverse data types and structures into downstream applications. Ensure your model always outputs in the right format. 
Model censorship
Built-in model censorship for advanced data protection
  • Use TitanML's controllers to censor your model; this means it can only say pre-approved phrases and words.
  • Prevent mission-critical internal and external leaks. Ensure compliance, safeguarding sensitive data from falling into the wrong hands.
FAQ

FAQs

01
What is retrieval augmented generation (RAG)?

Retrieval augmented generation (RAG) is a popular method for enhancing factuality and groundedness of the outputs of a machine learning model with a corpus. Unconstrained generation from LLMs is prone to hallucinations and it is difficult / error-prone to finetune to add capabilities or knowledge to a model. Allowing access to a corpus of data at model runtime, for example, a company wiki or open source documentation, can add capabilities without requiring finetuning.

02
What are examples of unstructured to structured transformations?

Popular unstructured to structured transformations include document processing. For example, processing a long form document (such as a contract or a product review) and extracting the key information in a structured form to populate a database. 

03
How does TitanML guarantee a pre approved JSON or REGEX output? 

We use token masking to ensure that the language model is only able to select from the tokens that will not break the JSON or REGEX schema. 

04
How can you prevent AI models from leaking sensitive data?

Our Enterprise Inference Stack uses censorship which, when enabled, only allows the model to answer using a pre-approved set of phrases.