The fastest and easiest way to deploy LLMs - Titan Takeoff Inference Server 🛫

Optimize model reliability for enterprise-grade applications

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

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

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

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

Titan Takeoff runs locally, so your sensitive data never leaves your secure perimeter.

Structured outputs
Reliably and effortlessly convert unstructured text into structured information
Convert unstructured text into structured data effortlessly with Titan Takeoff’s JSON and REGEX controllers. Ensure models can only output JSON / REGEX in the correct form.
Integrate diverse data types and structures into downstream applications. Ensure your model always outputs in the right format. 
  • Use Titan Takeoff’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.
Model censorship
Built-in model censorship for advanced data protection


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.

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. 

How does Titan Takeoff guarantee a pre approved JSON or REGEX output? 

Titan Takeoff uses 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. 

How can you prevent AI models from leaking sensitive data?

Titan Takeoff uses censorship which, when enabled, only allows the model to answer using a pre-approved set of phrases.