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.
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.
Reliably and effortlessly convert unstructured text into structured information
Built-in model censorship for advanced data protection
- 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.
FAQs
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.
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.
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.
Titan Takeoff uses censorship which, when enabled, only allows the model to answer using a pre-approved set of phrases.