Today Innit, a startup best known for its shoppable recipe and smart kitchen software solutions, announced the release of FoodLM, a software intelligence layer that helps power more contextually relevant food-related answers from generative AI large language models (LLMs).
The new platform, which itself is not a new LLM, is instead a software intelligence layer built to plug into existing LLMs to do pre and post-processing of queries to help provide better answers around a variety of food-related topics.
From the announcement:
FoodLM enables powerful semantic search for retailers to go beyond keywords and understand intent. Brands can provide consumers with highly personalized AI assistance from product selection through preparation and cooking. For health providers supporting patients with chronic diseases such as type 2 diabetes, FoodLM provides powerful science-backed assistance for healthy eating and food as medicine.
Innit CEO Kevin Brown described FoodLM as a “vertical AI” expert layer that can integrate into popular LLMs such as OpenAI’s GPT4 or Google’s PaLM. Brown compared FoodLM to what Google has done with Med-PaLM, which is Google’s medical knowledge layer that provides focused answers that are so contextually smart around medical information that it has started to pass the medical exams.
“You’re going to need the pairing of an LLM with expert training and expert systems to narrow it down for certain functions where it’s essential to be accurate,” Brown said.
The biggest concern with LLMs today is their tendency to hallucinate. Brown says that integrating with a vertical knowledge layer increases the likelihood of more relevant and accurate answers, ultimately leading to more trust in these systems.
“Food queries are one of the top use cases for LLMs, helping with tough problems like helping to manage people’s diets,” said Brown, “But only if you can trust them. If you can trust these systems and ensure they reflect key dietary and health factors, it becomes much more valuable.”
According to the company, answers are pre-processed and post-processed through FoodLM’s focused computation models, which it calls validators. The different validators within FoodLM include:
- Nutrition & Diets: Analyzes more than 60 diets, allergies, lifestyles, and health profiles to provide detailed recommendations tailored to individual needs.
- Health Conditions: Provides dietary guidelines, product scoring, and content specifically designed for conditions such as type 2 diabetes or hypertension.
- Personalized Shopping: Automated grocery purchases, incorporating personalized scoring and selection of over three million grocery products worldwide.
- Culinary & Cooking: Advanced logic to ensure that AI-generated recipes follow culinary guidelines and are cookable. Seamlessly integrates with smart kitchens, featuring automated cooking programs.
For now, Brown says FoodLM will be used by its partners through custom integrations via API. Over time, he sees the system as having a more approachable user interface where the system is used via a SaaS model.
From my perspective, FoodLM makes lots of sense for Innit. While we’ve already seen similar moves from some data-service and SaaS providers in the food space, Innit’s offering goes further and has more granular breakouts to provide specific contextualized offerings to power food-related services for their CPG, appliance, and health/wellness industries.
If you’re interested in the intersection of food and AI, make sure to check out The Spoon’s Food AI Summit, which is on October 25th in Alameda, California.
This post originally appeared on TechToday.