Tribe's Generative Blueprint helps you navigate this bleeding edge technology and apply it to business functions that reduce cost and increase profit.
In four weeks unlock the power of generative AI for your business.
Identify generative use cases unique to your business, explore which LLM is right for you, build a working prototype, fine-tune your model to optimize results, and get a comprehensive product roadmap and implementation plan.
We start by identifying generative use cases specific to your business and setting performance benchmarks to ensure alignment with your goals. Next we conduct stakeholder interviews to gain a deeper understanding of your organization's workflows to develop a clear success criteria for your generative product.
In week two of the Blueprint, we bring your generative use case to life. In this phase, we do rapid prototyping to build an MVP tailored to your identified use-case. Our team will test various Large Language Models (LLMs) to pinpoint the one that delivers optimal results for your specific needs.
We believe in a data-driven approach, and thus, in week three we continuously evaluate and measure the performance of your product to guarantee it meets established benchmarks and success criteria. Based on the results, we'll fine-tune the models and the prompts to optimize the outputs.
In the final week of the engagement our team will develop a comprehensive implementation plan, outlining the necessary steps to seamlessly transition your MVP into a full-scale production solution. We consider factors such as scalability, maintainability, and integration with existing systems, ensuring a smooth and efficient deployment.
Worked at Google Brain, Gretel AI
AI and research at Meta,
Allen AI, and Palantir
Worked for Nasa, Google,
Director of Data Science at Appen,
Researcher at Toyota
ML at Google
Early engineer at Amazon Data
ML Producer at Uber and Groupon,
HCI at Stanford
Researcher at University of Edinburgh
Product at Google
Engineering at Surge AI, Twitter
Harvard CS degree
Deep Learning Scientist at Ravel Biotech
Researcher at Harvard Medical School
Data Scientist at IBM and AWS,
Faculty at Carnegie Mellon
First employee at Weights & Biases,
Worked at Google
Worked for Google Brain
and Facebook AI Research
CTO at Togal
ML at Adblock
Software Engineering at Google
Linguistics at UCLA
VP of Eng at Carta
Head of Eng at Nova
Data at Google
Launched Apple card at Apple
AI Research at Amazon
PhD Cognitive Neuroscience, Published 50+ papers
Engineer at Cityblock Health
Sr Engineer Flatiron Health
Led ML at ClearBrain , Amplitude
ML at Amazon, Microsoft,
Engineer at RBC
ML scientist at Arterys medical
Worked at Asana, Wikimedia, CertiK
1st DS hire at Deserve
PhD from Cambridge University
Worked on NLP at Amazon Alexa
ML Engineer at Meta
Co-founder Habiter (YC S20)
Top AI talent
Tribe has built a network of 300+ leading AI and ML practitioners from companies like OpenAI, Google, Meta, by leveraging fractional work models and making it easy for top talent to start consulting.
Our own custom models
Sandr (our bespoke staffing LLM) and our extensive data infrastructure, enable us to staff technical dream teams in hours instead of weeks. Our process combined with our constantly growing pool of talent let us support companies as they evolve their across their AI lifecycle.
Tribe members have years of experience building and deploying real-world AI solutions at scale and across industries. Our network has deep expertise in finance, healthcare, insurance, private equity, ecommerce and more.
Our team has extensive experience in data privacy and security compliance, ensuring that your data remains secure and confidential. We provide secure storage of sensitive data through a combination of encryption, access control, and secure protocols.
Every company can be an AI company with the call of an API.