Eventum builds cutting edge machine learning products, solutions, and models for organizations that want to be at the cutting edge. Based in the heart of San Francisco and led by seasoned AI experts, we specialize in helping clients create their AI agents and AI systems.
Our team members hold advanced degrees in machine learning and have decades of experience building AI products for the largest tech companies and fastest-growth startups.
We have deep expertise in every ML subfield from LLM agents to linear regression.
Rapidly augment your team with our expert ML/LLM engineers, researchers, MLOps specialists, team leads and other developers.
Our developers are available part time and full time to get your project to its milestones.
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Build AI Agents and AI Systems on top of OpenAI, Anthropic, or open source LLMs.
Extract relevant information from thousands of PDFs. Create a customer support chat agent. Screen resumes. Extract structured data from unstructured text.
Training/Fine-tuning. We've trained and fine-tuned thousands of models.
Evaluation. Model performance, accuracy, and scalability
Alignment/Post-Training. Align your models with algorithms like RLHF, DPO for safety and bias correction
We expertly orchestrate AI projects from start to finish, building you state of the art models and software. Working together with your team we allocate resources, manage risks, and ensure seamless execution for success.
Our team empowers ML initiatives through seamless MLOps. We build ETL, distributed model training, cloud infrastructure, cloud billing reports, continuous integration/deployment and anything else to keep your ML team efficient.
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Our engineers have experience with vision, diffusion, video, voice cloning, vision-LLMs, and coding models.
We can adapt open-source models to work for you, or train models from scratch.
We thoroughly explore your data, challenges, and AI capabilities to identify fresh business prospects, define specific use cases, and chart the course for AI software development.
Subsequently, we elucidate the feature set of the AI solution, devise the architecture, and select appropriate tools and technologies.
During the creation of a Proof of Concept, we engage in training and fine-tuning of ML models and AI algorithms, consistently subjecting them to testing for their feasibility.
We craft the application using optimal artificial intelligence development practices, integrate the ML models, and deploy your solution within the intended environment.
Image, video, text, and audio generation. Training custom generative AI models and fine tuning open source models.
Implementing Responsible AI Practices in Government Operations
Enhancing Clinical Workflow and Forecasting ICU Transfers
Streamlining Claims Management and Detecting Fraudulent Activities
Enabling Predictive Maintenance and Optimizing Transportation Processes
Personalizing Content and Improving Lead Scoring Techniques
Optimizing Assortment and Pricing Strategies for Retailers
Enhancing Customer Support with Predictive Insights
Addressing Credit Scoring, Customer Churn, and Anti-Money Laundering Challenges
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"Eventum built and managed our ML team, models, and software from the ground up at below market rates delivering incredible results. They are an essential partner for us that I couldn’t recommend more highly."
“Eventum helped us go from stuck to cutting edge in a matter of months, rewriting our entire ML training stack and continually supporting our R&D efforts.”
“Eventum came in and quenched our MLOps fire in quick order ensuring our ML Scientists could make rapid progress. We then immediately hired them to help build us a generative AI audio model from scratch”
Generative AI next gen social media. Plai Labs was founded by previous founders of MySpace and Jam City, Chris DeWolfe and Aber Whitcomb, receiving $33M in Seed Funding from a16z.
- We acted as interim ML team lead
- Built a team of 6 ML Engineers and 1 Product Manager from the ground up
- Launched app into the App Store in 4 months, currently going viral
DeepCell ($70M Series B) is a company building micro-fluidic microscope based cell sorting tech.
- Rewrote deep learning computer vision pipeline
- Applied self-supervised representation learning, adversarial learning, recalibration, and neural network architecture design
- Dropped manual labelling requirements from millions to thousands
- Provided R&D roadmapping & mentorship of ML team
Accent removal for call centres, enabling companies to match call centre operator voice to the geography of the customer. $50M raised
- Developed custom CI/CD, dramatically improving developer efficiency and decreasing chances of model regressions
- Created a cloud-agnostic solution to model training
- Decreased cloud spend by ~$30K a month
- Enabled a further 70% cost reduction in model training
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