Data sciences teams seek partners that can perform these data preparation functions for them at large-scale and at high quality, while using automated tools to minimize cost. As AI projects become more specialized and mission-critical and data preparation becomes increasingly complex, data science teams seek partners with deep domain knowledge and an infrastructure in which data security is assured.
We believe that Innodata is ideally situated to partner with data science teams.
In 2024, we expanded existing relationships and forged new relationships with several of the world’s large technology companies to support their efforts at building generative AI foundation models. For these companies, we are now providing or are poised to provide a range of scaled data solutions and services. Our scaled data solutions include providing instruction data sets for fine-tuning LLMs to understand prompts, to accept instruction, to converse, to apparently reason, and to perform the myriad of incredible feats that many of us have now experienced. We also provide reinforcement learning and reward modeling, services which are critical to provide the guardrails against toxic, bias and harmful responses, and model evaluation services.
For social media companies, financial services companies, and many others, we collect or create training data, annotate training data, and train AI algorithms for working with images, text, video, audio, code and sensor data.
We utilize a variety of leading third-party tools, proprietary tools and customer tools. For text annotation, we use our proprietary data annotation platform that incorporates AI to reduce cost while improving consistency and quality of output. Our proprietary data annotation platform features auto-tagging capabilities that apply to both classical and generative AI tasks. Our platform encapsulates many of the innovations we have conceived of in the course of our 35-year history of creating high-quality data.
In addition, because collecting real-world data is often impracticable (due to data privacy regulations or rarity of cohorts and outliers), we create high-quality synthetic data that maintains all of the statistical properties of real-world data, using a combination of domain specialists and machine technologies that leverage LLMs.
We are presently working with five of the largest technology companies, and several of the world’s leading brands spanning multiple verticals, to enable, accelerate or enrich the services they deliver to end users around generative AI foundation models and other AI that supports chatbot assistance, facial recognition, social networking, podcasts, legal research and medical diagnostics, to name a few.
The AI data training market is estimated to be $12.7 billion in 2024, projected to grow at a CAGR of 22% to reach $92.4 billion by 2034,2 essentially proxying the enormous growth expected in AI system spending overall ($632 billion by 2028, a 29% CAGR over the 2024-2028 forecast period).3 Similarly, the global data annotation tools market was valued at $2.02 billion in 2023, and is projected to reach $23.11 billion by 2032, which is a CAGR of 31.1%.4
AI Model Deployment and Integration
We believe that over the next decade, almost all industries will be fundamentally reinvented through the advent of high-performing AI models. We help businesses leverage the latest AI technologies to achieve their goals. We develop custom AI models (where we select the appropriate algorithms, tune hyperparameters, train and validate the models, and update the models as required). We also help businesses fine-tune their own custom versions of our proprietary models and third-party foundation models (including LLMs) to address domain-specific and customer-specific use cases.
2 Data Labeling Solution and Services Market, FactMR (Apr. 2024)
3 Worldwide Artificial Intelligence Systems Spending Guide, IDC (Aug. 2024)
4 Data Annotation Tools Market, Astute Analytica, (Nov. 2024)