For over 10 years, we've been helping startups, enterprises, and product companies turn their data into actionable decisions using our real-world machine learning models. The models we develop today don't live in labs; they run in production, solving real-world problems such as improving efficiency, reducing risk, and driving measurable growth.
Digilawyer needed an ML assistant to deliver real-time legal guidance and educate them about their rights. We built a legal AI/ML assistant powered by the MCP protocol, letting users access real-time information, especially in tier 2 and tier 3 cities.
Legal research time reduced from hours to seconds.
From custom-built models to MLOps pipelines and compliance-ready delivery, these capabilities keep your ML systems running in production.
Here's why partnering with the right ML development firm can turn your data into measurable ROI.
From ideation to deployment, our sole purpose is to serve: build custom ML solutions that automate processes and provide actionable insights for your business. We develop AI/ML models across diverse industries, including aviation, energy and trading, travel and hospitality, and legal tech.
Here's the step-by-step roadmap that we follow for building, training, evaluating, deploying, and scaling our ML models with our data scientists.
Whether you need to fine-tune an existing model (often 2-6 months) or build from scratch (often 6-12 months), pick the engagement that matches your timeline and data readiness.
From data readiness and model design to development, BigOhTech has delivered ethical and responsible ML systems for DigLawyer, Lufthansa, and 5 Star Luxury Hotel.
The models we develop today don't live in labs; they run in production. Compare how a dedicated ML partner stacks up when you need forecasting, fraud detection, or recommendations that actually ship.
As a Machine learning development service provider, we're helping businesses of all sizes, from startups to enterprises, to build ML applications using emerging technologies such as AI, computer vision, NLP etc.
Our ML solutions enable them to forecast demand, maximise their ROI, and make predictions from complex datasets - on cloud platforms you already trust.
We've partnered with Microsoft Azure, Google Cloud, and AWS to deliver powerful machine learning and cloud-based platforms that organizations can use, trust, and scale.
Here's how companies are getting value from these technologies - Our ML solutions enable them to forecast demand, maximise their ROI, and make predictions from complex datasets. Over the next 3 years, 92% of companies are planning to increase their investments in AI (McKinsey).
As a Machine learning development service provider, we're helping businesses of all sizes, from startups to enterprises, to build ML applications using the emerging technologies such as AI, computer vision, NLP etc.
The time to see results from an ML project depends on several factors, such as how quickly the solution is adopted, how easily it scales, and how well it aligns with business goals. While some projects can deliver quick wins and measurable ROI within a short time.
We take care of the end-to-end ML development process, from planning to deployment, and post-launch maintenance and support. With real expertise across industries such as aviation, energy and trading, hospitality, and legal tech, we know how to deliver real value from these intelligent models.
No, we manage the ML model development process and handle the training on our own infrastructure.
ML models can be integrated directly with your existing systems, such as CRMs, ERPs, and other customer-facing apps, through secure APIs, and adopt best MLOps practices to deliver seamless performance.
Fine-tuning the existing model can take 2-6 months, depending on model size, complexity, and business use case. Developing a new model from scratch requires 6-12 months due to the time required for data collection, training, and validation.
We use data processing methods to handle missing data and outliers, and to normalize data to improve model performance. We carefully select the most relevant features to reduce complexity.
We train the machine learning models on labelled and unlabelled datasets. We work with any type of data that can be converted into numbers, such as text, images, video, graphs, etc. More data generally leads to better model performance. Data diversity is also an important parameter that helps the model learn from diverse data. If your existing datasets don't match these standards, then we'd advise you to collect new training datasets or reuse available training datasets.
If you currently have another team working on your project, our consultants will collaborate with you. They will review your existing documentation and understand your project scope to determine the necessary resources.
BigOhTech has a skilled team of machine learning engineers and data scientists who can assess your business needs and data infrastructure to build tailored ML solutions, including predictive analytics, natural language processing, computer vision, and recommendation engines.
Pair machine learning development with the AI, data, and engineering services that keep models useful in production.
Share your project goals, timeline, and technical requirements. We'll review your requirements and send a tailored solution with an indicative estimate within 48 business hours.
