AI that knows your business inside out
Building a high-performing AI model takes more than just data — it requires the right strategy, architecture, and expertise. With custom AI model development by Vention, you get models designed for real-world impact and engineering peace of mind.
The case for custom models: When prebuilt AI isn’t enough
Custom development is more expensive and time-intensive than using ready-made models. Yet, the demand for custom AI model development is growing.
The market is expected to increase from $6.48B in 2024 to $20.96B in 2030, with a CAGR of 21.6 percent.
What do businesses win with custom artificial intelligence models?
No pre-built AI model can claim this. They can produce decent (even good) predictions, but most still require at least some customization. The question is: Would you settle for decent or good when you could achieve exceptional results?
Custom AI models are fully tailored to your use cases (no limits here), data, and systems (even legacy and custom software).
It’s hard to stand out using the same AI as your competitors. This is the case with pre-built AI models that are available to everyone.
Custom AI models offer unique features, deeper insights, and unmatched predictive accuracy — and are more challenging to replicate.
With pre-trained models, biased or manipulated data injections can be a serious issue. The impact of such injections varies by industry — it can range from disrupting business processes to life-threatening consequences in healthcare.
Disclosing sensitive data is another concern. Hackers can reverse-engineer your training dataset and extract confidential information.
Are custom models risk-free? No AI model is. But with custom development, the risks are significantly lower since you have greater oversight, from on-premises deployment to strong security mechanisms and controlled updates.
Pre-trained AI models are built by vendors, often functioning as black boxes with little transparency or control. In contrast, custom AI models put you in charge, from robust security measures to a clear understanding of data storage and processing.
Owning your model ensures it’s bias-free and explainable — transparent in its decision-making.
With pre-built models, scaling costs can outweigh the investment in custom AI model development, as you’re charged for every API call — costs that skyrocket at scale.
With custom models, you’re not dependent on a vendor’s infrastructure or affected by performance slowdowns when their processing power reaches its limits.
Hear from our expert
Is custom AI worth the investment?
Custom AI models require an upfront investment, which can seem daunting, especially when good-quality pre-built models are available for free or at a low cost. But the decision isn’t that simple.
In the long run, custom AI delivers significant ROI, offering unique advantages in user engagement, operational efficiency, and brand differentiation — benefits that off-the-shelf models rarely match.
Making an informed decision means considering multiple factors, from competitive market analysis to evaluating existing AI models and conducting a cost-benefit assessment to determine the best path forward.”
Makhmudjon Sodikov
Machine learning engineering manager
Need custom AI models built for your unique challenges, delivering accuracy, security, and seamless scalability?
How we build custom AI models
Ever wondered what goes into end-to-end AI model development? Here’s a behind-the-scenes look at how our experts craft your AI solutions.
1. Define the use case
What kind of AI solution do you need? Does it need to interpret images, mine data, or analyze speech? Will AI be embedded in a product or an enterprise tool? What specific task will it perform?
Answering these questions is critical for choosing the right AI framework.
2. Scrutinize your data
Even a perfectly built model won’t work well if your data is messy. Auditing data sources is the first critical step, followed by thorough data preparation. Key steps include:
Data cleaning: Duplicates, erroneous entries, and missing values can all undermine the accuracy of your AI model.
Data bias: The data set for AI model training should be compiled from diverse data points to ensure the model produces high-quality results when it’s live. For example, custom AI models for medical image analysis should include different demographics and medical conditions and present balanced data without overweighing particular groups.
Data annotation: Assigning tags is another critical point of data preparation. Consider the AI model for sentiment analysis: It’s first trained based on labeled data sets that mark a piece of text as negative, positive, or neutral.
3. Engineer features
Our AI engineers define the model’s features unless we rely on deep learning, in which case the model itself determines them.
A feature is an attribute that allows an AI model to make predictions. Suppose you want to predict sales based on past data. Then, the day of the week, holiday marker, average basket size, and seasonality index can be among the critical features.
4. Design the model
This multi-step process begins with defining the model type and narrowing down suitable AI algorithms:
Type explained
Example of tasks
Sample algorithm
Classification model
Allocation of input data among predefined categories
Spam detection, fraud detection
Decision trees, random forests, convolutional neural networks (CNNs), transformers
Sequence-based model
Predictions based on past data and temporal patterns
Customer churn prediction, demand forecasting
Recurrent neural networks (RNNs) like long short-term memory (LSTM) and gated recurrent unit (GRU), transformers
Regression model
Prediction based on multiple independent factors (e.g., predicting sales based on advertising spend, store location, and product category)
Sales forecasting, price prediction
Linear regression, XGBoost, neural networks
Generative model
Creation of new data based on learned patterns
Generation of text or images
Variational autoencoders (VAEs), generative adversarial networks (GANs), and large language models (LLMs)
If pre-built AI models exist, we can test them to see how well they handle your data. Based on the results, our AI engineers estimate whether customizing the model (fine-tuning its parameters) will help achieve the needed quality.
If no pre-trained model fits, we design a custom architecture, which may combine different approaches, such as a CNN for feature extraction and an LSTM for sequence analysis.
The final architecture choice considers the model’s scalability and a perfect balance between prediction accuracy and processing speed.
5. Train the model
A part of the training data set is used to teach the model to recognize patterns and dependencies in data. During the training, AI engineers optimize the model’s internal parameters — think of weights and biases in neural networks, coefficients in regression models, or split points and depth in decision trees.
When the model performs well, it’s time to feed it with another part of the training data set. This is needed to validate that the model performs equally well on new data. If it does, it’s ready to go live. If it fails, AI engineers continue tuning the model’s parameters and improving the training data to achieve a better model performance in pattern recognition and generalization.
6. Deploy, monitor, and evolve
We integrate the model into your IT environment, ensuring it fetches all necessary data and seamlessly connects to all planned systems via APIs.
We also recommend implementing a monitoring tool to continuously track the model’s performance. It will indicate if the model’s prediction accuracy declines or if its features become irrelevant, e.g., due to market trends or shifts in customer preferences.
In most cases, AI models should evolve. As mentioned earlier, market shifts can drive this need, but businesses may also choose to add more data sources to improve prediction accuracy. In such cases, retraining the model with updated data is a logical step.
Tailored to your needs, built by our top AI experts so you get powerful, scalable AI without the hassle.
Why Vention is your go-to custom AI model development partner
Years of experience in custom software development
Completed AI projects
AI professionals proficient in deep learning, machine learning models, natural language processing, and computer vision
Dedicated to implementing top-notch security practices according to ISO 27001
Continuously recognized for impact and growth by the Financial Times, IAOP, and Inc. 5000.
Models of success: Hear it from our clients
They have done multiple projects around AI, ML and VR spaces. We worked on leading-edge voice agent technology.
They recently built an AI agent with Ph.D.-level research and development skills; the result was a breakthrough for that industry.”
Paul Steckler
Founder & Senior Partner, Ramp Catalyst
Models of success: Hear it from our clients
We develop and distribute artificial intelligence (AI) software to extract actionable intelligence for brands, and to detect abuse on the internet
Despite our very niche and specialized application domain, the developer assigned to us has been doing a great job understanding our needs and developing a quality product.”
Vadim Berman
CEO, Tisane Labs
Models of success: Hear it from our clients
The Vention team has been a truly supportive and professional partner throughout our project.
They’ve shown a remarkable level of personal engagement with our goals, demonstrating a genuine commitment to our success as a startup. Communication was always clear, and they kept us fully informed at every step, ensuring we were aligned throughout the process. The management team, in particular, understood the unique challenges we face as a startup, offering innovative solutions while being mindful of our limitations. Their flexibility, collaboration, and proactive approach have played a key role in the smooth progress of the project, making them an invaluable asset to our team.”
Elena Minich
Head of Content, Export Hunter UAB
Models of success: Hear it from our clients
The model we chose is team extension, developers joining the core engineering team.
We're happy with those additions to the team. The programmers are a fantastic addition to the group. They were promptly integrated and brought up to speed. The level of expertise of the developers they let me interview is impressive. And the ones we eventually hired.”
Lior Harel
CTO, Staircase AI
Our services
As a leading AI development company, we offer full-cycle custom AI model development services. You can involve our AI experts for strategic guidance, hands-on programming, integration, support, and evolution.
Consulting
How we support your strategic decision-making:
- Audit your data sources and data quality;
- Support you in deciding between customizing a pre-built model and going fully custom;
- Test the feasibility of a custom AI model;
- Estimate return on investment;
- Choose best-fitting algorithms and underlying AI frameworks;
- Help decide between on-premises and cloud deployment;
- Advise on security.
Development
What we do to build custom AI models of the highest quality:
- Prepare (clean, augment, label) your traditional and big data;
- Write and optimize model code, ensuring scalability, efficiency, and seamless integration;
- Finetune the model’s hyperparameters to achieve the required performance;
- Monitor the model’s performance on testing, validation, and real-world data;
- Deploy the model in the production environment and integrate it with your systems.
Support and evolution
To keep your AI adaptive and efficient, we:
- Implement monitoring tools;
- Add new data sources;
- Finetune the model;
- Retrain the model;
- Optimize the use of cloud or server resources.
Every industry plays by different rules. Custom AI and Vention help you win
Our experts bring a rare mix of technical depth and industry know-how. With hands-on experience across 30+ sectors, we build AI models that deliver measurable value.
Healthcare
Finance and banking
Retail
Manufacturing
Automotive
Energy
Telecom
Media and entertainment
Marketing and advertising
Real estate
Game dev
Professional services
Custom AI model development
Case study
Comet
Comet, a leading machine learning platform, partnered with Vention to boost AI development, integrate with key ML libraries, and refine UX. Over three years, our engineers improved experiment management, enabling real-time tracking and seamless data logging with just one line of code.
What’s more, we optimized model production monitoring, which strengthened Comet’s Python library for tracking neural network inputs and outputs. To support the rise of large language models, we also helped launch Comet LLM, an industry-first tool for visualizing and debugging AI interactions.
FAQs
What’s the price tag for custom AI model development?
No two projects are exactly alike. Development costs depend on model complexity, security and compliance requirements, performance needs, and accuracy expectations. To get a tailored estimate, complete our free project cost calculator form.
What if my data is low quality?
There are two possible scenarios: either building a custom AI model is not feasible, or extensive data preparation is required. We can’t provide a recommendation without first auditing your data.
What technologies do you use to develop and deploy AI models?
We leverage industry-leading AI/ML frameworks, cloud platforms, and data engineering tools to ensure scalable, high-performance models. Here are just some of them:
AI/ML frameworks: TensorFlow, PyTorch, sci-kit-learn, XGBoost;
Cloud platforms: AWS, Google Cloud, Azure (including on-prem and hybrid deployment options);
Data processing & storage: Apache Spark, Kafka, Hadoop, PostgreSQL, MongoDB
MLOps & Automation: MLflow, Kubeflow, Airflow, Docker, Kubernetes.
We tailor the technology stack to each project, selecting the most performant yet cost-effective solutions to align with your business goals and budget.
If we need top-notch security, should we deploy the model on-premises?
While on-premises deployment offers the highest level of control and security, it’s not the only option. Private cloud and hybrid setups are also worth considering. Security is essential, but your strategic decisions should balance it with performance and accuracy.
How will you ensure the scalability of the AI model as our business grows
Scalability can be achieved via architecture, model efficiency, and automation. This is a well-balanced range of decisions that can cover introducing microservices, optimizing data pipelines, splitting the workload between the internal servers and the cloud, and dynamic cloud resource allocation.