AI Development

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Accelerate growth with our AI solutions

In the fast-paced digital age, we harness the power of data and AI to build custom solutions that modernise systems, create new digital opportunities, and drive exponential growth. Whether you're upgrading legacy systems or pioneering innovative business models, our AI development services are designed to elevate your organization to the forefront of your industry.

Artificial Intelligence is revolutionising industries by solving complex problems, enhancing decision-making, and optimising operations. From advanced Machine Learning to Deep Learning technologies, AI delivers precision, efficiency, and innovation, enabling personalised customer experiences and streamlined workflows.

Why Choose Us?

With a proven track record in cutting-edge AI technologies—spanning Computer Vision, Natural Language Processing (NLP), Large Language Models (LLM), and Generative AI—we empower businesses to unlock their full potential. Our solutions drive end-to-end digital transformation, ensuring your business remains agile and competitive.

  • AI Product Engineering: Bring your ideas to life with AI prototypes and Minimum Viable Products (MVPs).
  • Custom AI Software Development: Tailor-made intelligent solutions to address your unique challenges.
  • AI-Powered Automation: Achieve operational excellence with robotic process automation (RPA).
  • Seamless AI-Driven Transformation: Leverage cloud-based AI to future-proof your operations.
  • Generative AI Solutions: Revolutionise customer experiences and optimise business workflows.
  • Predictive Analytics and AI Integration: Gain actionable insights to make informed decisions in real-time.

Fuelling Growth Across Industries

From logistics optimisation to supply chain automation, our AI solutions are reshaping the way businesses operate. Let us help you streamline your processes, boost efficiency, and stay ahead of the competition.

Your Future Starts Here

AI isn’t just a tool—it’s a game-changer. Don’t let your business fall behind. Partner with us to embrace the possibilities of AI development and achieve sustainable growth. Contact us today to explore how our expertise can transform your organisation into a leader in intelligent technology.

All our frequently asked questions

These FAQs will help clarify important aspects of AI development and our processes, please see the contact page to ask us more specific questions and queries.

What AI Technology do we work with?
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The list below is dynamic and evolves as new models and technologies emerge daily. If you’re interested in working with a specific technology, simply ask us. If it’s not already in our arsenal, our development team typically requires about one month to become proficient.

Frameworks:

MXNet, PyTorch, Keras, Caffe2, Deeplearning4j, Detectron
Cloud Platforms: AWS, Google Cloud, Azure
Generative AI Models: GPT-4, GPT-3,GPT-3.5 Turbo, Lamda, DALL-E, Codex, Stable diffusion, Google Gemini, Claude 3, Transformer
Libraries: TensorFlow, Tensor2Tensor, tf-slim, NLTK, spaCy, AutoKeras, TensorRT, FastAI, TenorFlow Lite, PyTorch Mobile, Scikit-learn, NumPy, Pandas
Algorithms: Clustering, Metric Learning, FewShot Learning, Supervised/Unsupervised Learning, Online Learning, Ensemble Learning, Transfer Learning, Reinforcement Learning
Neural Networks: CNN,  RNN, Variational Autoencoders, Bayesian Network, Autoregraession Networks, Long Short-term Memory (LSTM)
Learning Paradigms: Representational Learning, Manifold Learning
Module/Toolkits: ONNX, Core MLModule/Toolkits - ONNX, Core ML
Image Classification Models: VGG-16, ResNet 50, Inceptoinv3, EfficientNet, YOLO, SSD, U-net, Vision Transformer, CLIP
Programming Languages: Python, R Programming, Java, JavaScript, Django, React, Angular, Vue,js, Ruby, Flask, ExpressJs
AI Development Tools: Jupyter, Anaconda, Google Colab, Deepnote,  RStudio, MiniConda, Virtualenv, Pienv, Kaggle Kernals, MS Azure Notebooks, Amazon Sagemaker Studio Labs, IBM
Data Warehousing & Management: Apache Spark, Hadoop, NoSQL,PostgreSQL,  MongoDB Amazon Redshift, Apache Flink, Apache Storm, Google BigQuery, Azure Synapse, Bigtable, Cassranda, Cosmos, Oracle, SQL Service, MySQL, NVIDIA RAPIDS, Apache HIVE
Intergration & Deployment: Kubernetes, Docker, Ansible, Terraform, Github Action, Jenkins, Splunk
Data Visualisation & Reporting: Tableau, Power BI, Matplotlib, Seaborn, Plotty, Amazon QuickSight, ChartJS, Apache Superset
API Development: Rest API, GraphQL API's, FastAPI
Edge Devices: Raspberry Pi, Jetson Nano, Intel NCS,  Deepstream, TAO Toolkit
Text Processing: OpenAI, Llamaindex, Langchain, Langflow, Dify, Flowise
Analytics: Rasa, Dialogflow, Logstash, Kibana, Grafana, Wavenet, Lyrebird, Resemble AI
Security: Oauth, let's encrypt, JWT, OpenID

What types of services do we deliver with AI?
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  • Visual Detection and Inspection
  • Chatbots and Virtual Assistance
  • Natural Language Processing (NLP)
  • Large Language Models (LLMs)
  • Data Science and Data Analytics
  • Process Automation
 How long does it take to develop and AI Models?
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The time required to develop an AI project varies widely depending on several factors, such as the complexity of the project, the scope of work, and the expertise of the development team. Below is a general breakdown of timelines for different types of AI projects: 

  1. Proof of Concept (PoC): 2–4 weeks
  2. Minimum Viable Product (MVP): 2–3 months
  3. Full-Scale AI Solutions: 6–12 months or more
  4. Custom AI Models: 4–8 months
  5. AI Integration with Existing Systems: 2–6 months
  6. Continuous Improvement: Ongoing 

Real-World Examples Chatbots:
Basic chatbots can be developed in 1–3 months, while advanced conversational AI systems may take 6–9 months.
Predictive Analytics Models: Typically require 3–6 months, depending on the dataset size and complexity.
Computer Vision Systems: Developing applications like facial recognition or object detection often takes 6–12 months.

What are the costs involved in developing an AI model?
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Summary of Estimated Costs

  1. Data Acquisition and Preparation: $1,000–$50,000+
  2. Development Team and Expertise: $50,000–$500,000+
  3. Infrastructure Costs: $5,000–$100,000+ depending on cloud usage or hardware investment.
  4. Model Development and Training: $10,000–$250,000+
  5. Licensing and Software Tools: $1,000–$50,000+ annually
  6. Testing and Deployment: $10,000–$100,000+
  7. Ongoing Maintenance and Updates: $10,000–$200,000+annually. 

Total Estimated Cost
For a typical AI project, costs can range from $50,000 to$1,000,000+, depending on scale, scope, and customisation.

All costs in US$

What are the stages involved in AI developing?
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AI development typically follows a structured process that includes multiple stages. The timeline for each stage depends on factors like project complexity, available resources, and technological challenges. Below is a general breakdown:

1. Problem Definition & Feasibility Analysis (1-4 weeks)

  • Define business objectives and use cases
  • Assess feasibility based on data availability and technical constraints
  • Identify success criteria (KPIs)
  • Perform market and competitor analysis

2. Data Collection & Preparation (4-12 weeks)

  • Gather relevant data from internal or external sources
  • Clean, pre-process, and label data
  • Handle missing values, outliers, and data inconsistencies
  • Split data into training, validation, and testing sets

3. Model Selection & Development (4-12 weeks)

  • Choose the right AI approach (Machine Learning, Deep Learning, NLP, etc.)
  • Select algorithms and frameworks (TensorFlow, PyTorch, Scikit-learn, etc.)
  • Develop and train initial models
  • Optimise hyperparameters and test different architectures

4. Model Training & Evaluation (6-16 weeks)

  • Train the AI model using training data
  • Evaluate performance on validation and test sets
  • Fine-tune the model to improve accuracy, precision, recall, etc
  • Conduct A/B testing or benchmark against existing solutions

5. Deployment & Integration (4-12 weeks)

  • Choose deployment strategy (cloud, edge, hybrid)
  • Develop APIs or integrate with existing systems
  • Optimise for scalability and efficiency
  • Set up continuous monitoring for model performance

6. Testing & Validation (4-8 weeks)

  • Conduct real-world testing with end-users
  • Validate against business requirements and compliance standards
  • Address biases, security vulnerabilities, and ethical concerns
  • Finalise documentation and deployment pipeline

7. Maintenance & Continuous Improvement (Ongoing)

  • Monitor model drift and retrain as needed
  • Collect user feedback for improvements
  • Update data sources and fine-tune models over time
  • Ensure compliance with evolving regulations

Total Estimated Timeline: 3-12 months

Simple AI solutions: 3-6 months (e.g., rule-based systems, small-scale ML models).
Medium complexity AI: 6-9 months (e.g., NLP chatbots, recommendation engines).
Advanced AI models: 9-12+ months (e.g., large-scale deep learning, autonomous systems).

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