AI Application Development Services

The investment required for custom AI application development varies significantly based on complexity, data infrastructure requirements, integration depth, and compliance constraints. Straightforward AI applications, a focused document processing tool or a single-purpose chatbot, typically fall in the range of $50,000–$150,000.

More complex enterprise AI platforms with multi-system integration, custom model fine-tuning, and regulated-industry compliance requirements typically range from $150,000–$500,000 or more.

We provide detailed scoping and cost estimates after an initial discovery engagement, where we understand your specific requirements rather than applying a standard rate card. Contact our team to discuss your use case.

Off-the-shelf tools and platforms offer speed and lower initial cost for common use cases. Custom AI application development is appropriate when your use case requires proprietary data, specific integrations with your existing systems, compliance with regulatory requirements, accuracy levels that generic models cannot reliably reach, or a workflow design that doesn't fit within a platform's constraints.

Most enterprise AI applications that deliver lasting competitive value require at least a layer of custom development. The question is how much. We help clients assess this during discovery rather than defaulting to a full custom build when a hybrid approach would serve them better.

Data handling, access control, and security architecture are part of our standard build process, not afterthoughts.

We hold ISO 27001 certification for information security management and design AI applications with role-based access control, data minimisation, encryption at rest and in transit, and audit logging as defaults.

For regulated industries: healthcare (HIPAA), financial services (SOC 2, PCI DSS), and others, we apply the relevant compliance framework from the architecture phase.

We do not use client data to train models without explicit agreement, and we structure data flows to support your obligations under GDPR, HIPAA, or applicable regional regulations.

Our AI AppDev work spans healthcare and life sciences (patient intake, clinical documentation, predictive scheduling), financial services and fintech (fraud detection, risk scoring, document intelligence), legal (email automation, contract review), logistics (demand forecasting, route optimisation), SaaS and software (AI feature development, LLM integration), wealth management, and manufacturing.

Our regulated-industry experience is a meaningful differentiator; we understand what production means in environments where accuracy, auditability, and compliance are non-negotiable.

A focused MVP for a well-scoped AI application, with defined inputs, outputs, and integration points, typically takes 8–14 weeks from kickoff to deployment.

Full production applications with broader scope, multiple integrations, and enterprise hardening take 4–9 months.

Timeline is largely determined by data readiness, integration complexity, and how clearly the requirements can be defined upfront. Our discovery process is designed to surface the factors that typically cause schedule risk before development begins.

Yes. We are not tied to a specific platform, cloud provider, or framework. We design AI applications to integrate with your existing infrastructure, whether that's a specific cloud provider, on-premise systems, an existing data warehouse, or a legacy application that needs to remain in place. Integration design is part of our standard approach, not an add-on.

AI technologies and stack we work with:

Foundation models and LLMs: OpenAI GPT-4o, Anthropic Claude, Meta Llama, Mistral, domain fine-tuned models
RAG and vector infrastructure: LangChain, LlamaIndex, Pinecone, Weaviate, pgvector, FAISS
ML frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers
MLOps and deployment: MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, Docker, Kubernetes
Cloud platforms: AWS, Microsoft Azure, Google Cloud Platform
Programming languages: Python, TypeScript, Java, Go
Databases and data infrastructure: PostgreSQL, Snowflake, Databricks, Apache Kafka, Redis

We select from this stack based on your requirements, not all of it, and not always the same combination.

You work directly with our technical team: AI engineers, solutions architects, and our VP of AI Engineering. We don't insert account managers between your team and ours.

For most engagements, you'll have a named lead engineer and architect who remain with the project, not a rotating resource pool. We treat knowledge transfer as a core deliverable, not an end-of-project activity.