AI Development

Build smarter products with AI at the core

We design and build AI-powered systems — from LLM integrations and RAG pipelines to full AI SaaS products. Senior engineers, production-ready architecture, 8–12 week delivery.

What we build

End-to-end AI development across the full product lifecycle.

LLM Integration

Integrate GPT-4, Claude, Gemini, or open-source models into your product. Chat interfaces, document Q&A, content generation, code assistants.

OpenAIAnthropicGemini

RAG Pipelines

Retrieval-Augmented Generation systems that let your LLM answer questions based on your private data — documents, databases, knowledge bases.

LangChainLlamaIndexPinecone

AI-Powered SaaS

Full-product development where AI is a first-class feature — not a bolted-on afterthought. Multi-tenant, subscription-ready, scalable from day one.

ReactNode.jsAWS

Agentic Systems

Autonomous AI agents that reason, plan, and take actions. Tool use, multi-agent orchestration, human-in-the-loop workflows.

LangGraphAutoGenCrewAI

ML Model Development

Custom ML models for classification, prediction, NLP, and computer vision. Full pipeline from data preparation to deployment and monitoring.

PyTorchTensorFlowHugging Face

AI Infrastructure

Scalable infrastructure for AI workloads — GPU provisioning, vector databases, model serving, batch processing pipelines, cost optimisation.

PineconeWeaviatevLLM

AI Tech Stack

Tools and frameworks we use in production AI projects.

LLM Providers

OpenAI GPT-4oAnthropic ClaudeGoogle GeminiMistralLlama 3

Frameworks

LangChainLlamaIndexLangGraphSemantic Kernel

Vector Databases

PineconeWeaviateQdrantpgvector

ML / Training

PyTorchTensorFlowHugging FaceMLflow

Backend

PythonFastAPINode.jsGo

Infrastructure

AWS SageMakerGCP Vertex AIDockerKubernetes

How we work

From first conversation to production deployment.

Discovery & Scoping

We audit your data, define the AI use case, and scope the MVP — typically within one week of signing.

Architecture Design

Model selection, pipeline design, infrastructure planning, and cost estimation before a single line of code.

Iterative Development

2-week sprints with live demos. You see working AI features early — not at the end of the project.

Evaluation & QA

LLM output evaluation, hallucination testing, latency benchmarking, and adversarial input testing.

Production Deployment

CI/CD pipeline, monitoring dashboards, cost alerts, and model versioning — ready for real traffic.

Ongoing Optimisation

Post-launch support, prompt refinement, cost reduction, and model upgrades as the ecosystem evolves.

Common questions

What clients typically ask before starting an AI project.

Can you integrate GPT-4, Claude, or other LLMs into my existing product?

Yes. We work with all major LLM providers — OpenAI, Anthropic, Google, and open-source models via Hugging Face or self-hosted Ollama. Integration typically takes 2–4 weeks depending on the complexity of your existing codebase.

How much does an AI development project cost?

A focused LLM integration (e.g. adding a Q&A feature to an existing product) starts from $15,000–$30,000. A full AI SaaS MVP with custom pipeline, frontend, and infrastructure ranges from $40,000 to $120,000. We provide a detailed estimate within 48 hours of a discovery call.

How do you handle hallucinations and output quality?

We implement structured evaluation frameworks: automated output scoring, human-in-the-loop checkpoints, retrieval grounding via RAG, and prompt engineering best practices. We also build monitoring so you can catch quality regressions in production.

Do you work with proprietary / confidential data?

Yes. We sign NDAs before any data is shared and implement data isolation, access controls, and encryption as standard. For highly sensitive data we can design fully self-hosted pipelines that never send data to third-party APIs.

Ready to build your AI product?

Book a free 30-minute discovery call. We'll review your use case and come back with a concrete approach within 48 hours.