Most generative AI projects fail in production, not in the demo. Building AI that works reliably with real users, real data, and real edge cases takes a different level of engineering. KiwiCommerce supplies UK-based generative AI developers who build production-grade LLM integrations, RAG pipelines, AI agents, and AWS Bedrock applications. 250+ digital products delivered since 2016.
A generative AI demo is easy to build. Getting the same system to handle edge cases, prompt injection, rate limits, and real user loads without breaking is where most AI projects fall short. Our developers engineer for production from the start, not as an afterthought.
Get In TouchVector database selection, chunking strategy, embedding choice, and hybrid search design all affect retrieval quality. We get these decisions right at the start.
Production AI systems need guardrails. We build input validation, output filtering, and adversarial prompt safeguards from day one, not after the first incident.
oken costs at scale are not trivial. Our developers model per-conversation cost, design model routing by query complexity, and configure prompt caching where applicable.
We understand how AI features interact with product data, inventory states, and customer journeys. That combination produces better AI product decisions.
Generative AI development in 2026 is a distinct engineering discipline. It is not data science, which trains models from scratch. It is not standard software development, which does not involve working with the specific failure modes of large language models: hallucinations, context window limits, prompt injection risks, cost at scale, and the gap between impressive demos and systems that stay reliable under real conditions.
A generative AI developer integrates foundation models into applications, designs the architecture that makes them useful in production, and manages the engineering challenges that standard developers are not trained to handle. RAG pipelines need a careful chunking strategy, embedding selection, and vector retrieval design. AI agents need tool-use logic, memory management, and failure handling. Prompt engineering for production requires systematic testing and versioning, not a single well-worded system prompt.
KiwiCommerce builds LLM integrations, end-to-end RAG systems, agentic AI workflows using LangChain and LangGraph, and AWS Bedrock applications using Bedrock Agents and Knowledge Bases. For eCommerce businesses, we also integrate AI features directly into Shopify, Magento, or WooCommerce, drawing on over a decade of eCommerce development experience and AI engineering expertise. An NDA is signed on every engagement. UK-based developers working your hours. Speak to us about your AI project today.
Discuss Your Project2+ Experience Years
Works with LLM APIs under close guidance, builds basic LangChain applications, implements simple RAG pipelines against documented vector database schemas, and contributes to prompt engineering on well-specified tasks. Familiar with Python, the OpenAI or Anthropic API, and foundational vector database concepts. Best suited for adding AI features to an existing application where the integration architecture has already been designed by a more senior developer.
5+ Experience Years
Designs and builds production-quality RAG pipelines independently. Proficient in LangChain or LangGraph, embedding strategies, vector database selection and query optimisation, structured output formatting, and advanced prompt engineering. Understands the failure modes of LLM-based systems and builds safeguards against them. Can choose correctly between pgvector, Pinecone, and Qdrant by use case and implement hybrid BM25 and vector search where retrieval quality demands it.
5+ Experience Years
Architects complex generative AI systems from requirements through to production deployment. Deep expertise in multi-agent architectures, RAG system design at scale, fine-tuning strategy decisions, LLM observability and cost monitoring, and the security design required to give LLMs access to enterprise data safely. Designs model routing strategies (Opus to Sonnet to Haiku) by query complexity and configures prompt caching to reduce inference cost. Makes the architecture decisions that determine whether an AI system stays reliable as usage grows.
3+ Experience Years
Specialist in building generative AI solutions on AWS infrastructure using Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Agents, and Bedrock Flows. Holds or is working towards the AWS Certified Generative AI Developer Professional (AIP-C01) certification, which validates production-grade AI system design on AWS with appropriate security, observability, and cost controls. Best suited for organisations standardised on AWS infrastructure where enterprise compliance posture matters.
5+ Experience Years
Combines generative AI backend engineering with frontend development in React or Vue.js. Builds complete AI-powered applications where the LLM integration layer, RAG pipeline, and user interface are developed under unified technical leadership. For eCommerce businesses adding AI-powered search, recommendations, or support automation, this profile avoids the coordination overhead and integration gaps that come from splitting AI backend and frontend across separate teams.
7+ Mixed levels
For businesses at the strategy and architecture stage who need senior generative AI expertise before development begins. Assesses whether RAG, fine-tuning, or prompt engineering is the right approach for a specific use case, evaluates foundation model providers against cost and performance requirements, identifies OWASP LLM Top 10 risks relevant to the application, and produces a technical specification a development team can build against. Prevents expensive AI projects from being built on the wrong foundations.
What You Get When You Hire Generative AI Developers
Connecting OpenAI GPT-4o, Anthropic Claude, Google Gemini, and Amazon Bedrock models to your application with proper error handling, rate limit management, retry logic, cost monitoring, and response validation. Integration that does not break under real traffic and does not silently return degraded outputs.
End-to-end retrieval-augmented generation systems: document ingestion and chunking, embedding generation, vector database setup and indexing, semantic and hybrid retrieval, and context injection into LLM prompts. Built to retrieve accurately from your actual data, not just on a clean test set.
Agentic AI systems using LangChain, LangGraph, AutoGen, or Bedrock Agents that plan multi-step tasks, use external tools and APIs, maintain memory across interactions, and execute complex workflows. Each agent includes failure handling and tool-use validation so autonomous behaviour stays within defined boundaries.
AWS-native generative AI development using Bedrock for foundation model access, Bedrock Knowledge Bases for RAG, Bedrock Agents for agentic workflows, and Bedrock Flows for orchestration. Includes the access controls, observability, and compliance architecture that AWS-native infrastructure makes possible.
Adding generative AI capabilities to an existing eCommerce platform, SaaS product, or web application without a full rebuild. Modular AI service layers that integrate cleanly with your existing Shopify, Magento, WooCommerce, or custom application architecture.
Evaluation frameworks that measure LLM output quality, detect hallucinations, and track accuracy against ground truth. Production monitoring for latency, cost, error rates, and model behaviour drift so degradation is caught before users encounter it.
Generative AI development has a large gap between agencies that have integrated one LLM API and those that have built systems that stay reliable at production scale. Here is what separates our approach.
Calling an LLM API takes a few lines of code. Building a system that handles prompt injection attacks, cost spikes from unexpectedly long outputs, hallucination in edge cases, and model provider outages is a different problem. Our developers address all of these at design time. Production-readiness is part of the brief from the first conversation, not a second phase after something breaks.
Generative AI development involves rapid iteration: prompt refinements, evaluation results, architecture decisions, and model behaviour questions that benefit from real-time discussion. Every developer on your project is UK-based. Technical conversations happen in your working day, not the next morning. For AI projects where the model's behaviour shapes the product design, that feedback loop speed makes a measurable difference.
KiwiCommerce has been building eCommerce platforms on Shopify, Magento, and WooCommerce since 2016. When our generative AI developers build AI-powered search, recommendation, or customer support automation for an online retailer, they understand how AI features interact with product catalogue structure, inventory states, and customer behaviour data. That domain knowledge produces better AI product decisions than AI engineering alone.
The AWS Certified Generative AI Developer Professional (AIP-C01) certification validates the ability to design and deploy production-grade AI systems on AWS using Bedrock, Bedrock Agents, and Bedrock Knowledge Bases, with proper security controls, access management, and observability. For organisations where AI must meet enterprise compliance requirements on AWS infrastructure, this is the credential that distinguishes properly architected systems from PoC-quality builds.
The OWASP Top 10 for LLM Applications covers the specific security risks of production AI systems: prompt injection, insecure output handling, sensitive information disclosure, and excessive agency. Our developers build defences against these risks by design, not as an afterthought. AI systems that touch customer data, internal documents, or business-critical workflows need this level of security engineering from the start.
Generative AI projects often involve proprietary documents, internal knowledge bases, confidential business data, and commercially sensitive architecture decisions about model selection and prompt design. Non-disclosure agreements are signed as standard on every project. Your data, your prompts, your AI architecture, and your vendor relationships remain private throughout and after the engagement.
KIWI Commerce redeveloped my entire website, exactly to my requirements. They worked exactly along with my timeframe and were fantastic to work with. I would highly recommend this company. Thank you Sam and the team!
Great response to tickets. We feel our website is in safe hands with Kiwi. Any issues are fixed very quickly. Great value for money. We have been working with Kiwi for a couple of years now and find them very helpful and easy to get along with. Would highly recommend.
Working with Kiwi Commerce has been a very smooth experience. Their team is responsive, knowledgeable, and always willing to go the extra mile. From development to ongoing support, everything is handled efficiently and with great attention to detail. Highly recommended.
We’ve been working with Kiwi Commerce for ongoing website improvements and support, and the experience has been consistently positive. They are proactive, easy to work with, and quick to respond whenever we need assistance. Great team and great service.
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A generative AI developer integrates foundation models into applications and business workflows. The work includes building RAG pipelines that ground model responses in your data, developing AI agents that plan and execute multi-step tasks, engineering and testing prompts for consistent model behaviour, evaluating and monitoring model output quality, and deploying AI systems to production infrastructure. The role is distinct from data science: a generative AI developer does not train models, but works deeply with their capabilities, failure modes, and the scaffolding that makes them useful for a specific business problem.
RAG stands for Retrieval-Augmented Generation. It grounds an LLM’s responses in your own data by retrieving relevant information from your documents, database, or knowledge base at query time and including it in the prompt. An LLM trained by OpenAI or Anthropic knows nothing about your products, policies, or customers. RAG fixes that. For most business AI applications where accuracy and current information matter, RAG is the right approach. It requires careful data architecture and retrieval design, but it is significantly less expensive and complex than fine-tuning.
Most production AI systems use all three, but at different layers. Prompt engineering is involved in every LLM application and sets the model’s behaviour and output format. RAG is right when information is specific to your business and changes frequently. Fine-tuning is right when a task is highly specialised, consistent, and where the performance gain from training on your data is worth the computational cost and model maintenance overhead. For most business applications, well-designed RAG with systematic prompt engineering outperforms fine-tuning at lower cost. If you are unsure which approach fits your use case, that is exactly what a discovery phase should answer.
The AWS Certified Generative AI Developer Professional (AIP-C01) is an AWS certification that validates the ability to build production-grade generative AI applications using Amazon Bedrock, Bedrock Knowledge Bases, Bedrock Agents, and related AWS services. It requires two or more years of production application development experience and at least one year of hands-on generative AI experience. The standard exam opened for registration in March 2026. It covers RAG architecture design, agentic AI system development, prompt engineering and management, responsible AI guardrails, and production deployment with security, observability, and cost controls on AWS.
Our developers work across the major foundation model providers: OpenAI GPT-4o, Anthropic Claude (Sonnet, Haiku, Opus), Google Gemini Pro and Flash, and Amazon Bedrock, which provides managed access to Anthropic Claude, Meta Llama, Mistral, and Amazon Titan models. For open-source model work, particularly fine-tuning on specific datasets, we work with models through Hugging Face. Model selection depends on the specific use case, latency requirements, cost constraints, and data privacy requirements. We advise on model selection as part of the discovery phase.
Yes. Our generative AI full-stack developers build end-to-end AI-powered applications: the Python FastAPI or Node.js backend handling LLM integration and RAG pipelines, the React frontend providing the user interface, and the AWS deployment infrastructure. For eCommerce businesses, we also integrate the AI layer with existing Shopify, Magento, or WooCommerce platforms. Full-stack capability removes the coordination gap between separate teams building the AI backend and the frontend interface, which is where AI product quality most often degrades.
All of our generative AI developers are UK-based and work remotely as standard. AI development involves frequent technical discussions about model behaviour, prompt refinements, and evaluation results. We structure remote engagements with regular touchpoints, shared evaluation dashboards for monitoring output quality, and agreed communication channels for the rapid-fire questions that come up during active AI development. For dedicated retainer arrangements, we agree the working process and tooling before the engagement begins.
A focused LLM integration or a single RAG-based feature can be delivered in two to four weeks for a well-scoped requirement. A complete AI-powered application with a custom frontend, production RAG pipeline, and AWS deployment typically takes six to twelve weeks, depending on the complexity of the data architecture and the number of AI features. Legacy system integrations and multi-agent architectures take longer. We give a realistic timeline based on the actual scope during the discovery phase, not an estimate based on a keyword.
Our UK-based AI development team is ready to look at your project. Whether you need a dedicated GenAI developer on a retainer, a team to build a complete AI-powered product, expert GenAI developers for a specific LLM integration or RAG pipeline, or an AWS GenAI developer for Bedrock-based applications, get in touch today. We respond within one business day.
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