Have an AI use case but unsure which approach -- RAG, fine-tuning, agents, or custom ML -- is the right fit?
Built an AI prototype that works in demo but fails in production at real-world scale?
AI Development Services
AI development is not a single thing. It spans generative AI applications, RAG pipelines, AI agents, machine learning models, computer vision systems, natural language processing, and voice AI -- each requiring different expertise, different infrastructure, and different evaluation criteria.
We build AI systems across the full stack: from the data and embedding layer through the model integration to the production application. Every engagement starts with the problem, not the technology.
Generative AI, RAG, AI agents, ML, NLP, computer vision, and voice AI
Model-agnostic -- GPT-4o, Claude, Gemini, Llama, and open-source models
Production-grade: monitoring, evaluation, cost management, and failure handling
From proof of concept to full production deployment
RaftLabs builds AI systems across the full stack: generative AI applications, RAG pipelines for knowledge retrieval, AI agent systems for multi-step task automation, machine learning models for prediction and classification, NLP for text understanding, computer vision for image analysis, and voice AI for conversational interfaces. We are model-agnostic -- we select from GPT-4o, Claude, Gemini, and open-source models based on your use case. Every production AI system we deliver includes evaluation frameworks, monitoring, and failure handling.
The gap between AI demo and AI product
Every impressive AI demo has three things behind it: a well-scoped problem, the right approach for that problem, and engineering discipline to make it work reliably. Most failed AI projects got at least one of those wrong.
We start every engagement by getting all three right.
What we build
Generative AI applications
Production applications powered by large language models: AI assistants grounded in your knowledge base, document analysis and extraction, content generation at scale, and conversational interfaces for your specific use case. We handle prompt engineering, RAG pipeline development, output validation, and the full application layer. Model-agnostic -- GPT-4o, Claude, Gemini, or Llama depending on what your use case requires. See Generative AI Development and Generative AI Integration.
RAG pipelines and knowledge retrieval
Retrieval-augmented generation systems that ground AI responses in your documents, data, and knowledge. Vector database setup, embedding pipelines, hybrid search, re-ranking, and context assembly. Evaluation framework to measure retrieval quality. The retrieval infrastructure that makes AI assistants and document Q&A systems accurate rather than hallucinating. See RAG Pipeline Development and Vector Database Development.
AI agents and multi-step automation
AI agents that plan and execute multi-step tasks using tools: querying databases, calling APIs, processing documents, and making decisions based on intermediate results. LangGraph orchestration for stateful workflows. Human-in-the-loop checkpoints for high-stakes decisions. Production failure handling and monitoring. See AI Agent Development, Multi-Agent Systems, and AI Orchestration.
Machine learning and predictive analytics
Custom ML models for prediction, classification, and anomaly detection: customer churn prediction, demand forecasting, fraud detection, pricing optimisation, and recommendation systems. Data audit, feature engineering, model training, evaluation, and production deployment with monitoring. See Machine Learning Development and Predictive Analytics.
NLP and computer vision
Natural language processing for text classification, entity extraction, sentiment analysis, and document understanding. Computer vision for object detection, image classification, document OCR, and visual inspection. Both traditional ML-based and LLM-based approaches depending on your data and accuracy requirements. See NLP Development and Computer Vision Development.
Voice AI and conversational interfaces
Voice AI systems for inbound call handling, phone interviews, customer support, and conversational automation. Speech-to-text, intent recognition, dialogue management, and text-to-speech integration. Real-time latency optimisation for natural conversation feel. See Voice AI Development and AI Chatbot Development.
Have an AI use case you want to validate?
Tell us the problem, your data, and what good output looks like. We'll tell you which approach we'd recommend and what a proof of concept would involve.
AI by industry
Industry-specific AI pages covering the use cases most common in each vertical:
AI for Insurance -- claims automation, fraud detection, underwriting risk scoring, churn prediction
AI for Logistics -- demand forecasting, route optimisation, predictive ETAs, exception detection
AI for Manufacturing -- predictive maintenance, computer vision QC, yield optimisation, energy forecasting
AI for Retail -- personalised recommendations, dynamic pricing, demand forecasting, churn prediction
AI for Real Estate -- automated property valuation, lead scoring, document extraction, market forecasting
AI for Healthcare -- clinical documentation, prior auth prediction, readmission risk, revenue cycle optimisation
AI for Education -- adaptive learning, student performance prediction, automated assessment, AI tutoring
AI for Hospitality -- dynamic pricing, demand forecasting, guest personalisation, churn prediction
AI for Construction -- project schedule risk, cost overrun prediction, safety compliance, BIM analysis
AI for Legal -- contract review, legal research, document extraction, matter cost prediction
AI for FinTech -- credit risk scoring, fraud detection, AML anomaly detection, document extraction for lending
AI for Energy -- predictive maintenance, demand forecasting, pipeline anomaly detection, renewable output forecasting
AI for Telecom -- churn prediction, network anomaly detection, fraud detection, capacity planning
AI for Travel -- dynamic pricing, demand forecasting, personalised recommendations, review sentiment analysis
AI for E-commerce -- personalised recommendations, dynamic pricing, demand forecasting, fraud detection
Related services
Generative AI Consulting -- AI strategy and use case prioritisation before building
AI Consulting -- AI readiness assessment and roadmap
Custom AI Development -- end-to-end custom AI product development
AI MVP Development -- fast first version to validate AI use cases
LLM Fine-Tuning -- custom model training for specific tasks
Frequently asked questions
The right approach depends on what your AI system needs to do, what data you have, and what constraints you're operating under. RAG (retrieval-augmented generation): if you need to answer questions from your existing documents, knowledge base, or data -- without training a model. AI agents: if you need to automate multi-step workflows where the AI needs to use tools, make decisions, and adapt to intermediate results. Fine-tuning: if you have a specific, narrow task and a labelled dataset, and a general model's accuracy isn't sufficient. Custom ML: if you have a prediction or classification problem, labelled historical data, and need a model trained on your specific data. We diagnose the right approach in a scoping session before recommending a build.
No. We use OpenAI (GPT-4o, GPT-4o mini), Anthropic (Claude 3.5 Sonnet, Claude 3.7), Google (Gemini 1.5 Pro, Gemini 2.0), Meta (Llama 3), and open-source models depending on what's right for the use case. Model selection is driven by performance on your task, cost at your volume, data residency requirements, and latency constraints. We have production experience with all the major frontier models and will tell you the trade-offs honestly -- including when a cheaper or open-source model is a better fit than the most capable frontier model.
An AI proof of concept (POC) is a time-boxed build that answers a specific technical question -- does this approach work on our data, with acceptable quality, at feasible cost? POCs make sense when: the task is novel enough that there's genuine uncertainty about whether AI can do it well; the data quality or availability is unknown; or there are compliance, latency, or cost requirements that need to be validated before a full build. We run focused 2-4 week POCs with a defined success criterion. If the POC succeeds, we scope the production build. If it doesn't, you've spent a fraction of what a failed production build would cost.
Quality in production requires evaluation infrastructure -- not just good prompts. We build: evaluation datasets representing real query distribution, automated quality scoring using LLM-as-judge for qualitative outputs, regression testing to catch quality degradation when prompts or models change, production monitoring for output quality metrics over time, and human review queues for flagged outputs. Quality evaluation is not optional for production AI systems -- it's the only way to know if the system is working.
Costs range significantly by scope. An AI proof of concept runs $8,000--$20,000 for a 2-4 week focused investigation. A production AI feature integrated into an existing product runs $25,000--$75,000. A standalone AI application with RAG, evaluation, and monitoring runs $50,000--$150,000. A complex multi-agent system or custom ML pipeline runs $100,000--$300,000+. We provide fixed-cost proposals after a scoping session -- not hourly estimates that shift as scope changes.