What types of custom AI systems do you build?

We build across the main categories of applied AI: (1) LLM-powered systems -- chatbots, document Q&A, content generation, and knowledge retrieval using GPT-4, Claude, or open-source models with RAG. (2) Computer vision -- quality inspection, document OCR, object detection, and image classification trained on your specific images and defect types. (3) Predictive analytics -- demand forecasting, churn prediction, anomaly detection, and risk scoring trained on your historical data. (4) AI agents -- multi-step automated workflows that use AI to take actions in your systems. (5) Custom NLP -- entity extraction, document classification, and sentiment analysis for your domain.

What data do you need to build a custom AI system?

Data requirements depend on the type of AI system. For LLM-powered systems with RAG, we need your knowledge base, documents, or product data -- no training required for the model itself. For computer vision, we need labelled images of your specific product, defect types, or documents -- typically 500--5,000 images per class depending on difficulty. For predictive models, we need 12--24 months of historical data with the outcome you're trying to predict and the features that might predict it. We assess your data during scoping and design the technical approach based on what's available.

How do you ensure the AI system works for our specific use case?

We build evaluation frameworks specific to your use case before we start optimising. For a document extraction system, we define accuracy metrics against a set of real documents. For a computer vision system, we define precision and recall targets for your defect types. For a predictive model, we define the performance metric and acceptable error rate. We measure against these benchmarks throughout development and deliver a system that meets them, not one that performs well on generic benchmarks that don't reflect your data.

What does custom AI development cost?

A focused AI system -- one use case, one data type, integrated into one system -- typically runs $20,000--$60,000. Complex multi-modal systems, production AI pipelines with multiple models, or AI systems requiring significant data preparation run higher. Cost depends on the type of AI, the quality and quantity of training data, the integration complexity, and the performance requirements. We scope every project before pricing it and don't start development until cost and scope are agreed.

What types of custom AI systems do you build?

We build across the main categories of applied AI: (1) LLM-powered systems -- chatbots, document Q&A, content generation, and knowledge retrieval using GPT-4, Claude, or open-source models with RAG. (2) Computer vision -- quality inspection, document OCR, object detection, and image classification trained on your specific images and defect types. (3) Predictive analytics -- demand forecasting, churn prediction, anomaly detection, and risk scoring trained on your historical data. (4) AI agents -- multi-step automated workflows that use AI to take actions in your systems. (5) Custom NLP -- entity extraction, document classification, and sentiment analysis for your domain.

What data do you need to build a custom AI system?

Data requirements depend on the type of AI system. For LLM-powered systems with RAG, we need your knowledge base, documents, or product data -- no training required for the model itself. For computer vision, we need labelled images of your specific product, defect types, or documents -- typically 500--5,000 images per class depending on difficulty. For predictive models, we need 12--24 months of historical data with the outcome you're trying to predict and the features that might predict it. We assess your data during scoping and design the technical approach based on what's available.

How do you ensure the AI system works for our specific use case?

We build evaluation frameworks specific to your use case before we start optimising. For a document extraction system, we define accuracy metrics against a set of real documents. For a computer vision system, we define precision and recall targets for your defect types. For a predictive model, we define the performance metric and acceptable error rate. We measure against these benchmarks throughout development and deliver a system that meets them, not one that performs well on generic benchmarks that don't reflect your data.

What does custom AI development cost?

A focused AI system -- one use case, one data type, integrated into one system -- typically runs $20,000--$60,000. Complex multi-modal systems, production AI pipelines with multiple models, or AI systems requiring significant data preparation run higher. Cost depends on the type of AI, the quality and quantity of training data, the integration complexity, and the performance requirements. We scope every project before pricing it and don't start development until cost and scope are agreed.

Generic AI tools don't work for your specific data or workflow?
Need AI that understands your domain, your terminology, and your business context?

Custom AI Development

Off-the-shelf AI tools are built for generic workflows. Your business has specific data, specific processes, and specific requirements that generic tools don't address.
Custom AI development means building AI systems designed around your data and your workflows -- not adapting your business to what a SaaS product will do. We build custom AI solutions from LLM integration and RAG pipelines to computer vision systems and predictive models, deployed in your infrastructure with your ownership.

Custom LLM integration, RAG systems, AI agents, and computer vision built for your use case
20+ AI systems shipped across healthcare, fintech, manufacturing, and operations
All models trained or fine-tuned on your data -- not generic benchmarks
Full source code ownership, deployed in your infrastructure

RaftLabs builds custom AI development solutions including large language model integration, retrieval-augmented generation systems, computer vision for quality inspection and document processing, predictive analytics models, and AI agents for workflow automation. Unlike off-the-shelf tools, custom AI systems are trained or fine-tuned on your specific data and integrated with your existing infrastructure. We've shipped 20+ AI systems across healthcare, fintech, manufacturing, and operations. Most custom AI projects deliver in 10--16 weeks at a fixed cost.

Generic AI tools are built for the average use case. Yours isn't average.

An AI writing assistant built for marketing teams doesn't understand manufacturing defect codes. A fraud detection service built for e-commerce doesn't handle the transaction patterns of a B2B lending platform. A document extraction tool built for invoices doesn't work on your specific regulatory filings.

Custom AI means building the system around your data and your problem, not the other way around.

What we build

LLM integration and RAG systems

Large language model systems built around your specific data and use case -- document Q&A over your knowledge base, AI assistants that understand your product domain, content generation with your style and standards, and information extraction from your document types. GPT-4, Claude, Gemini, or open-source models -- selected based on your requirements, privacy constraints, and cost targets.

AI agents and workflow automation

Multi-step AI agents that use tools, retrieve information, and take actions in your systems -- automatically completing workflows that currently require human coordination. Claims processing agents, data enrichment pipelines, research assistants, and automated reporting systems. We've built AI document intelligence and business process automation systems that handle thousands of cases per day.

Computer vision systems

Vision AI trained on your specific images -- manufacturing defect detection on your product types, document classification for your document types, OCR for your specific form layouts, and object detection for your operational context. Models trained on your data, not generic datasets. We've built computer vision systems for gas station receipt processing, pharmaceutical packaging inspection, and retail shelf analysis.

Predictive analytics models

Machine learning models trained on your historical data to predict outcomes you care about -- demand forecasting, churn prediction, anomaly detection, risk scoring, and predictive maintenance. Models evaluated against your actual prediction task with the performance metrics that matter for your business decision, not generic ML benchmarks.

Custom NLP and text processing

Named entity recognition, document classification, sentiment analysis, and information extraction trained on your specific document types and domain vocabulary. Legal documents, medical records, financial filings, and technical documents have terminology and structure that generic NLP models handle poorly.

AI evaluation and monitoring

Production AI systems need measurement and monitoring. We build evaluation frameworks, accuracy dashboards, and drift detection for AI systems in production. You can't manage what you can't measure -- and AI systems degrade over time as data distributions shift.

20+ AI systems shipped. Custom AI built for your data, not generic benchmarks.

Fixed cost delivery. Full source code ownership. Deployed in your infrastructure.

How we approach custom AI

Data assessment and feasibility

Before scoping the system, we assess your data -- quality, quantity, labelling, and whether it's sufficient to train or fine-tune a model that will meet your performance requirements. If data is insufficient, we design a data collection strategy as part of the project. Honest assessment of what AI can and can't do with your current data.

Evaluation framework first

We define what "working" means for your specific use case before we start building -- the accuracy metrics, the error rate tolerance, and the edge cases that matter. We build against these targets, not generic benchmarks. You know what you're getting before you approve the cost.

Iterative model development

AI systems aren't built once. We train, evaluate, identify failure modes, improve the training data or model architecture, and retrain -- in iterations. Each iteration is measured against the evaluation framework. Development continues until the system meets the defined targets.

Production integration and deployment

The model alone isn't the product -- the integration into your existing workflow is. We build the API layer, the data pipeline, the user interface (if needed), and the deployment infrastructure. The AI system integrates into your workflow, not as a separate tool your team has to use separately.

Custom AI that works for your use case, not the use case the vendor imagined

We scope, build, and deploy AI systems around your data and your business problem.

Generative AI Development -- LLM-powered product development
Computer Vision Development -- vision AI for inspection and document processing
Predictive Analytics -- forecasting and risk models
AI Document Intelligence -- document processing and extraction
RAG Pipeline Development -- knowledge retrieval systems

Let's talk about your project

Tell us the use case, the data you have, and what the AI needs to do. We'll assess feasibility and give you a fixed cost.

Frequently asked questions

: We build across the main categories of applied AI: (1) LLM-powered systems -- chatbots, document Q&A, content generation, and knowledge retrieval using GPT-4, Claude, or open-source models with RAG. (2) Computer vision -- quality inspection, document OCR, object detection, and image classification trained on your specific images and defect types. (3) Predictive analytics -- demand forecasting, churn prediction, anomaly detection, and risk scoring trained on your historical data. (4) AI agents -- multi-step automated workflows that use AI to take actions in your systems. (5) Custom NLP -- entity extraction, document classification, and sentiment analysis for your domain.
: Data requirements depend on the type of AI system. For LLM-powered systems with RAG, we need your knowledge base, documents, or product data -- no training required for the model itself. For computer vision, we need labelled images of your specific product, defect types, or documents -- typically 500--5,000 images per class depending on difficulty. For predictive models, we need 12--24 months of historical data with the outcome you're trying to predict and the features that might predict it. We assess your data during scoping and design the technical approach based on what's available.
: We build evaluation frameworks specific to your use case before we start optimising. For a document extraction system, we define accuracy metrics against a set of real documents. For a computer vision system, we define precision and recall targets for your defect types. For a predictive model, we define the performance metric and acceptable error rate. We measure against these benchmarks throughout development and deliver a system that meets them, not one that performs well on generic benchmarks that don't reflect your data.
: A focused AI system -- one use case, one data type, integrated into one system -- typically runs $20,000--$60,000. Complex multi-modal systems, production AI pipelines with multiple models, or AI systems requiring significant data preparation run higher. Cost depends on the type of AI, the quality and quantity of training data, the integration complexity, and the performance requirements. We scope every project before pricing it and don't start development until cost and scope are agreed.