Production-Ready AI Systems Built for Your Business Goals
We engineer end-to-end AI solutions — from LLM integration and RAG pipelines to custom model training and MLOps. GPT, Claude, open-source, all stacks.
Full-stack AI engineering
LLMs, RAG, ML, MLOps
100%
code & IP yours from day one
6–10 wks
48h
Avg. Response Time
no surprises, ever
What is AI development?
AI development is the design, engineering, and deployment of artificial intelligence systems — including large language model (LLM) applications, retrieval-augmented generation (RAG) pipelines, autonomous AI agents, computer vision systems, and machine learning models — that automate decisions, generate content, or extract insights from data. CodeShiper builds these systems end to end: problem framing, architecture, data pipeline, model integration, application layer, deployment, and ongoing tuning.
What we build, end to end
Every AI engagement we run covers the full stack — from model selection and data pipelines to the application layer and production infrastructure.
LLM Application Development
Custom applications powered by OpenAI, Anthropic, or open-source language models. Chat interfaces, document Q&A, content generation, classification, and summarization — built around your data and deployed in your infrastructure.
RAG System Development
Retrieval-Augmented Generation pipelines that let LLMs answer questions about your proprietary documents, databases, and knowledge bases. Chunking strategy, vector storage, retrieval tuning, and reranking included.
AI Agent Development
Autonomous agents that take multi-step actions — querying APIs, browsing the web, writing and executing code, managing files. Orchestrated with LangChain, LlamaIndex, or custom frameworks depending on reliability requirements.
Computer Vision Systems
Object detection, image classification, OCR, face recognition, and video analysis pipelines. From training on your labeled data to production deployment on edge devices or cloud inference endpoints.
AI Integration & Embedding
Embed AI capabilities into your existing product — recommendation systems, anomaly detection, intelligent search, predictive scoring — without rebuilding your stack. API-first, event-driven architectures.
ML Model Development & Fine-tuning
Custom ML models trained on your data. Classification, regression, time-series forecasting, and NLP models. Fine-tuning of foundation models when prompt engineering plus RAG is not enough.
The AI stack we use in production
We pick tools that are production-ready and fit your latency, cost, privacy, and performance requirements — not just whatever is trending.
From problem statement to production AI
Six stages with full visibility. You see working AI at every milestone — not a slide deck about what we plan to build.
Problem Framing & Scoping
1–2 wksDefine the problem in measurable terms, audit your data, identify the right AI approach, and produce a written scope with architecture recommendation and line-item cost estimate.
Data Pipeline & Evaluation Setup
1–3 wksBuild the data pipeline, create the evaluation test set, and establish baseline quality metrics. Evaluation infrastructure is ready before we generate a single production output.
Model Integration & Prototyping
2–4 wksWorking prototype connected to the model and your data. Prompt engineering, retrieval tuning, and system prompt optimization. You test the AI behavior against real use cases.
Application Development
SprintsFull application layer — backend API, frontend UI, authentication, admin controls, usage logging, and integrations. You review working software at each sprint milestone.
Evaluation & Security Testing
1–2 wksAutomated evaluation suite, hallucination rate measurement, latency profiling, edge case testing, and security review. Performance is measured and reported before launch.
Launch & Ongoing Tuning
OngoingProduction deployment, monitoring setup, and post-launch tuning. AI systems need ongoing attention — retrieval quality, model updates, and new edge cases. Support available on retainer.
AI development cost & timeline
Cost depends on the type of AI system, data complexity, model choice, and application scope.
| Project type | Example scope | Timeline | Indicative cost |
|---|---|---|---|
AI feature integration Embedded into existing product | RAG chatbot, semantic search, or classification added to existing app | 4–8 weeks | $15,000–$40,000 |
AI-powered application Standalone AI product | LLM app with custom data pipeline, backend API, frontend UI, auth | 2–4 months | $40,000–$100,000 |
Enterprise AI platform Multi-model, fine-tuning | Multiple AI capabilities, evaluation infrastructure, fine-tuned models, scale | 4–9 months | $100,000+ |
Final pricing follows a free scoping call with a line-item breakdown. Data volume, model choice, and infrastructure requirements are the primary cost drivers.
What makes our AI work in production
We do not compete on buzzwords. Here is what clients actually get — verifiable and specific.
Senior AI engineers throughout
The engineers who scope the project are the same ones who build it. No handoff to a junior team after the contract is signed.
Evaluation-first approach
We define how we will measure AI quality before we build anything. Hallucination rate, retrieval precision, latency — all measured and reported, not guessed.
Working AI at every milestone
You test real AI behavior against real use cases at each sprint. Issues surface in week two, not at final delivery.
Line-item estimates, not ranges
You get a written scope with a cost breakdown before you sign. If scope changes, we tell you in writing before the extra work begins.
Production-grade, not demo-grade
We design for latency, reliability, fallback behavior, and cost at scale — not just for impressive demos that fall apart under real load.
Post-launch AI support
Model updates, retrieval tuning, prompt optimization, and new feature development. AI systems need ongoing attention, and we provide it.
Frequently asked questions
AI development — costs, timelines, models, data privacy, and everything else clients ask before they sign.
What is custom AI development?
How much does AI development cost?
How long does an AI development project take?
Which AI models do you work with?
What is RAG and do I need it?
Can you build AI agents?
Do you do fine-tuning?
How do you handle data privacy and security?
Who owns the code and models after delivery?
Let's Talk
Got an AI project in mind? Let's figure out if we are the right fit.
We will ask about your use case, review your data situation, and give you an honest estimate with a line-item breakdown. No commitment needed for the first conversation.