AI Development Services — LLM, RAG & Agentic AI that ships.

AI development is the engineering process of designing, building, deploying, and maintaining systems that use machine learning, large language models, or statistical models to automate decisions, generate content, extract information, or assist users. It covers five disciplines: data engineering (collecting, cleaning, and structuring data), model selection (choosing the right LLM, ML model, or architecture), application development (the API, frontend, and integration layer), evaluation (measuring accuracy, relevance, and safety), and MLOps (monitoring model performance in production to detect drift).

AI development in 2026 primarily means LLM-based systems — applications built on foundation models like GPT-4o, Claude 3.5, Gemini, or Llama 3 — rather than training models from scratch. Building on foundation models reduces development time by 6–18 months versus custom training, while delivering capabilities that took years to achieve with traditional ML. The engineering work focuses on integration, RAG pipelines, agent orchestration, prompt engineering, and production reliability — not model architecture research.

RAG is an AI architecture that retrieves relevant information from a knowledge source at query time and passes it as context to a large language model, enabling answers grounded in specific private or up-to-date data rather than the model's training knowledge alone.

A RAG pipeline runs in four steps. First, the user query is converted into a vector embedding — a mathematical representation of semantic meaning. Second, that embedding searches a vector database (Pinecone, pgvector, Weaviate) for the most semantically similar chunks from your indexed documents. Third, the top-k (typically 3–10) retrieved chunks are passed as context alongside the original query. Fourth, the LLM generates a response grounded in the retrieved context rather than its training data.

Production RAG adds three enhancements: hybrid search (combining dense vector search with sparse keyword search like BM25 for exact-match queries), reranking (a second model scores retrieved chunks for relevance, improving precision), and hallucination detection (a verification step that checks whether the generated answer is supported by the retrieved context before returning it).

Agentic AI refers to systems that autonomously execute multi-step tasks by deciding which tools to call, in what sequence, and how to handle intermediate results — rather than simply responding to a single query. A chatbot responds to input; an AI agent completes a workflow.

A support chatbot answers "What is your return policy?" by retrieving a document. An agent for the same domain receives "Process this return for order #8821" and autonomously looks up the order, verifies it's within the return window, initiates a refund via the payments API, sends a confirmation email, and updates the CRM — completing the workflow without human intervention.

Agentic systems require four components beyond basic LLM integration: tool definitions (structured descriptions of APIs and functions the agent can call), planning logic (how the LLM decides which tools to use — via ReAct, LangGraph, or AutoGen), error handling (retry logic, fallbacks, and human escalation), and AgentOps monitoring (observability into decisions, tool calls, costs, and failure rates). 96% of IT leaders view agents as a security risk — guardrails, permission scoping, and audit logging are required before production.

MLOps (Machine Learning Operations) is the engineering discipline that keeps AI models reliable, accurate, and cost-effective after they deploy. Without it, AI systems degrade silently: accuracy drops as real-world data shifts from training data (model drift), costs spike from token overuse, latency rises with volume, and hallucination rates climb as prompts meet new data patterns.

A complete LLMOps stack covers six areas: latency monitoring (p50, p95, p99 tracked continuously), cost-per-query tracking (token usage and API spend per endpoint), quality evaluation (automated relevance scoring and human-eval sampling), prompt versioning (prompts as code with A/B testing and rollback), drift detection (statistical tests that flag output divergence from baseline), and automated retraining triggers (pipelines that update fine-tuning or RAG indexes when quality falls below thresholds).

A focused AI feature — LLM integration with RAG pipeline, API, and basic monitoring — costs roughly $15,000–$50,000 and takes 6–10 weeks. A production agent system with tool use, full LLMOps monitoring, and enterprise guardrails costs $50,000–$150,000 and takes 12–20 weeks. A custom fine-tuned model with a training-data pipeline costs $30,000–$100,000 and takes 8–14 weeks depending on data readiness.

Cost drivers differ from standard software: data quality and volume (poorly structured data multiplies engineering time 2–3×), evaluation rigour (meaningful evaluation needs 200–500 labelled test cases), LLM API cost (GPT-4o at ~$15/million tokens versus self-hosted Llama 3 at ~$0.50/million — a 30× difference at scale), and MLOps infrastructure (often 20–30% of total cost but required for reliability). We provide phased cost breakdowns after a discovery session, with the PoC priced separately so you can validate before the full investment.