Most enterprise AI projects fail not because the AI doesn’t work — it usually does — but because the integration with existing systems wasn’t thought through before the proof-of-concept was built.
This guide covers the practical steps to add AI capabilities to software that already exists: what to assess first, which patterns to use, and what separates a production-ready AI integration from a demo that sits in a staging environment forever.
Start with the use case, not the model
The first mistake most teams make is choosing an AI model before defining what it needs to do. GPT-4o, Claude, Gemini, and open-source models like Llama are not interchangeable — they have different strengths, cost profiles, context limits, and data residency options.
Before picking a model, answer these questions:
- What does the AI need to know? General world knowledge, or your organisation’s specific documents, data, and processes?
- What does a correct output look like? How will you evaluate whether it worked?
- What happens when it’s wrong? Is a wrong answer annoying (low risk) or costly (high risk)?
- Where does your data live? Can it leave your infrastructure, or must it stay on-premises?
Once you can answer these, model selection becomes straightforward.
The four most common integration patterns
1. LLM-powered feature inside an existing application
You have a web application and want to add an AI-assisted feature — a writing assistant, a summarisation tool, a code reviewer, a customer support responder.
How it works: Your application sends a prompt to an LLM API (OpenAI, Anthropic, Google) and renders the response. The LLM has no memory of previous requests by default.
Good for: Features where the AI uses general knowledge or structured input from your application.
Watch out for: Prompt injection if users can influence the prompt directly, cost spikes from unbounded usage, latency if the response blocks the UI.
2. RAG — making AI answer questions about your own content
Retrieval-Augmented Generation (RAG) is the correct pattern when you want the AI to answer questions based on your organisation’s internal documents, knowledge base, policies, or product data — not just general training data.
How it works:
- Your documents are chunked and converted to vector embeddings (numerical representations)
- Stored in a vector database (Pinecone, pgvector, Weaviate, Qdrant)
- When a user asks a question, the most relevant chunks are retrieved
- The retrieved content is included in the LLM prompt alongside the question
- The LLM answers based on the retrieved content
Good for: Internal knowledge assistants, document Q&A, support automation grounded in real product information.
Watch out for: Chunk quality (garbage in, garbage out), retrieval relevance, hallucination when the answer isn’t in the documents.
3. AI agents for workflow automation
Agents go beyond single-turn Q&A — they can take actions, call tools, and complete multi-step tasks with or without human approval at each step.
How it works: The LLM is given a set of tools it can call (internal APIs, databases, external services). It decides which tool to call based on the user’s request, executes the tool, observes the result, and continues until the task is complete.
Good for: Automating repetitive internal workflows, processing structured requests that touch multiple systems, building internal copilots for complex tasks.
Watch out for: Unbounded tool calls (agents that do too much without human review), cost from multi-step reasoning loops, error handling when a tool fails mid-task.
4. Document intelligence and data extraction
Structured data extraction from unstructured documents — contracts, invoices, emails, PDFs, forms — using a combination of OCR and LLM-based parsing.
Good for: Automating data entry, contract review, invoice processing, classification of incoming documents.
Watch out for: Accuracy on low-quality scans, confidence scoring (the system should tell you when it’s unsure), human-in-the-loop for low-confidence extractions.
What makes an AI integration production-ready
Most proofs-of-concept fail to reach production because they skip the following:
Prompt versioning. Prompts are code. They need version control, testing, and a deployment process — not a string hardcoded in an API handler.
Output validation. LLM outputs must be validated before they’re acted on or displayed. Define what valid output looks like and reject or flag anything that doesn’t match.
Cost controls. Uncapped LLM usage at scale is expensive. Rate limiting, token budgets, and model tiering (use a cheaper model for simple tasks) are necessary from day one.
Observability. Log inputs, outputs, latency, and cost per request. You cannot debug an AI system you can’t observe.
Error handling. LLM APIs fail, time out, and return unexpected responses. Retry logic, fallback responses, and graceful degradation are not optional.
Access control. AI features that access sensitive data must respect your existing permission model. An AI assistant should not return data the user couldn’t access through the normal application.
Data residency and security
If your data cannot leave your infrastructure — because of regulatory requirements, client contracts, or data sensitivity — you have two options:
- Azure OpenAI Service or Vertex AI — cloud-hosted GPT/Gemini with regional data residency and no data used for training
- On-premises open-source models — Llama 3, Mistral, or Phi deployed via Ollama or vLLM on your own servers
On-premises models have higher setup cost and are less capable than frontier models, but they give you complete data control. For most regulated enterprise use cases, Azure OpenAI Service with a regional deployment is a better balance.
The practical starting point
If you have a specific AI use case and an existing platform, the fastest path to production is:
- Technical feasibility assessment (1–2 weeks): Can your current architecture support this? What data pipeline is needed? Which pattern fits?
- Prototype with evaluation (2–4 weeks): Build a working prototype with defined accuracy metrics. Evaluate against real data before committing to production build.
- Production build (4–8 weeks): Prompt versioning, observability, cost controls, access control, error handling, documentation.
Skipping step 1 and 2 is how teams end up with AI demos that never ship.
If you want an honest assessment of whether your use case is feasible and what it would take to build it properly, describe it to us — we’ll tell you what we think before any commitment is made.