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AI & Automation

Add AI to Your Product Without Starting Over

Integrate Claude or GPT into your product — summarisation, classification, RAG pipelines, and content generation. Practical AI built for production, not demos.

Timeline 2–6 weeks
Investment $2,500–$10,000
Claude APIOpenAI APIPythonDjangoLangChainPostgreSQLCloudflare Workers
🔗
API integration

Clean integration layer between your app and the LLM — retries, error handling, and cost controls.

📝
Prompt engineering

Structured prompts with few-shot examples, output format constraints, and quality validation.

🔍
RAG pipeline

Retrieval-augmented generation — AI that answers based on your specific data, not just training data.

💰
Cost optimisation

Caching, batching, and model selection strategy to keep API costs predictable and low.

01
Use case definition

Identify exactly what the AI needs to do, what inputs it receives, and what outputs are acceptable.

02
Prompt development

Iterative prompt engineering with test cases. System prompts, few-shot examples, and output validation.

03
Integration build

API client with retry logic, rate limiting, error handling, and response caching where appropriate.

04
Evaluation & monitoring

Quality evaluation pipeline and cost monitoring. You'll know when output quality drops or costs spike.

Case Study
Language Factory — AI Content Pipeline

Python pipeline generating A1 language learning content across 7+ languages using Claude API — two-stage LLM translation with retry logic, morphology validation, batch semantic validation via OpenAI, and TTS audio generation.

7 languages · 1000+ content items · Automated quality validation
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Claude for complex reasoning and instruction-following. GPT-4o for multimodal tasks. Gemini for Google ecosystem integration. Smaller models (GPT-3.5, Claude Haiku) for high-volume, simpler tasks where cost matters.
RAG (Retrieval-Augmented Generation) lets the AI answer questions based on your private data — documentation, product catalogue, customer records. You need it when the AI needs to know things that aren't in its training data.
System prompts, constrained output formats (JSON schema), and validation layers. LLMs can be reliably constrained — it just requires careful prompt engineering.
Hallucinations are reduced by RAG (AI cites sources), constrained output formats, and validation steps that check outputs before showing them to users. They can't be eliminated, but they can be managed.
Yes — that's my primary stack. Adding an LLM integration to a Django app is straightforward and clean.
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