Applied Artificial Intelligence in our projects
We integrate AI where it adds real value: automating repetitive processes, extracting information from documents, generating contextualised content and intelligent user assistance. We make implementation decisions based on ROI, not trends.
LLM integration — Claude API and OpenAI
We use Claude (Anthropic) as the primary model for complex reasoning tasks, document analysis and long-form content generation — the 200k token context window allows processing an entire legal file or financial report in a single call. GPT-4o is chosen for multimodal tasks (image + text) and when the OpenAI ecosystem (Whisper, DALL-E) is needed.
Tool use (function calling) allows the LLM to interact with internal APIs: search the database, trigger actions in existing systems, return structured data validated with Pydantic. Streaming responses reduce perceived latency for conversational interfaces — the user sees text appearing progressively, not waiting 5-10 seconds.
- Prompt caching (Anthropic) reduces cost by 90% for repetitive context (system prompts, reference documents)
- Structured outputs (JSON mode) for type-safe responses without regex on free text
- Automatic Claude → GPT-4o → Gemini fallback on availability errors
- Per-user and per-feature cost tracking — LLM usage visible in admin dashboard
RAG — AI with knowledge from client data
Retrieval-Augmented Generation (RAG) allows the LLM to respond based on client-specific documents (contracts, manuals, legislation, emails) without retraining. Documents are chunked, embedded with text-embedding-3-large (OpenAI) or embed-english-v3 (Cohere), stored in pgvector (PostgreSQL extension) and retrieved via cosine similarity.
The RAG pipeline includes: document ingestion (PDF, DOCX, HTML), intelligent chunking (with overlap for context), re-ranking with Cohere Rerank for increased relevance, injection into LLM context and source citation with page numbers. The result is a chatbot that responds accurately based on real documents with verifiable sources.
- pgvector for vector similarity search directly in PostgreSQL — no Pinecone/Weaviate
- Hybrid search: dense vectors + sparse BM25 (keyword) → RRF fusion
- RAG evaluation with RAGAS — precision, recall, faithfulness measured automatically
- Metadata filtering for multi-tenant: user A does not see user B's documents
Computer Vision and document processing
For extracting data from scanned or photographed documents (invoices, contracts, IDs) we use a combined pipeline: image pre-processing (deskew, noise reduction with OpenCV), OCR with Google Document AI or Amazon Textract (structured, not raw OCR), post-processing with LLM for normalisation and validation. >98% accuracy on typical business documents.
Image classification (products, categories, NSFW detection) is done with fine-tuned models on CLIP or EfficientNet, deployed as FastAPI microservices with optional GPU inference (CUDA). For projects without GPU budget, cloud APIs (Google Vision, AWS Rekognition) offer 95%+ accuracy at marginal cost.
- Product description generation from specs — hundreds of products, consistent brand tone
- Automatic product categorisation at catalog import
- Review and user-generated content moderation with cloud APIs + LLM fallback
- Automatic data extraction from PDF/image supplier invoices → updated stock
Pros and cons
Pros
- Automates repetitive processes: data extraction, classification, support.
- RAG answers from client documents, with verifiable sources.
- Prompt caching cuts cost by up to 90% on repetitive context.
- Model fallback (Claude → GPT-4o → Gemini) for availability.
Limitations
- LLMs can hallucinate — any critical output needs validation and sources.
- Per-token cost grows fast at high volume without careful caching.
- Sensitive data sent to third-party APIs raises privacy concerns.
- Latency of a generated answer can reach several seconds.
Quick verdict
Integrate AI where it adds real ROI: automation, document RAG, data extraction. Avoid AI for AI's sake on tasks where a simple rule or classic search is enough.
Specifications table
| Capability | Limit / threshold | Typical use | |
|---|---|---|---|
| LLM context | Up to 200k tokens (Claude) | Large context raises cost and latency | Long-document analysis, reports |
| RAG | pgvector, hybrid search, re-ranking | Quality depends on chunking and sources | Chatbot over own documents |
| Computer vision | Structured OCR, > 98% accuracy | GPU inference increases cost | Invoice extraction, ID verification |
| Typical cost | Billed per token / per API call | Grows with volume without caching | From chatbot to full pipeline |
RAG vs fine-tuning
| Criterion | RAG | Fine-tuning |
|---|---|---|
| Knowledge update | Instant (add documents) | Requires retraining |
| Verifiable sources | Yes, with citation | No |
| Upfront cost | Low | High (data + GPU) |
Frequently asked questions
What is RAG and when do I use it?
RAG (Retrieval-Augmented Generation) lets the AI answer from your documents (contracts, manuals, legislation) without retraining. Use it when you want accurate, sourced answers based on your own data.
RAG or fine-tuning?
RAG for frequently-changing knowledge and verifiable sources — you update it by adding documents. Fine-tuning for fixed style or format, with higher upfront cost. For most business cases, RAG is the choice.
How do you control AI hallucinations?
We use RAG with source citation, structured outputs (JSON) validated with Pydantic and human validation for critical output. The AI answers from real documents, not from memory, and every claim has an attached source.
Does my data stay confidential?
Sensitive data can be isolated per tenant and, if needed, processed with self-hosted models or APIs with no-training clauses. For regulated cases, we pick providers that do not use data for training.