What is RAG (Retrieval-Augmented Generation)?

RAG is a technique that lets an AI model answer questions using your proprietary data — documents, contracts, product catalogues — by retrieving the most relevant passages at query time and grounding the model's answer in them, instead of relying on what it learned during training.

How do you stop an AI system from hallucinating?

We combine retrieval grounding (so answers are based on real source documents), source citation, evaluation pipelines that measure hallucination rate before launch, and guardrails that catch low-confidence or out-of-scope responses before they reach a user.

Do you fine-tune AI models or only use APIs?

Both. We use foundation model APIs (Claude, GPT-4o) for most tasks, and fine-tune smaller models for specific, repeatable tasks like classification or extraction where a fine-tuned model is faster and cheaper at scale.

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CustomAISystems

Production AI is harder than most companies expect. The demo works. The product doesn't. We close that gap — building full-stack AI systems with evaluation frameworks, fine-tuned models, vector retrieval pipelines, and the observability to know when things go wrong before your users do.

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01 — What we build

Whatwebuild.

Systems that don't just use AI — they depend on it. Built to a production standard from day one.

RAG Pipelines

End-to-end retrieval-augmented generation: document ingestion, chunking strategy, embedding, vector storage, hybrid search, and a generation layer with source attribution.

RAGVector DBEmbeddings

Fine-tuned Models

Domain-specific fine-tuning on proprietary data — classification, extraction, summarisation, and generation tasks that base models can't handle reliably.

Fine-tuningLoRAQLoRA

AI-Powered Search

Semantic search layers over your product catalogue, documentation, or knowledge base — replacing keyword matching with meaning-aware retrieval.

Semantic SearchHybrid SearchReranking

Document Intelligence

Structured extraction from PDFs, invoices, contracts, and forms — turning unstructured documents into validated database records automatically.

OCRExtractionValidation

Recommendation Systems

Personalised recommendation engines that improve with every interaction — content recommendations, product suggestions, and next-best-action systems.

RecommendationsPersonalisationML

AI Evaluation Frameworks

Systematic evals that measure accuracy, hallucination rate, latency, and cost across model updates — so you can ship improvements with confidence.

EvalsLangSmithRegression Testing

02 — Infrastructure

Infrastructure.

AI at scale needs infrastructure that won't let you down at 3am.

Inference Optimisation

Prompt caching, batching, streaming, and model selection strategies that cut inference costs by 40–70% without sacrificing output quality.

Prompt CachingBatchingCost Optimisation

Vector Database Architecture

Choosing and scaling the right vector store — Pinecone, Weaviate, Qdrant, or pgvector — with proper index configuration and namespace design.

PineconeQdrantpgvector

AI Observability

Structured logging of every LLM call — inputs, outputs, latency, token usage, and cost — so you can debug, audit, and improve systematically.

LangSmithHeliconeOpenTelemetry

Model Governance

PII detection, output filtering, guardrails, and model versioning protocols that keep your AI system compliant and your users safe.

GuardrailsPIICompliance

Frequently asked

Let's talk about your project.

We'll ask the right questions, scope it honestly, and tell you exactly what it takes to hit your goal — before you commit to anything.

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We reply within one business day.