AI software glossary
Large language models
Foundation technology
A large language model (LLM) is a neural network trained on vast text corpora to predict and generate human-like language. Modern LLMs such as GPT-class and open-weight alternatives power chatbots, summarisers, code assistants, and document analysis tools. Deploying an LLM inside your organisation requires careful evaluation of hosting costs, data residency obligations, and output verification workflows. We help clients select the right model tier — from lightweight on-device models to enterprise-grade API deployments — balancing accuracy, latency, and compliance.
Retrieval-augmented generation
Architecture pattern
RAG combines a retrieval system — typically a vector database — with a generative model so that responses are grounded in your proprietary documents rather than the model's training data alone. This dramatically reduces hallucination risk and keeps answers current. Our integration service connects your knowledge base (SharePoint, Confluence, internal wikis) to a retrieval layer, then orchestrates the generative step with citation tracking so every answer links back to its source.
"We went from 40% hallucination rates to under 3% after implementing the RAG pipeline AI Integrity Tech designed for our legal research platform."— K. Maguire, CTO, contract intelligence start-up
MLOps
Operational discipline
Machine learning operations (MLOps) is the set of practices that unify model development, deployment, monitoring, and retraining into a continuous lifecycle. Without MLOps, models degrade silently as data distributions shift. We implement monitoring dashboards, automated retraining triggers, and model registries so your AI software stays accurate months and years after launch — not just on demo day.
Algorithmic bias
Ethics and governance
Algorithmic bias occurs when an AI system produces systematically unfair outcomes for particular groups. Bias can enter through training data, feature selection, label design, or evaluation metrics. Our audit methodology tests models against multiple fairness criteria — demographic parity, equalised odds, and calibration — then provides remediation paths ranging from data re-sampling to post-processing adjustments. Early detection is far cheaper than post-deployment litigation.
Explainability
Trust and compliance
Explainability (sometimes called interpretability) refers to the degree to which a human can understand why an AI system produced a specific output. Regulatory frameworks including the EU AI Act increasingly require explanations for high-risk decisions. We integrate SHAP values, attention visualisations, and natural-language rationale layers into client systems so decision-makers — and regulators — can trace any prediction back to its contributing factors.
Federated learning
Privacy-preserving technique
Federated learning trains a shared model across multiple devices or institutions without centralising raw data. Each participant trains locally, shares only model updates, and the central server aggregates improvements. This is particularly valuable in healthcare, finance, and multi-branch retail where data cannot legally leave its jurisdiction. We architect federated pipelines that balance convergence speed with differential privacy guarantees.
Vector embeddings
Data representation
Vector embeddings convert text, images, or structured records into dense numerical arrays that capture semantic meaning. Two documents about the same topic will sit close together in embedding space even if they share no keywords. Embeddings underpin semantic search, recommendation engines, and RAG retrieval layers. We benchmark embedding models against your domain vocabulary to ensure similarity scores remain meaningful for your specific content.
Fine-tuning
Model customisation
Fine-tuning adapts a pre-trained model to your domain by continuing training on a curated dataset of examples. It is the bridge between a general-purpose model and one that speaks your industry's language with precision. We manage the full fine-tuning lifecycle: data curation, hyperparameter search, evaluation against held-out test sets, and deployment with version control so you can roll back if performance drifts.
AI guardrails
Safety layer
Guardrails are programmable rules and classifiers that sit between the user and the AI model, filtering harmful, off-topic, or non-compliant outputs before they reach the end user. Effective guardrails combine keyword filters, toxicity classifiers, topic boundary detectors, and output-format validators. We design layered guardrail stacks tailored to each client's risk profile, from customer-facing chatbots to internal research assistants.
Synthetic data
Data engineering
Synthetic data is artificially generated information that mirrors the statistical properties of real datasets without containing actual personal records. It accelerates model training when real data is scarce, sensitive, or expensive to label. Our synthetic data pipelines use conditional generation and privacy metrics to ensure the output is diverse enough for robust training yet distant enough from source records to satisfy data-protection requirements.
