A complete technical reference for architects and engineers — covering every AI model, agentic framework, ML algorithm, document intelligence capability, security control, and infrastructure component in the Trinitiai platform.
Trinitiai abstracts all LLM and SLM providers behind a unified interface. Models are selected, swapped, and routed via YAML configuration — no code changes required. This is the multi-LLM architecture that eliminates vendor lock-in permanently.
Each model above is a third-party provider integrated through Trinitiai's unified LLM interface. The platform routes to the optimal model per request based on cost, latency, and task complexity — all configured in YAML. Trinitiai does not own or host these models; it orchestrates them.
Triverge is Trinitiai's proprietary multi-framework agentic orchestration engine. It is the only solution that unifies CrewAI, AutoGen, and LangGraph in a single YAML-configurable layer — enabling enterprises to leverage the best framework for each use case without architectural fragmentation.
A complete library of supervised and unsupervised machine learning models — all production-tested, all deployable via the platform's YAML configuration system. Each model is supported with explainability frameworks to ensure transparent, auditable AI decision-making.
Extreme Gradient Boosting — the most widely deployed predictive model in the platform. High accuracy on tabular operational data with fast inference.
Categorical feature-native gradient boosting. Particularly effective on mixed-type enterprise data without extensive feature engineering.
Robust ensemble classifier with strong performance on high-dimensional operational datasets. Resistant to overfitting and interpretable feature importance.
Fast, interpretable binary and multi-class classification. Used where explainability is paramount and audit requirements demand transparent model logic.
K-Nearest Neighbours classification. Effective for similarity-based routing decisions where the k most similar historical cases inform the recommendation.
SVM with kernel functions for high-dimensional classification. Particularly effective for text classification and complex non-linear decision boundaries.
Unsupervised clustering for grouping operational patterns without labelled data. Core to the Alert Deduplication solution for collapsing signal noise into root causes.
GNNs for analysing relational datasets and network-level interactions. Detects patterns in interconnected operational systems that tabular models cannot surface.
Provides unified feature importance scores grounded in game theory. Every model prediction comes with an explanation of which features drove the decision and by how much. Mandatory for audit-grade AI systems in regulated environments. Applied to XGBoost, CatBoost, and Random Forest deployments.
Generates local explanations for any individual prediction by approximating the model with an interpretable surrogate around that specific data point. Complements SHAP with human-readable case-by-case explanations. Used for complex ML models where global feature importance alone is insufficient for governance requirements.
Beyond standard LLM and ML capabilities, the platform includes specialised intelligence modules for document processing and network-level relationship analysis — enabling AI to work with the full range of enterprise data sources.
The platform extracts structured information from unstructured documents — PDFs, scanned images, screenshots, and attachments — using two state-of-the-art OCR and vision models. This is core to the DealWeaver contract automation solution and any workflow requiring enterprise document ingestion.
Graph Neural Networks analyse relationships between entities in complex interconnected systems — going beyond what tabular ML models can detect. In enterprise operations, this means understanding how infrastructure components, teams, and services relate to each other and how failures propagate through networks.
Security is not a feature added on top — it is embedded into the platform architecture from the control plane upward. Every deployment meets enterprise security and compliance requirements without additional configuration.
Every pull request on the Trinitiai platform must satisfy all 20 engineering standards before merge. These rules are mandatory — not guidelines. They are specifically designed for AI engineering teams building production LLM, Agentic, and ML systems.
Every pull request must satisfy all 20 rules before merge. Mandatory · V2.0 AI Engineering Edition.