Enterprise AI Platform Architecture

RAG Services

Vector, hybrid and graph retrieval over enterprise knowledge.

Fine-Tuned Models

LoRA and QLoRA adapters carrying domain-specific behaviour.

External Models

Commercial frontier models reached through the gateway.

Local Foundation Models

Enterprise models running inside your own boundary.

Open-weight families — Llama, Mistral, Gemma, Phi, DeepSeek, Qwen — deployed into infrastructure you control. That buys private deployment, materially lower per-token cost, the ability to operate offline, and a straightforward answer when a regulator asks where your data went.

Quantisation

Smaller weights, materially cheaper serving.

Reducing numeric precision shrinks a model's memory footprint and its GPU bill while holding accuracy inside an acceptable band. We benchmark the trade-off on your workload rather than assuming it: INT8 for production serving, INT4 where the target is an edge or embedded device.

Model Distillation

Teacher-to-student knowledge transfer.

A large teacher model supervises the training of a much smaller student, which then handles the narrow task at a fraction of the cost and latency. It works well for customer support, knowledge assistants, process automation and virtual agents — places where the job is bounded and the volume is high.

Fine-Tuning

Domain adaptation without retraining from scratch.

LoRA trains a small set of adapter weights over a frozen base model, keeping the training footprint and GPU spend down. QLoRA goes further, quantising the base model during training so large models can be adapted on modest hardware. Both are appropriate for domain adaptation, content generation and classification.

Retrieval-Augmented Generation

Grounding answers in your own content.

A query is embedded, matched against your indexed corpus, and the retrieved passages are folded into the prompt before the model answers. Sources include SharePoint, Confluence, ServiceNow, Salesforce, relational databases, PDF archives, internal APIs and public sites. The payoff is current information, fewer hallucinations, and a citation trail behind every answer.

Advanced Retrieval

When plain vector search is not enough.

Hybrid search combines semantic, keyword and metadata matching so exact identifiers are not lost to fuzzy similarity. Graph RAG adds knowledge graphs and entity relationships for questions that span records. Agentic RAG lets the system retrieve in several passes, reason over what it found, and synthesise across systems.

Context & Attention Optimisation

Long documents without runaway token bills.

Context compression cuts tokens, latency and cost before a request is ever sent. Attention tuning improves how reliably a model prioritises the relevant passage inside a long input. Together they make multi-document analysis, legal review and large-corpus discovery practical rather than merely possible.

Quality Assurance & Validation

Continuous scoring of what the system actually says.

Factual validation checks correctness and source grounding. Hallucination detection flags unsupported claims and fabricated references. Consistency testing measures whether the same question reliably produces the same answer. Reported as groundedness, answer relevance, faithfulness, precision, recall, task completion rate, latency and cost.

Agentic AI

Collaborating agents rather than one overloaded prompt.

Planner, research, validation and execution agents each hold their own tools and their own clean context, passing work between them. This underpins workflow automation, decision support and knowledge retrieval, with human approval gates wherever the blast radius warrants one.

Reliability & Resilience

No single provider is a single point of failure.

Requests route to a primary model with ordered fallbacks behind it, ending at a locally hosted model that stays available regardless of what any vendor's status page says. Azure OpenAI, OpenAI, Anthropic, Google Gemini and local models sit behind one interface, so provider choice remains a commercial decision.

MLOps & LLMOps

Lifecycle automation across data, models and deployment.

Data versioning, dataset governance and feature management underneath; experiment tracking, a model registry and version control in the middle; CI/CD pipelines with canary rollout on top. Drift detection, performance monitoring and cost tracking watch the whole thing in production.

Continuous Evaluation

Evidence for the decision to retrain.

Production prompts feed an evaluation dataset, which drives benchmark runs and quality scoring, which in turn informs whether a retrain is justified. Data drift, concept drift, prompt drift and knowledge drift are each monitored separately, because they have different causes and different remedies.

Security & Governance

The layer that makes AI defensible to a risk committee.

Role-based access control, encryption at rest and in transit, tenant isolation and audit logging as the baseline. On top of that, bias monitoring, explainability, compliance reporting and defined points of human oversight.