Artificial Intelligence Engineering
We engineer reliable, secure, and production-tested AI applications. From grounded retrieval-augmented generation (RAG) to multi-agent workflow automation and adversarial injection controls, we build AI that works within real enterprise boundaries.
AI Engineering & Risk Controls
Moving beyond toy wrappers into resilient architectures, deterministic tool-calling, and verified data confidentiality.
RAG System Architecture
Engineering production RAG pipelines with semantic chunking, reciprocal rank fusion (RRF), re-ranking models, and strict citation grounding to prevent hallucinations.
- • Hybrid keyword + dense vector search
- • Chunk-boundary context preservation
- • Source attribution verification
AI Security & Risk Controls
Protecting systems against direct and indirect prompt injections, jailbreak vectors, unauthorized tool execution, and sensitive data leakage to model providers.
- • Indirect prompt injection containment
- • PII scrubbing & token anonymisation
- • Tool execution confirmation boundaries
Agent Workflow Engineering
Designing structured agentic workflows using Python, n8n, and strict JSON schemas, with deterministic error-recovery loops and finite state machines.
- • Finite state machine workflow control
- • Schema-enforced tool calling
- • Rate limiting & fallback routing
Data Ingestion & Embedding Pipelines
High-throughput document extraction, OCR sanitisation, incremental embedding synchronization, and vector index maintenance at scale.
- • Incremental vector updates
- • Unstructured PDF/Word document normalization
- • Multi-tenant index isolation
Proof-of-Concept Development
Rapidly developing working prototypes to test whether a generative AI capability is technologically and economically feasible before commercial rollout.
- • Real-world token cost modeling
- • Latency benchmarking
- • Accuracy evaluation sets
LLM Evaluation & Guardrails
Implementing automated regression evaluation harnesses, drift monitors, and runtime input/output validators to detect degradation in model quality.
- • Ground-truth evaluation suites
- • Output structural validators
- • Latency and token tracking
[User Query / External Trigger]
│
├── Input Sanitiser & Prompt Injection Guard
▼
[Hybrid Retrieval Engine]
├── Vector Search (Dense Embeddings)
├── Keyword BM25 (Exact Match)
▼
[Re-Ranking & Citation Filter] ──► [Context Envelope (Zero-PII Tokenised)]
│
▼
[LLM Processing Layer]
│
├── Schema Enforcement Gate (JSON Schema Validated)
▼
[Output Validator] ──► [Audited Action Execution / Client Response]
Concerned About AI Automation & Agent Risks?
Our fixed-price AI Automation Risk Audit reviews your automated pipelines, prompts, and tool permissions for £495.