AI & Intelligent
Applications

Deploy grounded AI assistants, retrieval-augmented generation pipelines and intelligent automation grounded strictly in verified institutional knowledge.

Grounded AI RAG Pipelines LLM Systems Knowledge Assistants Agent Automation
VERIFIABLE INTELLIGENCE
Grounded reasoning.
Deterministic trust.
GROUNDING
Zero hallucination
KNOWLEDGE
Authoritative data
DEPLOYMENT
Production ready
OVERVIEW

Architecting enterprise AI that operates
with verifiable truth.

Most AI initiatives run into trouble because they treat a generative model like a decision-maker instead of what it actually is — an assistive engine. Hallucinated answers, no source citations, data pipelines nobody's verified, and no operational guardrails: put those together and a bare LLM wrapper simply isn't fit for critical business or engineering workflows.

I build grounded AI systems where every answer the model gives traces back to an actual source document, not a guess dressed up as fact. That means engineering solid Retrieval-Augmented Generation (RAG) pipelines, deterministic state machines, and evaluation frameworks that keep checking the system's work — so the AI delivers real business value instead of hallucination risk and compliance headaches.

An internal knowledge assistant for your engineering team, an automated document analysis engine, or secure agentic workflows wired into your internal databases — in each case I bring the same engineering discipline, all the way from model evaluation through to production deployment.

WHAT'S INCLUDED

Scope of this service

What's involved depends on where your organisation is starting from and how strict your security requirements are, but engagements in Grounded AI & Intelligent Applications typically draw on some combination of the following:

RAG & Vector Knowledge Base Architecture Working out how documents get chunked, embedded and indexed, then building multi-stage retrieval and hybrid keyword-plus-vector search that stays anchored to your authoritative source material.
Deterministic AI Agents & Tool-Calling Building agents that operate inside clear boundaries — strict state transitions, secure calls to your internal APIs, and a human in the loop wherever a decision actually matters.
LLM Evaluation, Guardrails & Hallucination Defense Setting up automated evaluation benchmarks, defenses against prompt injection, PII filtering, and ongoing checks of outputs against ground-truth data so drift gets caught early rather than in production.
Enterprise Data Governance & Isolation Locking down role-based access control, clean multi-tenant data boundaries, zero-data-retention model configurations, and audit trails detailed enough to satisfy whoever's checking your compliance.
Production Deployment & Telemetry Deploy high-throughput inference endpoints with latency caching, token expenditure controls, failure fallback routing, and observability dashboards.
OUTCOMES

What you can expect
to achieve.

I scope every engagement against outcomes that can actually be measured. Here's what clients tend to walk away with:

Fewer Hallucinated Answers Grounding every response in verified source documents cuts down sharply on confidently wrong answers reaching your users.
Faster Path to a Trustworthy Deployment Structured evaluation and guardrails mean you can ship an assistant your team is actually willing to rely on — not just something that looked good in the demo.
Lower Compliance Risk Access controls, audit trails and data isolation built in from the start keep the AI system inside your regulatory boundaries instead of testing them.
Predictable Operating Costs Caching, token controls and fallback routing stop inference spend from quietly spiraling as usage grows.
TYPICAL ENGAGEMENTS

When organisations
call on this service.

This service is engaged across a range of contexts. Some of the most common scenarios include:

01

Internal Knowledge Assistant

Your team burns real hours hunting through scattered docs, wikis and old tickets for answers that already exist somewhere. A grounded assistant that retrieves the actual source and cites it, instead of guessing at a plausible-sounding answer.

02

Legacy System Modernisation

An existing system is showing its age — slow, fragile, difficult to change. Structured assessment, migration planning and controlled modernisation to a sustainable architecture.

03

Agentic Workflow Automation

A repetitive, multi-step process touching several internal systems is eating up staff time. Bounded agents that carry out the steps reliably, with a human checkpoint wherever the stakes are high enough to warrant one.

04

AI System Audit & Hardening

An AI deployment is already live, but nobody's fully confident in what it might say next. Independent evaluation, hallucination testing and guardrail hardening before a bad answer becomes a real problem.