Build
Production-grade chat assistants, retrieval-augmented generation (RAG) and AI agents, integrated with your knowledge, tools and access controls.
Overview
Large language models are remarkably capable, but turning them into dependable products requires careful retrieval, evaluation, guardrails and cost control.
We design generative AI systems that cite their sources, respect permissions, stay within budget and improve over time. And we measure all of it.
Problems we solve
What’s included
User journeys, success criteria and a risk assessment for each assistant.
Document ingestion, chunking, embeddings and hybrid search tuned to your content.
LLMs that safely call your APIs, databases and business systems.
Automated test sets that score accuracy, groundedness and tone on every change.
Enterprise guardrail platforms such as Google Cloud Model Armor or Amazon Bedrock Guardrails, plus PII redaction, prompt-injection defences, content policies and human review.
The right model for each task, with fallbacks and cost monitoring.
Process
Phase 1
Pick one high-value workflow and define what “good” looks like.
Phase 2
A working assistant on your real content within weeks, not months.
Phase 3
Measure against a golden dataset; iterate until it clears the bar.
Phase 4
Staged rollout with feedback loops, analytics and support.
Technology
Models
Retrieval
Orchestration
Guardrails
Sample outcomes
Illustrative results from past engagements. Outcomes vary by context.
In practice
A regional health network
A privacy-first documentation assistant that drafts visit summaries and referral letters for clinician review across 14 outpatient clinics.
A mid-sized commercial law firm
A playbook-driven review assistant that flags deviations and suggests fallback language for lawyer approval.
FAQ
No. We configure enterprise API agreements or self-hosted models so that your data is not retained or used for training.
Retrieval with citations, constrained prompts, automated groundedness evaluation and clear “I don’t know” behaviour. We report hallucination rates as a tracked metric.
Whichever performs best on your evaluation set at an acceptable cost. Architectures are model-agnostic so you can switch as the market evolves.
Yes. Retrieval is filtered by the user’s identity and access rights from your existing systems.
Tell us what you want to achieve with generative AI & LLM integration. A senior consultant will reply within one business day.
Your privacy, your choice
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