Generative AI and LLM Consulting

Overview

Generative AI creates new content — text, images, audio, code — by learning patterns from large datasets, rather than retrieving or classifying existing information. Large Language Models (LLMs) are the technology behind most enterprise generative AI, increasingly capable of reasoning through multi-step problems and using tools. Avocado builds generative AI for organisations automating content creation, analysing complex documents, or replacing transactional conversations — grounded in your own data, not a generic public chatbot.

Who is this for?

You'll recognise the situation

Content creation, document review or customer conversations consume hours of skilled staff time on repetitive work.

You need to analyse large volumes of contracts, reports or documents faster and more consistently than manual review allows.

Your customer service or booking process still relies on manual phone or chat handling at peak times.

Your customers or staff fill out manual forms and potentially make errors.

You're not sure which model — GPT, Claude, Gemini or Llama — fits your use case, data sensitivity or stack.

Which AI model is right for us?

Model-agnostic by design — we help you choose

GPT (OpenAI)

Strong for long-horizon agentic tasks, coding and general-purpose use; the default most people already know.

Claude (Anthropic)

Favoured for long-context document analysis, agentic coding, and use cases where output safety and reasoning transparency matter.

Gemini (Google)

Natively multimodal with very large context windows; a strong fit if you're standardised on Google Workspace or Cloud.

Llama (Meta)

Open-weight and self-hostable — the right choice when data sovereignty, cost at scale or on-premise deployment are hard requirements.

What's included?

Explore what's possible

Retrieval-Augmented Generation (RAG)

Grounds output in your own documents and systems at inference time, with citations back to source — the biggest single reducer of hallucination.

Graph RAG

Knowledge graphs incorporated into retrieval for questions that span multiple documents or follow relationships between entities.

Chat with structured data

Ask financials, sales and reports in plain language and get answers and charts — the AI writes a query and works from the result, scoped to what each user may see.

Document & contract analysis

Extract clauses, dates, obligations and financial terms automatically, with proactive risk and compliance flagging.

Content creation & repurposing

Synthesise documents, links and recordings into long-form content and multi-format assets, tuned to your brand voice.

Conversational & voice AI

Voice and chat assistants that handle bookings, FAQs and transactional conversations, escalating to a human when needed.

How do we deliver it?

Discover, design, build, guardrail, deploy

Discover

Understand the specific task, the data it depends on and what a good outcome looks like.

Select & design

Choose the right model and architecture (RAG, Graph RAG or bespoke) based on the task and data sensitivity.

Build

Build the integration, grounding the model in your governed data rather than raw sources.

Test & guardrail

Test against real scenarios, including edge cases and adversarial prompts, before go-live.

Deploy & monitor

Roll out with monitoring, since model versions and behaviour change over time.

What tools and technology do we use?

The stack behind it

GPT (OpenAI), Claude (Anthropic), Gemini (Google) and Llama (Meta) as the underlying models, selected per use case; retrieval-augmented generation and Graph RAG for grounding; vector databases for retrieval. The model landscape moves quickly — we keep your architecture current rather than locked to one vendor.

What you walk away with

Manual content, document review and conversational workload shifted to AI, freeing skilled staff for higher-value work.

Faster, more consistent document and contract analysis with proactively flagged risks and obligations.

24/7 conversational coverage for bookings and routine enquiries without proportionally scaling headcount.

Service costs can fall 25% to 95% when chatbot deployments are done well.

How is this engagement structured?

Scoped individually depending on the use case, model choice and integration complexity. Contact us for a tailored quote.

Common questions

What is generative AI?

Models that create new content — text, images, audio, code — by learning patterns from large datasets, rather than analysing or classifying existing information.

What is a large language model?

A deep-learning system trained to understand and generate human-like text on transformer architectures. Modern LLMs can also reason through multi-step problems and use external tools.

Which model is right for us?

It depends on your task, data sensitivity and stack — Claude for long-context and safety-sensitive work, Gemini on Google Workspace, Llama for on-premise/open-weight, GPT for general-purpose and agentic. We help you choose.

Does this mean training a model on our data?

Almost never. We point an existing, capable model at your governed data rather than retraining the model itself — training from scratch is prohibitively expensive outside a few frontier labs.

How do you reduce hallucination?

By grounding the model in your own governed, well-categorised data at the point of answering, rather than relying solely on what it learned in training.

Generative AI and LLMs