AI · Retrieval-Augmented Generation

Turn your data into answers you can trust.

Knowledge bases, assistants, and document intelligence grounded in your own content — so the answer comes with a source, not a guess. Built on your data, kept under your control.

Scope an AI project See how it works
What we build

AI that works on your content.

The value isn't a clever model — it's the model wired to your documents, data, and systems, with the guardrails to be trusted in production. That's what we build.

KB

Enterprise knowledge bases

A single place your team can ask — answered from your policies, SOPs, and docs, with citations.

  • Ask-your-docs search
  • Source citations
  • Access controls
  • Always current
DOC

Document intelligence

Extract, classify, and summarise invoices, contracts, and forms — at volume, automatically.

  • Data extraction
  • Classification
  • Summarisation
  • Validation rules
BOT

Custom assistants & chatbots

Assistants for support, sales, or internal ops — that can answer and take action, not just chat.

  • Support & internal bots
  • Tool & action calling
  • Web, Slack, Teams
  • Human handoff
VEC

Vector search & retrieval

The retrieval engine under it all — chunking, embeddings, and ranking tuned for your data.

  • Chunking & embeddings
  • Hybrid search
  • Re-ranking
  • Vector databases
LLM

LLM integration & fine-tuning

The right model for the job — frontier or open-weight — integrated and tuned to your domain.

  • Claude, GPT, open models
  • Prompt engineering
  • Fine-tuning
  • Cost & latency tuning
EVAL

Guardrails & evaluation

The part that makes it trustworthy: measured accuracy, refusals when unsure, and citations.

  • Evaluation sets
  • Hallucination control
  • Refusal & safety
  • Monitoring
How it runs

From use-case to production.

A RAG system is a build, run in sequence — get the data in, get retrieval right, prove accuracy, then ship. We don't skip the evaluation step that everyone wants to.

01

Discovery & use-case 1 week

We pick a use case with clear value and a measurable answer — the questions it must get right, the sources it can use, and what "good" looks like before we build anything.

02

Data ingestion & indexing

We connect your sources, chunk and embed the content, and build the index — handling messy formats, permissions, and keeping it in sync as content changes.

03

Retrieval & prompt design

We tune retrieval — search, ranking, and context — and design prompts so the model answers strictly from retrieved sources, with citations.

04

Evaluation

We build an evaluation set from real questions and measure accuracy, grounding, and refusals. Nothing ships until the numbers clear the bar we agreed in step one.

05

Deployment & integration

We deploy into your cloud or on-premise and wire it into the channels and systems your team already uses — web, Slack, Teams, or your own app and APIs.

06

Monitoring & iteration

We watch real usage, catch weak answers, and feed them back into retrieval and prompts — so the system gets more accurate the more it's used.

Why Aquila360

Built on your data — and kept that way.

We're an engineering team first: the same people who build and run enterprise ERP systems build the AI layer on top of them. Your data stays in your control, and every answer is measured, not assumed.

50+
Projects delivered
15+
Countries served
5+
Years delivering
100%
Your data, your control
Questions

AI & RAG, answered.

What is RAG, and why not just use ChatGPT?
RAG — retrieval-augmented generation — connects a language model to your own documents and data, so answers are grounded in your content with citations, not a model's general training. A public chatbot can't see your contracts, SOPs, or product data and will guess; a RAG system retrieves the right passages first, then answers from them. That's the difference between a plausible answer and a correct, sourced one.
Is our data secure? Will it train public models?
Your data stays yours. We deploy in your cloud or ours under your control, use enterprise model endpoints that don't train on your inputs, and apply access controls so the assistant only surfaces what each user is allowed to see. For sensitive workloads we can run open-weight models entirely within your environment.
Which LLMs do you build on?
We're model-agnostic and choose per use case — frontier models like Claude and GPT for the hardest reasoning, and open-weight models such as Llama or Mistral when cost, latency, or on-premise hosting matter. We design so a model can be swapped without rebuilding the system.
Can it connect to Odoo and our existing systems?
Yes. We integrate with ERPs including Odoo, document stores, databases, and internal APIs, so an assistant can answer from live business data and, where appropriate, take actions like creating a record or drafting a document. The same team behind your Odoo implementation can build the AI layer on top of it.
Do you deploy on-premise or in the cloud?
Both. We deploy on Azure or AWS, or fully on-premise where data residency or compliance requires it. We recommend the hosting model that fits your security posture and the models you want to run.
How do you prevent hallucinations?
Grounding plus evaluation. The system answers only from retrieved sources and cites them, so answers are checkable; we add guardrails to refuse when confidence is low, and we build an evaluation set to measure accuracy before and after every change. Hallucination control is a measured target, not a hope.
Keep exploring

Related services.

Odoo · Implementation

Odoo implementation

The ERP your AI works on — configured to how your business runs.

View service Capabilities

Cloud, data & custom software

The engineering around the model — Azure, AWS, APIs, and apps.

See capabilities Get started

Scope an AI project

Have a use case in mind? Tell us the questions it must get right.

Talk to us
Start here

Scope an AI project.

Tell us the use case and the questions it has to get right. We'll come back with an approach, the data it needs, and how we'll measure it.

Scope an AI project Send a brief