Services
What we build, in plain language.
Four ways we help companies put AI to work. For each, here’s what it is, when it’s the right choice, what you get, and how it works.
RAG systems
Answers from your own documents
RAG stands for retrieval-augmented generation. In plain terms: before the AI answers, it first looks up the relevant pages in your own material (policies, manuals, contracts, tickets, product data) and writes its answer from those. It cites its sources, so your team can check the answer instead of trusting it blindly.
When it’s the right choice
- Your team spends a lot of time searching for information that already exists somewhere.
- Answers must be grounded in your own documents, not general internet knowledge.
- Your information changes often, so a model that “memorised” it once would go out of date.
- You need to see where every answer came from.
What you get
- A working assistant connected to the document sources you choose
- Answers with links or references to the source passages
- Access controls so people only see what they’re allowed to see
- A test set of real questions to measure answer quality
- Documentation and a handover session for your team
How it works
- 1 We gather and clean your documents, then split them into searchable passages.
- 2 Each passage is indexed so the system can find it by meaning, not just keywords.
- 3 When someone asks a question, the most relevant passages are retrieved.
- 4 A language model writes the answer using only those passages, and cites them.
Fine-tuned private models
Private models trained on your data
Fine-tuning means taking an existing open-source language model (such as Llama, Mistral or Qwen) and training it further on examples from your business, so it learns your terminology, formats and tasks. We use a method called QLoRA, which trains a small set of extra settings on top of a compressed model. That makes training affordable and lets the finished model run on modest hardware you control.
When it’s the right choice
- Your data is sensitive and should not be sent to an outside AI provider.
- You have a repeated, well-defined task: classifying, extracting, drafting in a set format.
- You use specialised language, or a language general models handle poorly.
- Usage volume is high enough that per-request fees from hosted models add up.
What you get
- A fine-tuned model tuned for your specific task
- An evaluation report comparing it with the base model and a hosted alternative
- Deployment on your infrastructure or a private cloud you control
- The training data pipeline, so the model can be retrained as things change
- Clear guidance on what the model does well and where its limits are
How it works
- 1 We define the task precisely and collect good examples of the right output.
- 2 We pick a base model that suits the task, your hardware and your licence needs.
- 3 We train with QLoRA, then test the model on examples it has never seen.
- 4 We package and deploy it, with monitoring so you can see how it performs.
AI workflow automation
AI in your everyday workflows
Most of the value from AI comes from small, reliable steps inside existing work, not from a single big chatbot. We connect AI to the tools your team already uses (email, spreadsheets, CRMs, ticketing systems) so routine tasks like reading invoices, routing requests or drafting first replies happen automatically, with a person reviewing where it matters.
When it’s the right choice
- People spend hours on repetitive reading, copying, sorting or summarising.
- The process is clear enough to describe step by step.
- Mistakes can be caught by a human check before they cause harm.
- You want gains without replacing the systems you already use.
What you get
- Automated workflows connected to your existing tools
- Human review steps where accuracy matters
- Logs of what the AI did and why, for auditing
- A simple way to measure time saved and error rates
- Runbooks so your team can operate and adjust it
How it works
- 1 We map the current process with the people who do it every day.
- 2 We mark which steps AI can handle, and which need a person.
- 3 We build the automation and test it on real past examples.
- 4 We run it alongside the old process first, then switch over when it proves itself.
AI readiness assessment
AI readiness assessment
Before spending on AI, it helps to know what you are working with. We review how your business runs, what data you have and how usable it is, and where the real bottlenecks are. You get a short, practical report: the opportunities worth pursuing, the ones to skip, and what needs to be in place first.
When it’s the right choice
- You know AI matters but aren’t sure where to start.
- You’ve heard many pitches and want an independent view.
- Leadership needs a grounded plan before committing budget.
- A previous AI project stalled and you want to understand why.
What you get
- A map of your key workflows and data sources
- A ranked list of AI opportunities, with expected effort and value
- An honest list of areas where AI is not the right answer
- Data and process gaps to fix first
- A recommended first project, scoped and ready to start
How it works
- 1 Interviews with leadership and the people who do the work.
- 2 A review of your systems, documents and data quality.
- 3 Analysis of where AI fits, where simpler tools fit, and where neither does.
- 4 A written report and a working session to go through it together.
Under the hood
Choices that run through everything we build.
Hybrid model strategy
Closed-source models (Claude, GPT, Gemini) are often the strongest at complex reasoning. Open-source models (Llama, Mistral, Qwen) can run privately and cost less at scale. We use each where it fits best, sometimes in the same system, and we don’t lock you into one provider.
Private and self-hosted deployment
For sensitive data, we can deploy models on your own servers or in a private cloud account you control. Your data stays inside your environment, and no outside AI provider sees it.
Engagement model
Start small. Grow only when it works.
Every engagement moves through three stages. Each ends with a clear decision point, so you never commit to more than you’ve seen proven.
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Stage 1
Assessment
We study your business and data and agree on the most valuable first project. You keep the report whether or not you continue with us.
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Stage 2
Pilot
We build a focused version of the solution and test it with real users and real data. The goal is a clear answer: does this work well enough to be worth scaling?
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Stage 3
Production
We harden the pilot into a reliable system, connect it to your tools, train your team, and support it after launch.
Every project is scoped and priced after our first conversation, once we understand what you need. The first consultation is free.
Which of these fits your business?
You don’t need to know yet. Tell us how your business works and what’s slowing it down, and we’ll suggest where to start.