AI consultancy · Kathmandu & Tallinn
AI that fits the way your business actually works.
We study your company first, then build the AI solution that makes sense for it, and tell you honestly when AI isn’t the answer.
The problem
Most AI projects start in the wrong place.
A lot of AI work begins with a tool. Someone sees a demo, buys a licence or builds a chatbot, then goes looking for a problem it can solve.
That’s backwards. The tool rarely matches how the team really works, the data isn’t ready, and nobody agreed what success looks like. The project stalls, and AI gets written off as hype.
We start from the other end: your business, your workflows and your data. The technology comes last.
Tool first
- 1. Pick an AI tool
- 2. Look for a use for it
- 3. Force it into the workflow
- 4. Wonder why nobody uses it
Business first
- 1. Understand how work gets done
- 2. Find the real bottleneck
- 3. Choose the right tool, if any
- 4. Build it into daily work
Our approach
Four steps, each one built on the last.
Like layers in a stack: we don’t move up until the one beneath is solid.
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Step 01
Assess
We study how your business actually runs: its structure, workflows and data.
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Step 02
Identify
We find where AI creates real value, and say so plainly when it doesn’t.
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Step 03
Build
We build the solution with the right mix of closed and open-source models.
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Step 04
Deploy & support
We put it into production and help your team use it well.
What we build
Practical systems, not science projects.
Each one is shaped around your data and the way your team works.
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RAG systems
Answers from your own documents
An assistant that answers questions using your company’s documents and data, and shows where each answer came from.
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Fine-tuned private models
Private models trained on your data
A custom open-source model adapted to your tasks and language, able to run on your own servers so data never leaves.
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AI workflow automation
AI in your everyday workflows
AI steps added to the processes you already run: reading documents, sorting requests, drafting replies, filling systems.
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AI readiness assessment
AI readiness assessment
A clear-eyed review of your data, processes and goals, with a prioritised plan of where AI will and won’t help.
Why work with us
We’re on the side of your business, not a product.
Honest advice
We don’t sell a product, so we have no reason to push AI where it doesn’t belong. If a simpler fix will do, or the timing is wrong, we’ll tell you.
Model-agnostic
We work with closed-source models like Claude, GPT and Gemini, and open-source models like Llama, Mistral and Qwen. We pick for cost, privacy and performance, not brand loyalty.
Privacy-first options
When your data is sensitive, we can run models on your own servers or private cloud, so it never leaves your control.
What this could look like for you
Example use cases across industries.
These are illustrations of what’s possible, not past client projects. Your version would be shaped by your own workflows and data.
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Example Professional services
An internal knowledge assistant
Staff ask questions in plain language (“What’s our policy on travel for client visits?”) and get an answer drawn from your handbooks, past proposals and internal wikis, with links to the source.
Typical approach: RAG
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Example Software and e-commerce
A support assistant grounded in your docs
Customers get accurate first answers based on your help centre and product documentation. Anything uncertain is handed to a person, along with a summary of the conversation.
Typical approach: RAG + workflow automation
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Example Finance, logistics, healthcare
Document processing
Invoices, forms, shipping documents or reports are read automatically, key fields are extracted into your systems, and unusual cases are flagged for review.
Typical approach: Fine-tuned private model + automation
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Example Retail and distribution
Demand forecasting
Sales history, seasonality and local factors are combined to forecast demand, so purchasing decisions rest on more than gut feel. Sometimes classic statistics beat language models here, and we’ll say so.
Typical approach: Readiness assessment + build
Do we need lots of data to get started?
Usually not. Many useful systems, such as an assistant that answers from your documents, work with whatever documents you already have. Fine-tuning a model needs more: typically a few hundred to a few thousand good examples of the task. Part of our assessment is telling you honestly whether your data is enough, and what to collect if it isn’t.
Does every company need RAG and QLoRA?
No. They are tools, not goals. RAG (a technique that fetches the relevant context from your database in the most efficient way, which can then be provided to the LLM during inference) makes sense when people spend a lot of time looking things up. QLoRA (an efficient way to train a private model on your data) makes sense when you have a repeated task, sensitive data or high volume. Many companies need neither: a well-written prompt on an existing model, a simple automation, or a better process can be enough. We recommend the simplest option that does the job.
Is our data safe?
We treat it as confidential from the first conversation and will sign an NDA before you share anything sensitive. We only ask for the data a task needs. When data must not leave your environment, we can run open-source models on your own servers or private cloud, so no outside AI provider ever sees it. As a company registered in the EU, we handle personal data under the GDPR.
What’s the difference between open-source and closed-source models?
Closed-source models such as Claude, GPT and Gemini are run by the companies that make them. You use them over the internet and pay per use. They are often the most capable. Open-source models such as Llama, Mistral and Qwen can be downloaded and run on hardware you control, which helps with privacy and cost at scale. We often use both, each where it fits best.
Not sure where AI fits in your business? Let’s find out together.
A free, no-obligation conversation about how your business runs and where AI could help. If it isn’t the right fit, we’ll tell you.