WorkshopScoped individually
RAG and Data Readiness Workshop
A workshop with your team on whether your documents and data suit an AI assistant with retrieval (RAG) and what must be prepared.
Custom AI engineering
Custom AI engineering: assistants, RAG and AI tools for your business
Language models can save many hours of work — but only when they are connected to your data, processes and rules. We build practical AI tools with a clearly defined task, quality control and data protection.
01
Use case
02
Prototype
03
Evaluation
04
Deployment
- Ways to work together
- Fixed-scope projectOngoing operation of the AI application
- What you receive
- A use-case description and success criteria
- First step
- A short call to clarify your goals and the current state.
When you need it
When a custom AI tool makes sense
The team answers the same questions over and over.
Writing product descriptions takes weeks.
Internal knowledge is scattered across documents and emails.
You want to use AI without putting customer data at risk.
Our approach
How we solve it
A use case with criteria
We describe the task and the test set against which the solution is accepted or rejected.
Your data as the source
Answers are grounded in your documents, catalogue and policies, with the source cited.
Swap the model without a rewrite
The architecture lets you change provider based on quality, cost and data requirements.
Custom AI engineering
The AI tools we build
Customer service AI assistants
Answers about products, delivery and policies based on your data, with hand-over to a person when needed.
Product description generation
Drafts in your brand voice that an editor reviews and approves before publishing.
Internal knowledge assistants
Fast access to procedures, terms and technical documentation for the team.
Document analysis
Extracting key information from contracts, offers, specifications and invoices.
Custom RAG solutions
Search and answers over your documents, with the source cited.
Reporting AI assistants
Summaries and explanations of sales, campaign and operations data.
Integration with your existing systems
Embedding AI features into an existing store, CRM or internal application via APIs.
Use cases
Where it helps in practice
Typical scenarios — every project starts from your own process and data.
Search over internal knowledge
A RAG assistant that answers staff questions on procedures and product documentation.
Product content
Draft descriptions and metadata in your brand's voice, approved by an editor.
Document extraction
Key fields from contracts, specifications and invoices in a structured form.
Technical review
What we assess before development
Data quality and freshness
AI answers only as well as the documents, catalogue and policies it works from.
Volume and repetition of the task
How often the task occurs and how long it takes today — that shows whether the investment makes sense.
Cost of a mistake
What happens if an answer is wrong, and where a human check is required.
Personal data and the provider
Which data will be processed, where, and under which contractual terms with the model provider.
How it will be measured
A test set of real examples and the criteria by which the solution is accepted or rejected.
What you receive from an AI project
- A use-case description and success criteria
- A working prototype for early feedback
- A test set for evaluating answer quality
- Documentation and handover of the solution
Ways to work together
- Fixed-scope project
- Ongoing operation of the AI application
Pricing is set once the scope is clear and is provided in a written quote.
Request a quoteWorkshops and assessments
Consultant-led formats with a clear scope that we usually start with.
How we work
How we build an AI tool
What we need from you
- Examples of the real task — questions, documents, descriptions
- Access to the data sources
- A person who assesses the quality of the results
- Privacy and data retention requirements
Use case
We describe the task, the users, the data and the success criteria.
Prototype
A working prototype on real examples within a short iteration.
Evaluation
Checking against the test set and adjusting sources, instructions and rules.
Deployment
Integration into your systems, access control, an action log and monitoring after launch.
Custom AI engineering
Frequently asked questions
Which AI model do you use?
We choose the model based on the task, quality, cost and data requirements. The architecture allows the provider to be changed without rewriting the application.
Can the AI give a wrong answer?
Yes. That is why we define test scenarios, restrict answers to verified sources and plan escalation to a person when the system is unsure.
What happens to our data?
Only the necessary data is sent to the model, personal data is removed where possible, and we work with providers that offer a data processing agreement.
Related tools
Start with a quick self-assessment before we talk.
- Available now
AI process analysis
AI Automation Advisor
Describe a manual process and get a structured first assessment: automation opportunities, workflow, integrations, risks and the points that need human approval.
- For:
- Owners and managers with a specific manual process
- You get:
- A structured first assessment: opportunities, workflow, integrations, risks and approval points.
- Time:
- about 3 min
- Available now
Strategy
AI readiness assessment
Twelve questions across six dimensions, from strategy to governance. You get a score, blockers, three priority actions and a 90-day plan.
- For:
- Companies considering their first AI project
- You get:
- A score across six dimensions, the main blockers, three priority actions and a 90-day plan.
- Time:
- about 5 min
Useful reading
- Guide5 min read
AI customer assistants: when they make sense and how to introduce them safely
When an AI customer service assistant delivers real value for an online store, what the risks are, and how to introduce one with quality control.
- Guide8 min read
How AI automation reduces manual work in small businesses
A practical guide to AI automation for small businesses: which tasks are a good fit, how to estimate the benefit in advance and how to keep control of important decisions.
Related services
AI workflow automation
Inquiry classification, CRM workflows, document processing and system integrations — with human approval where needed.
AI Managed Services & optimisation
Monitoring, maintenance, quality evaluation, security and cost optimisation for AI agents and automations that are already in production.
AI integrations & data systems
REST API and webhook integrations, connecting Shopify and WooCommerce with CRM and ERP, cloud deployment, monitoring and protection.
Discuss this service with us
Describe your task and we will suggest a suitable scope and engagement model.
- Written quote with a clear scope
- Transparent milestones and timelines
- Your data is used only to reply to you