Structure before software.
Find where AI can actually create value in your business.
We audit your processes, identify high-ROI AI opportunities, and turn the best ones into production systems. Sometimes the answer is that you don't need AI. We'll tell you that too.
Most AI projects don't fail on the model.
They fail on unclear success criteria, weak data foundations, and workflows nobody mapped before the build started.
What we build
Services
AI Audit & Strategy
Find the opportunities worth funding, and the ones worth killing.
RAG & Knowledge Systems
Retrieval that inherits your existing permissions instead of ignoring them.
AI Agents
Agents scoped to one job, with measurable success criteria.
AI Automation
Often the honest answer. No model required.
Data & AI Engineering
The layer everything else fails without.
Looking for a problem rather than a technology? Start with Solutions.
Most retrieval demos skip the part that matters.
Choosing a vector database and a model is the easy half. The half that decides whether you can actually deploy is who is allowed to see what.
How our retrieval works
Retrieval augmented generation in five steps: a natural language question is embedded and matched against the document index; every candidate passage is then filtered against the asking user's permissions, so documents inherit the access rights of their source system; only permitted passages are passed to the language model; and every answer links back to its exact source.
The audit
Six steps, fixed scope, one deliverable you can act on.
Discovery
How the business actually runs, not how the org chart says it does.
Process mapping
Repetitive tasks, real costs, available data, current pain.
Opportunity scoring
Impact × Feasibility × Cost × Risk. Published, not hidden.
Architecture
RAG, agent, classic automation, or nothing. We say which.
ROI
Current cost versus solution cost versus expected saving.
Roadmap
Quick wins, medium term, strategic. Priced.
Questions we get asked
What if the audit concludes we should not build anything?
Then that is the deliverable, and it is worth what you paid for it. Chasing technology instead of an outcome is the single most cited cause of AI project failure. We would rather tell you early.
Why pay for an audit instead of going straight to a build?
Because four out of five AI projects do not deliver their expected value, and the causes are organisational rather than technical. Unclear success criteria, weak data foundations, poor workflow integration. None of those get fixed by writing code faster.
Why is the company called Strukora?
It is an invented name built on structure. We chose it because the work starts with the structure of how your business actually runs, not with the software you put on top. That is also where most AI projects fail: not on the model, but on the process nobody mapped first.
What does the audit cost?
It depends on how many processes are in scope and how scattered your data is, so we price it after the first call rather than guessing in public. What is fixed and published is the scope, the duration and the deliverable. You will know exactly what you get before any number is discussed.
Is the first call a sales call?
It is thirty minutes to review where you actually are and whether we are useful to you. If the honest answer is that you do not need us yet, you will hear that on the call rather than after an invoice.
We already have an IT team. Why would we need you?
You probably do not need us for the build. You may need us for the diagnosis, because your team is close to the systems and far from the comparison set. That is a different job.