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.
30 minutes, no demo, no slides. Or 7 questions, no email needed for the result.
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.
How we think
Every diagnosis has four possible endings. Only one of them is AI.
Most firms need your problem to be an AI problem, because their solution was decided before they met you. Ours is decided at step four.
Where a diagnosis can land
A business problem goes through diagnosis and can end in one of four ways: a process change, deterministic automation, an AI system, or the conclusion that nothing should be built. The last outcome is a valid and common result, and it is the cheapest one to reach.
What actually changes.
Where most companies are
- Five systems that do not talk to each other
- Knowledge that lives in three people's heads
- Copy-paste between screens, all day
- AI experiments nobody measured
- A board asking what it returned
Where the work ends
- Workflows mapped, with their real cost attached
- One source of truth people actually search
- The repetitive half running without anyone
- One AI system in production, scoped and monitored
- A number you can put in front of the board
Not every engagement reaches all five. The audit tells you which ones are reachable for you, and what each one costs, before you commit to any of them.
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.
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.
Client proof, without the theatre
The deliverable is a trail of proof.
No invented logos. No vague “AI transformation”. The work leaves behind artifacts a technical team can inspect, a sponsor can fund, and a board can challenge.
Secure internal knowledge assistant.
The hard part is not the model. It is proving that nobody can read what they could not read before.
Open the full engineering noteScenario / design target
Public reference
Secure knowledge assistant
A reference architecture for retrieval that respects live permissions, cites sources, and knows when to refuse.
Read the architectureClient-ready protocol
Evaluation before implementation
A question set, permission replay, refusal tracking, and an agreed release gate before production work starts.
See the audit scopeDecision artifact
Build, automate, or stop
A decision memo that makes the trade-offs legible to the technical team and the board in the same room.
See how we decideMost 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.
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.