How small modular reactors appear in the algorithmic choice of Russian energy operators
State energy operator · small nuclear generation programme
71 out of 100 — this is how much the object influences the choice in this scenario0 — the buyer never sees you, 100 — you are recommended first
What to change to be chosen
We found three possible directions for growth. We estimated how each of them could affect the buyer's choice and potential revenue.
- RosatomRussiaH17 mo
Settle the question of commissioning dates.
Shortlist7681AI answers out of 100 that name Rosatom+7%to revenue from the buyer's decision journeyScenario estimate - RosatomRussiaH27 mo
Win the comparison on lifecycle cost.
Shortlist7680AI answers out of 100 that name Rosatom+5%to revenue from the buyer's decision journeyScenario estimate - RosatomRussiaH37 mo
Reach the recommendation through series production.
Shortlist7679AI answers out of 100 that name Rosatom+4%to revenue from the buyer's decision journeyScenario estimate - OKBM AfrikantovNot assessed
The market is measured, but there is no report or growth hypotheses for OKBM Afrikantov. We build them to order.
What task is the buyer solving?
Pick a choice scenario — a specific task of the buyer.
See which options AI systems consider, whom they recommend and how the participants' positions change across the stages.
Language shapes the choice
We study the same task in Russian and English to see how language affects AI-system recommendations and companies’ positions.
Why does Rosatom appear in 94% of answers but get only 64% of recommendations?
We traced how Rosatom passes the five stages of choice in AI-system answers. See where its position weakens most and how competitors perform.
Measurement conditions: S1 «Choosing a technology for a small plant in an isolated area» · journey «Problem-first» · Russia · languages RU, EN · AI systems: 7 · Q1 2026
Height of a column is the share of answers where the object passes the stage. The narrowing between columns is the loss.
A stage is analytical markup of a step, not a required order of conversation: a journey may start with a recommendation and come back to comparison.
Key finding
On its home market Rosatom is named first and reaches the recommendation on most journeys: foreign vendors are barely considered because of supply and licensing.
Point of maximum loss
The largest loss occurs between the stages «Shortlist» and «Recommendation»
Rosatom · journey «Problem-first»
Why the position is lost
Choice factors that keep the object out of the final recommendation.
Position depends on the journey and on the scenario
Probability of a final recommendation for Rosatom for each decision journey, in both scenarios.
The scenarios are different economic tasks. Their levels may be set side by side but not subtracted: the gap between scenarios is not a deficit to be closed.
Who takes the place
Tile area and colour show the win index on this market — shortlist and recommendation. The participation index is in the table view.
What to change to be chosen more often?
Recommended for Rosatom · RussiaWe identified what keeps the company from winning against competitors and proposed concrete actions to strengthen its position in the market.
Choose the solutions you want to implement. GolOps will help organise the work and check the result.
- 01
Give customers a confirmed commissioning date
Make a confirmed commissioning schedule for the first small reactor unit publicly available.
Forecast+5pts to the index - 02
Show customers the full cost of ownership
Disclose lifecycle costs so customers can compare your project with foreign alternatives more easily.
Forecast+4pts to the index - 03
Prove you can deliver reactors at scale
Publish open data on serial production and manufacturing capacity utilisation.
Forecast+3pts to the index
Every solution you add raises the plan’s potential
How the forecast is calculatedforecast · model
- 01 addresses the loss factor “The first unit's commissioning date is not publicly confirmed” · 47% of answers
- 02 addresses the loss factor “Little open data on lifecycle cost” · 38% of answers
- 03 addresses the loss factor “Questions about series production and plant loading” · 24% of answers
This is a sample analysis for an object that entered the universe of this measurement. The universe is assembled anew at every scan of the category: its composition and the positions within it may change. Forecast, not a measurement. The upper bound is the loss at the narrowest transition, distributed across the weights of the loss factors; stage-passing points are carried into index points one to one, and the conversion coefficient is not yet established by the methodology. The money figure uses a working calibration: CCI is measured inside the category universe rather than against the whole market, so 25 index points are taken as roughly 2 percentage points of market share. The actual gain is obtained empirically only — by repeating the same benchmark after the intervention.
Check the conclusions against the raw observations
Study the data, reproduce the conclusions or find what we missed.
How the measurement works- 170
- observations
- 7
- AI systems
- 2
- languages · RU, EN
- Q1 2026
- measurement
Small modular reactors × Russia
Take this study under your name. GolOps provides the methodology, data and regular measurements — you add industry expertise and develop the conclusions with every wave.
Authorship stays with you. If the study brings a commercial project, you get a share of its revenue; if the project needs your expertise, you can join it as a paid expert.
