# tatsyi-raiffeisen-ukraine-ai-engineers-different-not-just-faster-2026-05-05

## Veille

Medium op-ed by **Hryhorii Tatsyi** (CTO, **Raiffeisen Bank Ukraine**, ~900 IT engineers) reporting a **12-month longitudinal study** (May 2025 → April 2026) on the real impact of generative AI in a large European bank. Pivot thesis: ***"AI didn't make our engineers just faster. It made them different."*** Unlike individual accounts (Frizzo, Cherny) or meta-level ones (Curran/Intercom), this is a **quantified organizational assessment from a traditional regulated bank** — a corpus still rare in 2026. Results: **−75 people (−8% headcount, including 64 engineers)** over 12 months, yet **more code shipped, fewer incidents, improved security**; AI adoption **62% → 83%**; **68% of engineers receive ≥50% of their code via AI assistance**; **new-engineer onboarding 60-90 days → ~40 days** (consistent with Anthropic data of 82→40 days). Three emerging archetypes: (1) **Copilot-only** +10-25% on PRs, same scope; (2) **Multi-tool** story points ×1.5-3, cross-repo scope +50-80%; (3) **Claude on corporate stack** code volume ×4.5, radically expanded scope. **Seven AI products built** that did not exist before: Service Knowledge Hub (57 microservices, 83 releases/month), Mobile Android workflow CI plan/implement/test, AI Agent Portal (2,085 users / 649 MAU in 87 days, MCP generation via OpenAPI specs), Shift-left Security Plugin (−82% exposed secrets), DevPortal Backstage + Kubernetes diagnostics agents (−68% critical incident resolution time), DRAIF MCP text-to-SQL Data Lake with 10,000 tables (embedding fine-tuned 2× OpenAI), Call Evaluation (>97% transcription accuracy, voted best product in the Raiffeisen group). Stability: **blocking incidents −70%, critical resolution −68%, high-severity security alerts resolved +155%**. Central strategic insight: ***"AI expanded our production possibility frontier, and we deliberately allocated the freed capacity"*** — AI does not do the same thing faster, it shifts **what one can decide to do**. The evaluation question to reframe: not *"by how much % did existing KPIs increase"* but ***"what your engineers built that didn't exist before"***. AI lifts underperformers to baseline more than it accelerates top performers; **senior architects return to active development** after years away from it. Major relevance for banking/insurance/regulated-sector executive committees (Raiffeisen = bank, Ukraine = wartime context + operational resilience).

## Titre Article

AI didn't make our engineers just faster. It made them different.

## Date

2026-05-05

## URL

https://medium.com/@milhibisidek/ai-didnt-make-our-engineers-just-faster-it-made-them-different-95f1c1d4efd0

## Keywords

Hryhorii Tatsyi, Raiffeisen Bank Ukraine, CTO bank, 12-month longitudinal study, AI didn't make engineers just faster made them different, production possibility frontier, freed capacity allocation, 900 engineers, −75 people in 12 months, −64 engineers, AI adoption 62 to 83 percent, 68 percent engineers majority AI assistance, onboarding 60-90 days to 40 days, three archetypes, Copilot only 10-25 percent, multi-tool engineers 1.5-3x story points, Claude on corporate stack 4.5x code volume, seven new products, Service Knowledge Hub, 57 microservices 83 releases per month, Mobile Android workflow CI plan implement test, AI Agent Portal 2085 users 649 MAU, MCP agents from OpenAPI specs, Shift-left Security Plugin -82 percent secrets exposed, DevPortal Backstage Kubernetes diagnostics, -68 percent critical incident resolution time, DRAIF MCP text-to-SQL Data Lake 10000 tables, embedding fine-tuned 2x OpenAI, Call Evaluation >97 percent transcription accuracy, best product Raiffeisen group, blocking incidents -70 percent, security alerts resolved +155 percent, AI lifts underperformers to baseline, senior architects return to active development, what your engineers built that didn't exist before, regulated bank, banking sector AI, Ukraine operational resilience, organizational study, large enterprise IT 900 engineers, headcount contraction productivity maintained, technical debt repaid, debt repayment freed capacity, headcount contraction shipping more, deliberately allocated freed capacity, type of work transformed not just speed, measure AI impact

## Authors

**Hryhorii Tatsyi** — CTO de **Raiffeisen Bank Ukraine** (filiale ukrainienne du groupe bancaire autrichien Raiffeisen Bank International, RBI). Auteur Medium @milhibisidek. Profil discret côté visibilité publique (25 followers Medium au moment de la publication), mais position institutionnelle de premier plan : il dirige une organisation IT d'environ 900 ingénieurs dans une banque systémique opérant en contexte ukrainien (économie de guerre depuis 2022, résilience opérationnelle critique). L'article est sa première contribution publique d'envergure documentée sur cette plateforme.

## Ton

**Profile**: A **CTO-organizational case study** Medium op-ed, roughly 17 minutes to read, signed by a sitting CTO reporting the results of a structured, documented 12-month AI transformation at a regulated European bank. A hybrid format between an **internal transformation report** (quantified, sourced, metric-driven) and a **strategic essay** (pivot thesis, reframing of the evaluation question). Target audience: peer CTOs/CIOs/IT directors of large IT organizations, particularly in regulated sectors (banking, insurance, telecom, energy) looking for **organizational case studies** rather than individual accounts or vendor promises. Secondary audience: executive committees, board members, industry analysts, HR/transformation teams, Engineering Management communities.

**Style**: First-person-plural voice (*"our engineers"*, *"we deliberately allocated"*) that owns the **institutional position and decision-making responsibility**. Clear, factual English, **high quantitative density** (adoption percentages, PR volumes, incident cycles, user counts, embedding coefficients). A **sober, pedagogical, hype-free** register — this is the ethic of the *practitioner-CTO* reporting what worked and what transformed the organization, not a manifesto. No catastrophism either: Tatsyi does not dramatize the headcount reduction, he **contextualizes** it within a **deliberate reallocation of freed capacity** toward features, stability, and technical-debt repayment.

**Key aphorisms**:
- ***"AI didn't make our engineers just faster. It made them different."*** (title — a *speed / nature of work* symmetry that sums up the thesis).
- ***"AI expanded our production possibility frontier, and we deliberately allocated the freed capacity."*** (the strategic pivot phrase — economic-organizational).
- ***"What your engineers built that didn't exist before."*** (the reframed evaluation question — the **right metric**).

**Elaborated metaphors**:
- **Production possibility frontier** — a borrowing from microeconomics that makes visible how AI does not do the same thing better, it **shifts the entire set of possible choices**. A particularly powerful metaphor for executive committees and boards fluent in strategic vocabulary.
- **Freed capacity allocation** — the language of IT *capacity planning* projected onto the AI transformation: freed capacity is an **asset to allocate**, not a cost to eliminate. Frames the headcount reduction as a **strategic reallocation**.
- **Three archetypes** — a tripartite typology of engineers (Copilot-only / Multi-tool / Claude-on-corporate-stack) that makes the **uneven distribution** of AI impact within a single organization tangible.
- **AI lifts underperformers to baseline** — a counterintuitive formula (AI does not mainly accelerate the fast, it **catches up the slow**) that flips the usual cliché.

**Epistemic stance**: balanced and **institutionally owned**. Tatsyi reports **massive gains** without triumphalism and **does not dodge** sensitive topics (headcount reduction, redefinition of the job). He explicitly places himself in the lineage of Anthropic studies (onboarding figures) while contributing a **banking/Central-Europe angle** missing from the dominant Silicon-Valley-centric corpus. The approach is **Bayesian**: he publishes verifiable internal figures that other European CTOs can contradict or confirm.

**Authority**: built through (a) **institutional position** (CTO of a regulated systemic bank), (b) **quantitative density and longitudinal coverage** (12 months, 900 engineers, seven products, stability/security metrics), (c) **convergence with external Anthropic data** (onboarding 82→40 days), (d) a **sober tone** that inspires confidence among peer CTOs, (e) the **Ukrainian context**, which adds a dimension of **operational resilience proven under adverse conditions**. Limitation: Tatsyi still has low public visibility (25 Medium followers), so his authority rests on the **quantitative substance** rather than personal brand.

## Pense-betes

- **Date / source**: May 2026 (relative "2 days ago" at the time of consultation, i.e. ~2026-05-05), **Medium** @milhibisidek. URL: https://medium.com/@milhibisidek/ai-didnt-make-our-engineers-just-faster-it-made-them-different-95f1c1d4efd0
- **Format**: CTO op-ed, **organizational case study** (~17 min read).
- **Author**: **Hryhorii Tatsyi**, **CTO of Raiffeisen Bank Ukraine** — a regulated systemic bank, ~900 IT engineers.
- **Study period**: **May 2025 → April 2026** (12 full months).
- **Title aphorism**: ***"AI didn't make our engineers just faster. It made them different."***
- **Strategic pivot phrase**: ***"AI expanded our production possibility frontier, and we deliberately allocated the freed capacity."*** ### Organizational framing — the observed transformation | Dimension | Before (May 2025) | After (April 2026) | Change | |-----------|------------------|--------------------|-----------| | IT headcount | ~900 engineers | ~825 (−75 people, including 64 engineers) | **−8%** | | AI adoption | 62% | 83% | **+21 points** | | Engineers ≥50% code via AI | — | 68% | new metric | | Onboarding (1st PR) | 60-90 days | ~40 days | **−~50%** | | Code volume shipped | baseline | increased | **↑** | | Blocking incidents | baseline | −70% | **↓** | | Critical resolution time | baseline | −68% | **↓** | | High-severity security alerts resolved | baseline | +155% | **↑** | **Reading**: the headcount contraction (−8%) **coincides with** improvement on every axis — output, quality, security, onboarding. This is the **central data point** Tatsyi highlights, and he **does not dodge it politically**. ### Three emerging engineer archetypes | Archetype | Tools | Productivity effect | Scope effect | |-----------|--------|---------------------|------------------| | **Copilot-only** | GitHub Copilot alone | +10-25% on PRs | stable scope | | **Multi-tool** | combination of several AI assistants | story points **×1.5-3** | cross-repo scope **+50-80%** | | **Claude on corporate stack** | Claude Code (or equivalent) integrated into the internal stack | code volume **×4.5** | scope **radically expanded** | **Convergence**: senior architects **return to active development** after years away from it (consistent with Karpathy's claim that *"agents reduce friction to creation"* and Cherny's *"best accountant writes accounting software"*). **Counterintuitive insight**: ***AI lifts underperformers to baseline rather than mainly accelerating top performers.*** A position **opposite to the "elite 10×+ tail" reading** (Cherny / Curran top 5% / Karpathy) — Tatsyi describes a **bottom-up catch-up effect** that tightens the distribution. The two readings are **compatible**: the distribution **tightens from the bottom** AND **widens at the top** (top performers who stay at 10×+). ### Seven AI products built that did not exist before | # | Product | Description | Key metrics | |---|---------|-------------|----------------| | 1 | **Service Knowledge Hub** | Auto-generated microservice documentation via Kubernetes parsing | **57 microservices**, **83 releases/month** | | 2 | **Mobile Android workflow CI** | Automated plan / implementation / test pipeline for mobile | complete redesign of the mobile SDLC | | 3 | **AI Agent Portal** | Internal portal for automatic MCP agent generation from OpenAPI specs | **2,085 users**, **649 MAU**, **87 days** to reach this adoption | | 4 | **Shift-left Security Plugin** | In-IDE vulnerability detection before commit | **−82% exposed secrets** | | 5 | **DevPortal** | Backstage + AI Kubernetes diagnostics agents | **−68% critical incident resolution time** | | 6 | **DRAIF MCP** | Text-to-SQL over a Data Lake | **10,000 tables**, embedding fine-tuned **×2 OpenAI models** | | 7 | **Call Evaluation** | Audio transcription analysis + script redesign | **>97% accuracy**, **voted best product in the Raiffeisen group** (RBI) | **Strategic reading**: this is **not** a list of experiments, it is a **product portfolio** deployed in production, with measurable internal adoption, and one product (Call Evaluation) that **spans the group's subsidiaries** (moving from local to group-wide RBI). ### The pivot thesis — production possibility frontier > ***"AI expanded our production possibility frontier, and we deliberately allocated the freed capacity."***
- **Keyword 1 — "expanded"**: AI does not do the same thing better, it **enlarges the set of what's possible**.
- **Keyword 2 — "deliberately allocated"**: the freed capacity is **redirected by managerial decision**, not mechanically absorbed into more of the same work.
- **Three reallocation directions**: 1. **Features** (new products — the 7 listed above). 2. **Stability** (incidents −70%, resolution −68%). 3. **Technical-debt repayment** (rare and capitalizable from a CTO's standpoint). ### The reframed evaluation question **Wrong question**: *"By how much % did our existing KPIs increase?"* **Right question**: ***"What did your engineers build that didn't exist before?"*** **Why it matters**: percentages on existing metrics **miss the main transformation** — not speed but **type of work**. Optimizing for legacy KPIs means **missing the strategic window** where AI makes it possible to build what was previously unaddressable. ### Tie-in to the watch corpus #### Numeric convergences (committed median 3-5×)
- **Frizzo** (LinkedIn 2026-05-05): 3-5× productivity multiplier over 1 year of daily use.
- **Wescale Usine Logicielle Augmentée** (2026-05-03): *"realistic X3-X4"*.
- **Curran/Intercom** (2026-04-16): 3× R&D productivity over 16 months (entire organization, 500 R&D staff).
- **DORA Report 2025** (2025-09-23) and **Stanford Denisov-Blanch** (2025-11-23).
- **Tatsyi/Raiffeisen** (2026-05-05): *story points ×1.5-3 (multi-tool), ×4.5 code volume (Claude corporate stack)*. #### Onboarding convergence (60-90 days → ~40 days)
- **Tatsyi/Raiffeisen**: 60-90 days → ~40 days.
- **Anthropic internal studies** (cited by Sun NYT 2026-04-30 and others): 82 days → 40 days.
- → Correct reading: **independent convergence** between a European bank and a Silicon Valley AI player on the **same target figure (~40 days)**. This is a robust **stylized fact** for 2026. #### "Seven new products" / creative capacity convergence
- **Tatsyi**: 7 AI products in 12 months.
- **Cherny** (2026-05): 100% of code generated, *"a few dozen PRs/day, 150 PRs in a single day record"*, multiple Anthropic Labs products.
- **Curran/Intercom** (2026-04-16): Skills-Based Plugin Architecture (153 contributors, **267 skills**).
- **Wescale** (2026-05-03): *"long-standing needs that remained too costly can finally be addressed"*.
- → **Strong convergence**: the relevant measure is no longer speed on existing work but the **portfolio of new products / newly addressable spaces**. #### "The job is changing shape" convergence
- **Frizzo** (2026-05-05): *"the new bottleneck is supervision"*, *"writing muscle atrophy"*.
- **Tatsyi** (2026-05-05): *"AI didn't make our engineers just faster. It made them different"*, **three emerging archetypes**, **senior architects returning to active development**.
- **Karpathy** (2026-04-29): *Software 1.0/2.0/3.0*, *"outsource thinking but not understanding"*.
- **Mornati** (2026-03-14): *What is a Developer When We Use Coding Agents?*
- **Habert PROJ-AI** (2026-05-05): *"agent directives + Decision Records + five validation dimensions"*.
- → **Cross-cutting convergence**: the nature of the work has changed, not just its speed. Tatsyi contributes the **banking-sector organizational data point** that was missing from the corpus. #### Productive tension with "AI lifts underperformers"
- **Cherny** (2026-05): elite 10×+ tail (150 PRs/day record).
- **Curran/Intercom** (2026-04-16): top 5% at 6× median PR throughput (≈ 18× pre-AI baseline).
- **Karpathy** (2026-04-29): *"10× is not the speed up — people who are very good at this peak a lot more than 10×"*.
- **Tatsyi/Raiffeisen** (2026-05-05): *"AI lifts underperformers to baseline"* — a **bottom-up catch-up effect**.
- → A **compatible and productive** reading: the distribution **tightens from the bottom** (Tatsyi) AND **widens at the top** (Cherny, Karpathy, Curran top 5%). Both phenomena coexist. Useful for executive-committee presentations that want to both reassure (catch-up) and inspire (top performers). #### FR / Central-European vs. Anglo-Saxon positioning
- Tatsyi is **European** (Ukraine, Austrian RBI group), a **regulated** bank, in a **wartime context**.
- His **methodological rigor** (12 months, internal figures, per-archetype granularity) aligns more with **French caution** (Wescale, Habert) than with **American optimism** (Cherny, Curran).
- To be used in FR presentations as a **European banking case study** complementing the Wescale/consulting and Curran/SaaS figures.
- **Rare advantage**: a **systemic bank** CTO publishing his internal figures — a type of testimony **almost absent** from the 2026 corpus. ### Limitations to flag
- **No detailed quantitative methodology**: Tatsyi reports percentages without precisely documenting how they are measured (e.g., "story points ×1.5-3" — what baseline? what attribution? what bias adjustment?).
- **Author with low public visibility** (25 Medium followers) — authority rests on institutional position rather than personal brand. Should be weighed against other sources if the stakes involve citing this in an executive committee.
- **No explicit discussion of the cognitive costs** (FOMO, deskilling, ownership erosion) that Frizzo names. Tatsyi is **organizational**, Frizzo is **individual** — the two notes complement each other.
- **No discussion of banking-sector-specific regulatory risks** (GDPR, EU DORA, ECB/EBA supervision) — surprising for a systemic bank CTO. The topic was possibly omitted to preserve the readability of the public article.
- **Onboarding convergence 82→40 days with Anthropic**: needs verification that the cited Anthropic data point is indeed the study Tatsyi refers to (risk of reverse cherry-picking — an apparent convergence on a round number).
- **Sample of n=1 organization**: a single case, even if quantified. Should be replicated at other European banks to conclude a sector-wide stylized fact. ### To be used for
- **Banking/insurance/regulated-sector executive committee presentations**: a rare, quantified European case study, a counterweight to the Silicon Valley over-representation in the corpus.
- **HR / transformation strategy**: justifying the **reallocation of freed capacity** rather than simple cost reduction.
- **Boards / transformation sponsors**: reframing the evaluation question (*"what did your engineers build that didn't exist before"*) — a tool for **defusing the % productivity debate**.
- **Convergent quantitative sourcing**: 60-90 days → ~40 days onboarding (Anthropic + Tatsyi), 3-5× committed median, headcount contraction compatible with improved output/quality/security.
- **Equity debate / productivity-distribution skew**: to be used **alongside** Cherny/Karpathy/Curran top 5% to present a nuanced reading — AI **tightens from the bottom** AND **widens at the top** simultaneously.
- **Engineering Management community**: a rare CTO publication from a large regulated European IT organization on the subject.

## RésuméDe400mots

Hryhorii Tatsyi, CTO of **Raiffeisen Bank Ukraine** (~900 IT engineers), published a **12-month** longitudinal account (May 2025 → April 2026) of his organization's AI transformation on Medium in May 2026. The title crystallizes the thesis: ***"AI didn't make our engineers just faster. It made them different."*** This is one of the **rare quantified organizational case studies from a regulated European bank** available in 2026.

**Core data**: IT headcount contracted by **75 people (−8%, including 64 engineers)** — yet **more code shipped, fewer incidents, improved security**. AI adoption rose from **62% to 83%**; **68% of engineers receive ≥50% of their code via AI assistance**; new-engineer onboarding **60-90 days → ~40 days** (consistent with Anthropic data of 82→40 days, an **independent convergence**).

**Three emerging archetypes**: (1) **Copilot-only**: +10-25% on PRs, stable scope; (2) **Multi-tool**: story points **×1.5-3**, cross-repo scope **+50-80%**; (3) **Claude on corporate stack**: code volume **×4.5**, radically expanded scope. Counterintuitive insight: ***"AI lifts underperformers to baseline"*** rather than mainly accelerating top performers — the distribution **tightens from the bottom**. Senior architects **return to active development** after years away from it.

**Seven new AI products** (that did not exist before): Service Knowledge Hub (57 microservices, 83 releases/month), Mobile Android workflow CI, AI Agent Portal (2,085 users / 649 MAU in 87 days, MCP generation via OpenAPI), Shift-left Security Plugin (−82% exposed secrets), DevPortal Backstage + Kubernetes diagnostics agents (−68% critical incident resolution time), DRAIF MCP text-to-SQL Data Lake with 10,000 tables (embedding fine-tuned ×2 OpenAI), Call Evaluation (>97% accuracy, **voted best product in the Raiffeisen Bank International group**). Stability: **blocking incidents −70%, critical resolution −68%, high-severity security alerts resolved +155%**.

**Strategic pivot thesis**: ***"AI expanded our production possibility frontier, and we deliberately allocated the freed capacity"*** — toward features, stability, and technical-debt repayment. **Reframed evaluation question**: not *"by how much % did existing KPIs increase"* but ***"what did your engineers build that didn't exist before"***.

**Tie-in to the watch corpus**: numeric convergence around the committed median with Frizzo (2026-05-05), Wescale (2026-05-03), Curran/Intercom (2026-04-16), DORA 2025, Stanford Denisov-Blanch (2025-11-23). Independent convergence on ~40-day onboarding with Anthropic. Productive tension with Cherny / Curran top 5% / Karpathy (elite 10×+ tail): the distribution **tightens from the bottom** AND **widens at the top** — both readings coexist. Cross-cutting convergence on "the job is changing shape" with Frizzo, Karpathy, Mornati, Habert. To be used for banking/regulated-sector executive committees, transformation sponsors, and the productivity-distribution-equity debate.

## GrapheDeConnaissance

- Hryhorii Tatsyi —publie→ AI didn't make our engineers just faster (DOCUMENT, 0.97)
- Hryhorii Tatsyi —dirige→ Raiffeisen Bank Ukraine (ORGANISATION, 0.97)
- Raiffeisen Bank Ukraine —mesure→ ~900 ingénieurs IT (MESURE, 0.94)
- Raiffeisen Bank Ukraine —réduit→ effectif de 75 personnes (dont 64 ingénieurs) en 12 mois (CONCEPT, 0.95)
- Raiffeisen Bank Ukraine —mesure→ adoption IA de 62% à 83% (MESURE, 0.95)
- Onboarding Raiffeisen —mesure→ passage de 60-90 jours à ~40 jours (MESURE, 0.94)
- Onboarding ~40 jours —converge_avec→ données Anthropic 82→40 jours (CONCEPT, 0.92)
- Copilot-only, Multi-outils, Claude-on-corporate-stack —fait_partie_de→ Trois archétypes ingénieurs IA (CONCEPT, 0.95)
- Claude sur stack corporate —mesure→ volume code ×4.5 (MESURE, 0.93)
- Multi-outils —mesure→ story-points ×1.5-3 (MESURE, 0.93)
- Copilot-only —mesure→ PRs +10-25% (MESURE, 0.93)
- AI —améliore→ sous-performants (rattrapage à la baseline) (CONCEPT, 0.91)
- Retour des architectes seniors au développement actif —observé_dans→ Raiffeisen Bank Ukraine (ORGANISATION, 0.92)
- Raiffeisen Bank Ukraine —a_créé→ sept produits IA inédits en 12 mois (CONCEPT, 0.96)
- Service Knowledge Hub —s_applique_à→ 57 microservices, 83 releases/mois (CONCEPT, 0.94)
- AI Agent Portal —mesure→ 2 085 users / 649 MAU en 87 jours (MESURE, 0.94)
- AI Agent Portal —permet→ génération d'agents MCP via specs OpenAPI (TECHNOLOGIE, 0.94)
- Shift-left Security Plugin —réduit→ secrets exposés de 82% (CONCEPT, 0.93)
- DevPortal Backstage —réduit→ temps résolution incidents critiques de 68% (CONCEPT, 0.93)
- DRAIF MCP —s_applique_à→ Data Lake 10 000 tables en text-to-SQL (CONCEPT, 0.93)
- DRAIF MCP —surpasse→ modèles OpenAI (×2) (TECHNOLOGIE, 0.91)
- Call Evaluation —mesure→ >97% précision transcription (MESURE, 0.93)
- Groupe Raiffeisen (RBI) —recommande→ Call Evaluation (TECHNOLOGIE, 0.94)
- Incidents bloquants Raiffeisen —mesure→ −70% (MESURE, 0.94)
- Alertes sécurité haute sévérité résolues —mesure→ +155% (MESURE, 0.93)
- Hryhorii Tatsyi —affirme_que→ "AI expanded our production possibility frontier" (CITATION, 0.96)
- Capacité libérée par IA —s_applique_à→ features + stabilité + dette technique (allocation délibérée) (CONCEPT, 0.94)
- Hryhorii Tatsyi —recommande→ mesurer ce que les ingénieurs construisent qui n'existait pas (METHODOLOGIE, 0.95)
- Bilan Tatsyi —affine→ lecture "tail élite 10×" Cherny/Karpathy (CONCEPT, 0.9)
- Bilan Tatsyi —soutient→ resserrement par le bas et élargissement par le haut simultanés de la distribution productivité (CONCEPT, 0.92)
- Case study Raiffeisen —converge_avec→ témoignage individuel Frizzo (complémentarité) (CONCEPT, 0.93)
- Bilan Tatsyi —converge_avec→ Wescale X3-X4, Curran 3×, DORA 2025 (CONCEPT, 0.92)

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