# enliteAI vs Waterglass

> Two top-rated AI Development agencies in Vienna. Side by side: published rates, team size, service and industry coverage, and the curator's assessment of both — pairings are never sold.

Canonical page: https://www.aiagencies.eu/compare/enliteai-vs-waterglass
Generated: 2026-09-17 · Curated by Gabor Kiss · No paid placements — inclusion, score, and rank are never sold.

## Facts
| | enliteAI | Waterglass |
|---|---|---|
| Editorial score | 52.8/100 | 43.3/100 |
| Hourly rate | not published | not published |
| Team size | 10-49 | 10-49 |
| Location | Vienna, Austria | Vienna, Austria |
| Founded | 2017 | 2024 |
| Last reviewed | 2026-09-07 | 2026-08-04 |
| Services | AI Development, AI Agents, AI Consulting | AI Agents, AI Automation, AI Consulting, AI Development, Generative AI |
| Industries | Public Sector, Manufacturing | — |

## Our take on enliteAI
Reviewed 2026-09-07 by Gabor Kiss against published evidence.

The published evidence is research and product, not client delivery: enliteAI maintains Maze, an open-source applied reinforcement-learning framework, co-authored a 2025 paper with grid operator TenneT on power-grid topology control, and spun out Detekt, a GeoAI road-asset platform acquired by Cyclomedia in August 2025.

Fifteen client logos are published—A1, Andritz, Audi, Boehringer Ingelheim, EY, Erste Group, voestalpine and the City of Vienna among them—but no engagement is described anywhere on the site, so what was built for whom stays unstated apart from the 2019 municipal AI strategy for Vienna.

The energy work is presented as a methodology and a maturity model—a seven-step path toward autonomous digital twins for distribution grids—with integration claimed for Schneider Electric, Hitachi, Siemens and GE Vernova systems.

Best for grid operators and optimization problems where reinforcement learning is genuinely the method; a buyer who needs to read what a comparable engagement delivered will have to ask for it directly.

### Key strengths
- The method is checkable in public: a 2025 paper co-authored with grid operator TenneT on multi-objective grid topology control, plus first place in the 2022 L2RPN power-grid competition
- Maze, their applied reinforcement-learning framework, is on GitHub—you can read how they build agents before hiring them
- Detekt, the GeoAI platform they built and spun out, was acquired by Cyclomedia in 2025, which is shipped-product evidence even though it was their own venture

### Good to know
- No client case study is published anywhere on the site—ask for a written engagement summary with outcomes before shortlisting
- The energy work reads as a methodology and pilot framework rather than a live deployment—ask which grid operator is running it and at which maturity step
- Computer vision is still listed as a capability, but Detekt, the team behind it, was sold in August 2025—confirm what vision capacity remains in-house

## Our take on Waterglass
Reviewed 2026-08-04 by Gabor Kiss against published evidence.

Four named clients are published—InStaff, notus, HHLA Next and Heylog—but each is a single line rather than a case study, and the "read case study" links did not resolve at review.

The stronger evidence is the venture side, which is verifiable independently: Notehouse, a HIPAA-compliant case-management product used by 250+ organizations and acquired in 2025, and Pxl, a QR and microsite builder with 100 million interactions now being rebuilt as an agentic business.

The site is unusually straight with buyers—it states plainly that there is no Munich office and that on-site work means travel, which is the kind of answer most agencies bury.

Best for DACH businesses that want an AI agent built, run and hosted in Europe by a team that also builds and operates its own products.

### Key strengths
- Operates what it builds—monitoring, model changes and new requirements are named as an ongoing service, not a handover
- European hosting with model residency in Germany or the EU on request, documented per data flow
- Two acquired products of its own, so the AI-build claim is testable outside client references

### Good to know
- Client work is published as one-line summaries with no reachable case studies—ask for a reference you can speak to
- The company is recently formed and publishes no headcount; ask who would staff the build
- Vienna-based and open about it: on-site days in Germany mean travel, remote for the rest

## Which to choose
Choose enliteAI for:
- disclosed specialization in Public Sector / Manufacturing
- a higher overall editorial score

Choose Waterglass for:
- broader service offering — also covers AI Automation and Generative AI
- an overall editorial score of 43.3/100

## Questions buyers ask

### Which scores higher overall, enliteAI or Waterglass?
enliteAI scores 52.8/100, 9.5 points higher than Waterglass at 43.3/100. Both portfolio quality and business credibility feed into the score — see our methodology for the full breakdown.

### When should I consider both enliteAI and Waterglass?
Consider running parallel discovery briefs with enliteAI and Waterglass if your project spans multiple workstreams, you want competitive proposals to compare scope and approach, or you're undecided between the specialization angles each brings (see the Best for cards above). Most engagements ultimately go with one — but the parallel-brief phase is a low-cost way to validate fit.

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Source: AIAgencies.eu — the curated register of European AI agencies. Rate provenance: "agency-confirmed" = disclosed via submission or claimed profile; "directory-listed" = Clutch or two agreeing B2B directories (lone unverified sources are never written). Full method: https://www.aiagencies.eu/methodology · Rate index: https://www.aiagencies.eu/rates