ADAION

Name: ADAION
Brand: ADAION

Data Driven Cloud Based Platform that digitizes your grid to pave the way to a smart and sustainable future.

LinkedIn 920|Website|Web traffic 1K|FTEs 21|HQ Churriana de la Vega, Andalusia, ES

Published Jun 22, 2026

01Common complaints

1) If you require publicly documented security certifications prior to vendor evaluation - If procurement requires SOC 2 or ISO certifications before engagement and the vendor cannot provide them under NDA, larger incumbents with established certifications may be a safer choice. 2) If you need a large volume of third-party peer reviews - ADAION has limited visibility on major review platforms; organizations that require extensive peer benchmarking may prefer vendors with a larger public review footprint. 3) If your data foundation is weak - Without reasonably high-quality GIS and AMI data, digital-twin outputs will likely be unreliable regardless of vendor technology. 4) If your primary need is corporate investment planning rather than grid-physics planning - Solutions focused on portfolio investment optimization (e.g., Copperleaf) may be a better match for investment planning use cases. 5) If you want a single incumbent suite - Incumbent vendors such as Siemens, Schneider, or GE may reduce vendor sprawl when the customer already runs parts of their stack and prefers fully integrated contracts and support.

Deal disqualifiers (poor fit)

No AMI or unreliable meter data access
Procurement that requires published SOC 2 / ISO certifications before engagement
A requirement for a single-vendor suite tightly coupled to an existing ADMS vendor with no appetite for additional platforms

Publicly evident limitations

Security and compliance transparency: Certifications and a detailed enterprise security feature list are not publicly documented in accessible sources.
Review footprint: Minimal third-party product reviews were found; the G2 category listing showed 0 ratings at the time of access.
Case-study verification: The available metrics are largely vendor-published; independent validation is limited in the public record.

Roadmap / upcoming features

No official product roadmap with release dates was found in accessible sources.

Potential scale limitations (unknown)

No public benchmarks were found for:

Maximum feeder count or model size supported
Simulation throughput for large deployments
Latency for near-real-time monitoring scenarios

02Why they buy

Unique selling points supported by accessible evidence

Utility case study (Iberdrola) that includes a named stakeholder quote and vendor-published performance claims related to planning speed and site identification.
Focus on LV planning and congestion/voltage visibility, as reflected in the E.DSO proof-of-concept description (integration of GIS and smart meter data; digital twin; hosting capacity assessments).

Unique selling points that require independent verification

Vendor-stated metrics such as "+5X productivity" and "+30 grids optimized" lack disclosed methodology or independent validation and should be verified during vendor due diligence.
Claims about simplified interconnection workflows (for example, two-action or "two-click" workflows) should be validated for specific regulatory jurisdictions and back-office integration requirements.

Where competitors may have the advantage

Incumbents (Siemens, Schneider, GE) can offer more established enterprise procurement readiness, integration breadth, and global support.
Regionally established specialists (e.g., envelio) may be more embedded in specific connection-determination workflows and local market practices.

03Field reports

Review-platform coverage (G2 / Gartner Peer Insights / TrustRadius)

- G2: ADAION appears listed in G2’s "Emerging AI Software" category but showed no ratings on the category page at the time it was accessed. The category/company listing does not provide substantive product reviews. - Gartner Peer Insights: No ADAION listing or reviews were found via accessible search results during this run. - TrustRadius: No ADAION product review page was found via accessible search results during this run. Consequence: There is insufficient third-party review volume to produce a standard star-rating synthesis for ADAION specifically.

Accessible customer-style evidence (vendor case study + third-party success case)

Because major review-platform coverage is limited, this report relies on: - A vendor-published case study (Iberdrola) - A third-party association success case (E.DSO) - Investor/portfolio writeups #### Vendor-published positive evidence (quotes) 1) Vendor case study testimonial (Iberdrola i-DE): > "What previously took us months to prepare the Benidorm Urban Mobility Plan using current tools and procedures, we can now do with Adaion in a small fraction of the time" — Carlos Ojeda, Head of Alicante Area, i-DE (Iberdrola Group) 2) Third-party association success case (E.DSO): - E.DSO reports UFD collaborated with ADAION on low-voltage (LV) planning; following a proof of concept across multiple LV networks, UFD was considering scaling the solution. #### Negative evidence (what is missing / reasons buyers hesitate) Rather than negative reviews, the primary negative signals in accessible sources are omissions and unverifiable claims: - The vendor publishes time-to-value and productivity metrics (e.g., vendor-stated very rapid workflows and multi-fold productivity improvements), but methodology and independent validation are not disclosed. - Lack of public security/compliance documentation could slow procurement in regulated utilities. - Limited public review footprint reduces the ability of buying committees to validate support quality, implementation experience, and operational reliability.

Why customers choose ADAION (based on available evidence)

From the Iberdrola and E.DSO narratives, selection drivers likely include:

Shortening planning cycle time for LV hosting capacity, EV charging, and DER integration planning
Converting fragmented GIS and AMI data into a usable network model/digital twin
Automating constraint detection and surfacing reinforcement recommendations

Why some customers might leave / churn (inferred from category & typical patterns)

No public churn stories were found. Typical churn risks for this category include:

Integration complexity and data quality challenges
Preference for vendors already embedded in ADMS/GIS ecosystems
Security and procurement friction
Model trust issues (operators may demand transparent physics validation and explainability)

1) Iberdrola (i-DE) — EV charging deployment planning

Source: ADAION vendor case study. Claimed challenge: Plan installation of a substantial number of EV charging points in a mid-sized city (vendor-published scale). Claimed results (vendor-published): - Reduced planning time from several months to minutes (vendor description) - Identified a notably higher number of viable charging locations without additional investment (vendor-published figure) Customer quote (vendor-published): > "What took us months… we can now do it with Adaion in minutes" — Carlos Ojeda, i-DE. Critical evaluation / methodology gaps: - It is unclear whether the initial multi-month timeframe refers to total program time (data collection + analysis + approvals) or to a specific computational planning step. - The reported increase in viable locations lacks detail on the baseline methodology, constraints considered, and whether identified locations were ultimately implemented. - The vendor also uses a hyperbolic numeric operations claim in marketing materials; this is not an analytically meaningful performance metric without further explanation of operations, compute context, and measurement methodology.

2) UFD (LV Advanced Planning) — proof of concept across dozens of LV networks

Source: E.DSO success case. What is stated: - UFD collaborated with ADAION to implement a solution providing visibility over LV voltage behavior and congestion. - The solution integrates GIS and smart meter / field measurements, builds a digital twin, and supports monitoring and hosting capacity assessment. - After a proof of concept across dozens of LV networks, UFD was considering scaling the solution. Critical evaluation: - This is stronger than a purely vendor-authored case study because it is published by an industry association; however, it still lacks detailed operational metrics (accuracy improvements, planner hours saved, reduction in customer voltage events, etc.).

04Best fit

Company size

Likely best fit: Mid-to-large electric distribution network operators with LV visibility challenges and active DER/EV integration programs.
Practical range: DSOs managing large-scale endpoint/meter deployments.

Industries / verticals

Electric DSOs / utilities (regulated or partially deregulated markets)
Grid modernization and flexibility programs focusing on DER integration and EV charging expansion

Roles and personas

VP/Director of Grid Planning
Head of LV/MV Planning
Interconnection / hosting capacity teams
Reliability engineering / power quality teams
Utility digital transformation and data platform teams

Pain points addressed

Low visibility in LV networks
Manual and slow planning cycles
Difficulty assessing hosting capacity and congestion
Regulatory reporting needs (as stated by the vendor)

Qualification criteria (strong fit)

The utility has a usable GIS and access to AMI/smart meter streams
There is a near-term business driver: EV rollout, PV hosting capacity assessments, or LV voltage events
The utility is willing to evaluate a new workflow layer in addition to legacy tools

05The pitch

What the product does (objective value proposition)

ADAION is a B2B software platform for electric distribution grid operators (DSOs/utilities). It is designed to support grid planning and operations through a combination of: - A data integration layer (e.g., GIS and AMI/smart meter data) - A continuously updated digital twin of the distribution network - Simulation / power-flow style analysis and AI-assisted diagnostics - A conversational, AI-assisted interface for planners to query grid status, run analyses, and receive recommendations The vendor describes the product as a conversational assistant for grid planning intended to help automate and accelerate current-state decision-making. The vendor's public materials reference regulatory reporting readiness (hosting capacity, connection, loss reporting), anomaly/constraint detection, and scenario-based recommendations.

Target market and market positioning

Primary users: Distribution utility planning teams; grid operations and reliability teams; interconnection / hosting capacity analysts.
Primary buyers: DSO leadership, grid planning directors, and digital transformation leaders.
Positioning: A software layer based on a digital twin and analytics that can operate alongside legacy planning tools and workflows.

Get a comprehensive analysis of ADAION including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Jun 22, 2026

01Common complaints

If you require publicly documented security certifications prior to vendor evaluation
- If procurement requires SOC 2 or ISO certifications before engagement and the vendor cannot provide them under NDA, larger incumbents with established certifications may be a safer choice.
If you need a large volume of third-party peer reviews
- ADAION has limited visibility on major review platforms; organizations that require extensive peer benchmarking may prefer vendors with a larger public review footprint.
If your data foundation is weak
- Without reasonably high-quality GIS and AMI data, digital-twin outputs will likely be unreliable regardless of vendor technology.
If your primary need is corporate investment planning rather than grid-physics planning
- Solutions focused on portfolio investment optimization (e.g., Copperleaf) may be a better match for investment planning use cases.
If you want a single incumbent suite
- Incumbent vendors such as Siemens, Schneider, or GE may reduce vendor sprawl when the customer already runs parts of their stack and prefers fully integrated contracts and support.

Deal disqualifiers (poor fit)

No AMI or unreliable meter data access
Procurement that requires published SOC 2 / ISO certifications before engagement
A requirement for a single-vendor suite tightly coupled to an existing ADMS vendor with no appetite for additional platforms

Publicly evident limitations

Security and compliance transparency: Certifications and a detailed enterprise security feature list are not publicly documented in accessible sources.
Review footprint: Minimal third-party product reviews were found; the G2 category listing showed 0 ratings at the time of access.
Case-study verification: The available metrics are largely vendor-published; independent validation is limited in the public record.

Roadmap / upcoming features

No official product roadmap with release dates was found in accessible sources.

Potential scale limitations (unknown)

No public benchmarks were found for:

Maximum feeder count or model size supported
Simulation throughput for large deployments
Latency for near-real-time monitoring scenarios

02Why they buy

Unique selling points supported by accessible evidence

Utility case study (Iberdrola) that includes a named stakeholder quote and vendor-published performance claims related to planning speed and site identification.
Focus on LV planning and congestion/voltage visibility, as reflected in the E.DSO proof-of-concept description (integration of GIS and smart meter data; digital twin; hosting capacity assessments).

Unique selling points that require independent verification

Vendor-stated metrics such as "+5X productivity" and "+30 grids optimized" lack disclosed methodology or independent validation and should be verified during vendor due diligence.
Claims about simplified interconnection workflows (for example, two-action or "two-click" workflows) should be validated for specific regulatory jurisdictions and back-office integration requirements.

Where competitors may have the advantage

Incumbents (Siemens, Schneider, GE) can offer more established enterprise procurement readiness, integration breadth, and global support.
Regionally established specialists (e.g., envelio) may be more embedded in specific connection-determination workflows and local market practices.

03Field reports

Review-platform coverage (G2 / Gartner Peer Insights / TrustRadius)

G2: ADAION appears listed in G2’s "Emerging AI Software" category but showed no ratings on the category page at the time it was accessed. The category/company listing does not provide substantive product reviews.
Gartner Peer Insights: No ADAION listing or reviews were found via accessible search results during this run.
TrustRadius: No ADAION product review page was found via accessible search results during this run.

Consequence: There is insufficient third-party review volume to produce a standard star-rating synthesis for ADAION specifically.

Accessible customer-style evidence (vendor case study + third-party success case)

Because major review-platform coverage is limited, this report relies on:

A vendor-published case study (Iberdrola)
A third-party association success case (E.DSO)
Investor/portfolio writeups

Vendor-published positive evidence (quotes)

Vendor case study testimonial (Iberdrola i-DE):

"What previously took us months to prepare the Benidorm Urban Mobility Plan using current tools and procedures, we can now do with Adaion in a small fraction of the time" — Carlos Ojeda, Head of Alicante Area, i-DE (Iberdrola Group)

Third-party association success case (E.DSO):

E.DSO reports UFD collaborated with ADAION on low-voltage (LV) planning; following a proof of concept across multiple LV networks, UFD was considering scaling the solution.

Negative evidence (what is missing / reasons buyers hesitate)

Rather than negative reviews, the primary negative signals in accessible sources are omissions and unverifiable claims:

The vendor publishes time-to-value and productivity metrics (e.g., vendor-stated very rapid workflows and multi-fold productivity improvements), but methodology and independent validation are not disclosed.
Lack of public security/compliance documentation could slow procurement in regulated utilities.
Limited public review footprint reduces the ability of buying committees to validate support quality, implementation experience, and operational reliability.

Why customers choose ADAION (based on available evidence)

From the Iberdrola and E.DSO narratives, selection drivers likely include:

Shortening planning cycle time for LV hosting capacity, EV charging, and DER integration planning
Converting fragmented GIS and AMI data into a usable network model/digital twin
Automating constraint detection and surfacing reinforcement recommendations

Why some customers might leave / churn (inferred from category & typical patterns)

No public churn stories were found. Typical churn risks for this category include:

Integration complexity and data quality challenges
Preference for vendors already embedded in ADMS/GIS ecosystems
Security and procurement friction
Model trust issues (operators may demand transparent physics validation and explainability)

1) Iberdrola (i-DE) — EV charging deployment planning

Source: ADAION vendor case study.

Claimed challenge: Plan installation of a substantial number of EV charging points in a mid-sized city (vendor-published scale).

Claimed results (vendor-published):

Reduced planning time from several months to minutes (vendor description)
Identified a notably higher number of viable charging locations without additional investment (vendor-published figure)

Customer quote (vendor-published):

"What took us months… we can now do it with Adaion in minutes" — Carlos Ojeda, i-DE.

Critical evaluation / methodology gaps:

It is unclear whether the initial multi-month timeframe refers to total program time (data collection + analysis + approvals) or to a specific computational planning step.
The reported increase in viable locations lacks detail on the baseline methodology, constraints considered, and whether identified locations were ultimately implemented.
The vendor also uses a hyperbolic numeric operations claim in marketing materials; this is not an analytically meaningful performance metric without further explanation of operations, compute context, and measurement methodology.

2) UFD (LV Advanced Planning) — proof of concept across dozens of LV networks

Source: E.DSO success case.

What is stated:

UFD collaborated with ADAION to implement a solution providing visibility over LV voltage behavior and congestion.
The solution integrates GIS and smart meter / field measurements, builds a digital twin, and supports monitoring and hosting capacity assessment.
After a proof of concept across dozens of LV networks, UFD was considering scaling the solution.

Critical evaluation:

This is stronger than a purely vendor-authored case study because it is published by an industry association; however, it still lacks detailed operational metrics (accuracy improvements, planner hours saved, reduction in customer voltage events, etc.).

04Best fit

Company size

Likely best fit: Mid-to-large electric distribution network operators with LV visibility challenges and active DER/EV integration programs.
Practical range: DSOs managing large-scale endpoint/meter deployments.

Industries / verticals

Electric DSOs / utilities (regulated or partially deregulated markets)
Grid modernization and flexibility programs focusing on DER integration and EV charging expansion

Roles and personas

VP/Director of Grid Planning
Head of LV/MV Planning
Interconnection / hosting capacity teams
Reliability engineering / power quality teams
Utility digital transformation and data platform teams

Pain points addressed

Low visibility in LV networks
Manual and slow planning cycles
Difficulty assessing hosting capacity and congestion
Regulatory reporting needs (as stated by the vendor)

Qualification criteria (strong fit)

The utility has a usable GIS and access to AMI/smart meter streams
There is a near-term business driver: EV rollout, PV hosting capacity assessments, or LV voltage events
The utility is willing to evaluate a new workflow layer in addition to legacy tools

05The pitch

What the product does (objective value proposition)

ADAION is a B2B software platform for electric distribution grid operators (DSOs/utilities). It is designed to support grid planning and operations through a combination of:

A data integration layer (e.g., GIS and AMI/smart meter data)
A continuously updated digital twin of the distribution network
Simulation / power-flow style analysis and AI-assisted diagnostics
A conversational, AI-assisted interface for planners to query grid status, run analyses, and receive recommendations

The vendor describes the product as a conversational assistant for grid planning intended to help automate and accelerate current-state decision-making. The vendor's public materials reference regulatory reporting readiness (hosting capacity, connection, loss reporting), anomaly/constraint detection, and scenario-based recommendations.

Target market and market positioning

Primary users: Distribution utility planning teams; grid operations and reliability teams; interconnection / hosting capacity analysts.
Primary buyers: DSO leadership, grid planning directors, and digital transformation leaders.
Positioning: A software layer based on a digital twin and analytics that can operate alongside legacy planning tools and workflows.

Get a comprehensive analysis of ADAION including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →