Visia AI

Name: Visia AI
Brand: Visia AI

AI Native

Recycling's Material Intelligence Platform: multimodal AI that helps operators increase throughput and improve safety.

Est 2020|Valuation $32.9M|Raised $12.1M|10 Ref Customers

LinkedIn 2K|Website|FTEs 26|HQ New York, New York, US

MarketsHealth & Safety

01Competitive Landscape

Narrative analysis

- Areas where Visia shows strengths based on available information: - Integration of detection hardware with operator workflows (for example, laser pointer guidance for manual removal). - Vendor emphasis on deployment support and on-site customization for different facility streams. - Areas where alternatives may be stronger: - Established OEMs (e.g., TOMRA, Pellenc ST) may have longer track records for large-scale sorting equipment, broader service footprints, and standardized maintenance models. - Focused battery detection vendors may offer more narrowly scoped solutions with clearer procurement KPIs for battery-fire-specific initiatives.

Market adoption and trend context

Trade press coverage highlights increasing concern about battery-related fires and regulatory attention. This trend can increase buyer interest in upstream hazard detection investments, but does not validate specific vendor efficacy.

	Visia AI	Battery Detection Solutions (BDS)	TOMRA Recycling (sensor sorting)
Starting Price	Quote-based (not public)	Quote-based (not public)	Quote-based (not public for industrial sorting)
Primary use case	Recycling operators needing multi-sensor hazard detection, material composition analytics, and operational workflow integration	Facilities focused specifically on lithium-ion battery detection and removal via X-ray inspection	Facilities modernizing sorting lines for material separation (plastics/metals) with established OEM hardware and service models
Key strength (vendor-reported)	Combination of multi-sensor hardware, software analytics, and operational workflow features such as laser guidance; vendor-published support responsiveness	Single-purpose framing for battery detection with X-ray inspection capabilities and simpler procurement scope	Mature product portfolio (XRT, AI-enhanced sorting) and large installed base with established service networks
Key weakness / buyer concern	Limited independent third-party reviews publicly available; performance and security details require validation; custom deployments can be complex	Scope may be narrower (battery-only) than full material intelligence needs	May not address upstream hazard detection as a unified platform; retrofit and integration complexity can still be present

01Competitive Landscape

Narrative analysis

Areas where Visia shows strengths based on available information:
- Integration of detection hardware with operator workflows (for example, laser pointer guidance for manual removal).
- Vendor emphasis on deployment support and on-site customization for different facility streams.
Areas where alternatives may be stronger:
- Established OEMs (e.g., TOMRA, Pellenc ST) may have longer track records for large-scale sorting equipment, broader service footprints, and standardized maintenance models.
- Focused battery detection vendors may offer more narrowly scoped solutions with clearer procurement KPIs for battery-fire-specific initiatives.

Market adoption and trend context

Trade press coverage highlights increasing concern about battery-related fires and regulatory attention. This trend can increase buyer interest in upstream hazard detection investments, but does not validate specific vendor efficacy.

	Visia AI	Battery Detection Solutions (BDS)	TOMRA Recycling (sensor sorting)
Starting Price	Quote-based (not public)	Quote-based (not public)	Quote-based (not public for industrial sorting)
Primary use case	Recycling operators needing multi-sensor hazard detection, material composition analytics, and operational workflow integration	Facilities focused specifically on lithium-ion battery detection and removal via X-ray inspection	Facilities modernizing sorting lines for material separation (plastics/metals) with established OEM hardware and service models
Key strength (vendor-reported)	Combination of multi-sensor hardware, software analytics, and operational workflow features such as laser guidance; vendor-published support responsiveness	Single-purpose framing for battery detection with X-ray inspection capabilities and simpler procurement scope	Mature product portfolio (XRT, AI-enhanced sorting) and large installed base with established service networks
Key weakness / buyer concern	Limited independent third-party reviews publicly available; performance and security details require validation; custom deployments can be complex	Scope may be narrower (battery-only) than full material intelligence needs	May not address upstream hazard detection as a unified platform; retrofit and integration complexity can still be present