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FYLD

AI field operations platform that turns crew video, voice, and photos into structured safety and progress data

FYLD is a mobile app and cloud dashboard for utilities, heavy civils, and construction field teams. Crews record video, audio, and photos of jobsite work and hazards; FYLD's AI (NLP and computer vision) converts these into structured Video Risk Assessments, daily progress records, and real-time alerts so remote managers get live visibility without paper-based reporting.

$41M / £32M Series B (announced Feb 2026, led by Energy Impact Partners with participation from Partech's Growth Impact Fund and existing investor Ontario Teachers' Pension Plan). Prior disclosed rounds: £10M Series A (2022, led by Koru/Ontario Teachers' Pension Plan venture studio with corporate partner SGN) and a £12M round (late 2023, led by Ontario Teachers' Pension Plan) to fund global expansion. Total disclosed funding across all rounds is not independently confirmed by a single primary source; third-party trackers (Crunchbase) cited ~£26M raised as of April 2024, before the 2026 Series B. raisedGrowth Stage
Founded 2020 · Named customers/partners include SGN, National Grid, Southern Water, Yorkshire Water, Morrisons Water Services, Saesa, Emery Sapps, M Group, Ferrovial, Kier, Galliford, Colas, Amey, Ipsum, Clancy, Veolia, British Gas, and NCDOT (North Carolina DOT). Company reports 82% year-over-year growth entering 2026 and cites case-study results including a 35% increase in project completion rates and $1M in annual cost savings for SGN. Exact total customer/company count not publicly disclosed.
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WHY IT EXISTS

Why This Tool Exists

The Problem

Utilities, heavy-civils, and construction field teams typically document jobsite work, hazards, and progress on paper forms or disconnected photos, which gives remote managers and command centers no real-time visibility into site conditions. This delays intervention on emerging safety risks, slows manager spans of control across dispersed crews, and creates compliance/audit gaps because risk assessments and daily reports are reconstructed after the fact rather than captured live.

The Solution

Field crews use the FYLD mobile app to record short videos, audio narration, and photos describing the task, hazards, and controls in place; FYLD's AI engine (natural-language processing and computer vision) analyzes this unstructured input in real time to generate structured Video Risk Assessments (VRAs), flag hazards or missing controls the worker may have missed, and feed a cloud dashboard (FYLD Cloud) with live site status, alerts, and performance analytics for remote managers.

USAGE

How You Use It

Delivery Method
SaaSMobile App (Android/iOS)Web Dashboard (FYLD Cloud)
Integrations
Not publicly specified — FAQ states the platform connects via "analytics tools, cloud data services and open APIs" for ERP, EAM, GIS, and reporting-stack compatibility, but no named integration partners (e.g., specific ERP/GIS/EAM products) are disclosed on public sources
Disciplines
Construction, Civil Engineering, Facility Management
Workflows
Coordination & Delivery, Operations & Facility Management
Project Phases
Construction Administration
Project Types
Infrastructure, Industrial, Commercial
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Field video recordings, Voice/audio narration, Site photos
Optional:Text notes, Project/job setup data, Historical job data for resource planning
Formats:Video, Audio, Photo (JPG/PNG), Text
Output
What you get
Format:Structured Video Risk Assessments, real-time dashboards, and compliance/audit records
Fields:Video Risk Assessments (hazards, controls, flagged risks), Site/team progress status, Real-time manager alerts, Performance analytics by team/site, Compliance audit trail
Algorithm
How it works
Model:Proprietary AI engine combining natural-language processing and computer vision (specific model architecture/vendor not publicly disclosed); a Predictive Analytics Platform for forecasting operational/safety incidents was in development as of a 2023 OFGEM-funded initiative with SGN and National Grid
Accuracy:Not publicly specified — no independent accuracy benchmark published for hazard-detection or NLP analysis. Company case studies cite business-outcome metrics (e.g., "20-48% reduction in injuries and incidents," "75% reduction in time spent on risk assessments," ">6X ROI across all sectors") rather than model accuracy figures — these are vendor-reported and unaudited.
Privacy
How your data is protected
Retention:Personal data retained "as long as reasonably necessary to fulfil the purposes we collected it for," per the public privacy policy; retention may be extended during complaints or litigation. Specific retention periods vary by data type (e.g., cookies range from session-based to 2 years); no fixed retention period is stated for field video/audio/photo content.
Training:Not publicly specified — privacy policy does not explicitly address whether customer-submitted video, audio, or photo data is used to train FYLD's AI models.
Compliance:ISO 27001 (stated in privacy policy as an internal vendor-vetting requirement), SOC 2 Type II (stated in privacy policy as an internal vendor-vetting requirement), GDPR-compliant (lawful basis categories and data subject rights outlined in privacy policy), PCI-DSS compliant payment processing
API
Integration
Endpoint:Contact vendor for API access
Method:Not publicly documented — FAQ references "open APIs" for ERP/EAM/GIS/reporting-stack connectivity but does not publish endpoint, schema, or method details
USE CASES

Use Cases

  • ·Real-time Video Risk Assessments to replace paper-based jobsite hazard reporting
  • ·Remote site monitoring and manager span-of-control expansion across dispersed crews
  • ·Incident and near-miss management with automated hazard flagging
  • ·Compliance auditing and evidence trails for regulatory/traffic-management requirements
  • ·AI-driven job duration prediction and resource/team performance analysis for work scheduling
PRICING

Pricing

Free
No free tier disclosed; free demo available on request
Pro
Tailored subscription tiers based on use case and scale — exact pricing not publicly disclosed; company directs prospects to request a demo for a customized proposal
Enterprise
Custom enterprise contracts for large multi-site utilities/infrastructure deployments — pricing not publicly disclosed
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Research Sources & Data QualityLast verified: 2026-07-17
Data Sources (17)
Verified Data (12)
problem, solution, deliveryMethod, disciplines, yearFounded (2020, BCG X/Koru/Ontario Teachers' Pension Plan spinout), founders (Shelley Copsey, Anish Patel, Karl Simons), headquarters (London, UK), fundingTotal (Series A £10M, 2023 £12M round, Series B $41M/£32M), named customers/partners, compliance claims (ISO 27001, SOC 2 Type II, GDPR, PCI-DSS as stated in privacy policy), useCases, offline mobile capability
Not Found (8)
exact numeric pricing, named third-party integration partners (specific ERP/EAM/GIS products), public API documentation (endpoint, auth, rate limits), independent accuracy benchmark for hazard-detection AI, AI model architecture/vendor details, AI training-data usage policy for customer content, exact total customer/company count, data-center geographic location
Our Commitment: We only include verified data from official sources. If information isn't publicly available, we mark it as "Not publicly specified" rather than guessing.
ALTERNATIVES

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