How a UK field services operator turned admin drag into a unified intelligence layer.
A specialist field services operator was losing hours every week to manual ticket transcription, two-hour report builds before every client review, and maintenance scheduled by calendar rather than condition. Inflexion Analytics deployed a unified operational intelligence, predictive intelligence and agentic AI layer — and admin time and review prep fell in the same quarter.
Growth was being paid for in admin hours, not margin.
Manual ticket processing
Field vehicle parking tickets were transcribed by hand — up to 30 minutes per ticket, prone to error, and diverting skilled staff from billable work.
Reporting effort
Account managers spent up to two hours before every client review manually exporting and formatting FSM data, with the risk register held in one engineer's head.
Maintenance by calendar
Fixed PPM schedules triggered ghost visits to healthy assets and left real failures to surface only after breakdown — at three to five times the cost of a planned visit.
Disconnected systems
FSM, CRM and ERP data didn't talk to each other, so reconciling a single contract's true profitability took an administrator days of manual work every month.
An emergency callout runs three to five times the cost of a planned visit, and unlogged parts, ghost visits and scope creep cost field service operators an estimated 5–15% of annual revenue. For a £3M operator, that's up to £450k a year quietly leaving the business.
One managed data layer, from live dashboard to autonomous agent.
Three connected pillars replaced the manual stack, each one clickable for detail.
Operational Excellence ›
A Power BI contract performance suite giving COOs and account teams a live, per-client view of reactive performance, PPM delivery and site-level risk — no manual rebuild between meetings.
Forward LookPredictive Intelligence ›
A predictive layer over the asset estate — sensor feeds, service histories and fault patterns turned into alerts one to two weeks ahead of failure, parts staged before the engineer arrives.
Agentic AIIntelligent Automation ›
A growing fleet of agents tackling high-friction admin: a live workflow that reads and validates scanned parking tickets end to end inside Microsoft Teams, a quote-automation agent in progress, and an expense-claims agent now underway — always with a human in the loop.
Two measured results. One industry benchmark for what's next.
The prep-time and admin-hours figures are measured outcomes from live engagements. The downtime figure is an industry benchmark for predictive maintenance.
Manual prep eliminated
Account managers now walk into every review with a live, pre-built Power BI report — no manual export or formatting required.
Potential reduction in unplanned downtime
What condition-based alerts typically deliver once implemented — the outcome predictive intelligence could unlock here.
Admin hours reclaimed monthly
One agentic AI workflow cut ticket processing from 30 minutes to under one minute, decoupling admin cost from ticket volume.
A structured engagement, scoped to your systems.
Timelines depend on your FSM, CRM and ERP landscape and how much data connection work is involved — so we scope each phase with you rather than quoting a generic calendar.
Discovery & assessment
We map your FSM, CRM and ERP data landscape, current admin workflows and reporting cadence, and identify the highest-impact starting point.
Build & configure
Data connections, ETL pipelines and the warehouse are established. From there, we prioritise your highest-impact reporting needs and automation workflows, which are then built, configured and handed over.
Operate & iterate
Inflexion hands over the full solution and remains available for ad hoc support. Regular review meetings identify new data sources, additional agents, and the next admin bottleneck to remove.
Structural problems mapped to concrete capabilities.
30 minutes per ticket
Field staff transcribed scanned parking tickets by hand, ticket by ticket.
Under a minute, agent-assisted
A multi-agent workflow parses scanned documents automatically; a human confirms before submission.
Two hours of prep per review
Account managers manually exported and formatted FSM data before every client meeting.
Live report, always current
A master Power BI template filtered per client is ready before you walk in, risk register included.
Maintenance on the calendar
PPM visits were scheduled by date, not condition — triggering ghost visits and missed failures.
Maintenance on the signal
Condition-based alerts flag at-risk assets one to two weeks ahead, with parts staged in advance.
See tomorrow's capacity gap today, not on Monday morning.
A 7–14 day forward-look heatmap of engineer capacity, sitting alongside contract SLA and margin — so dispatch can rebalance before the week even begins.
Ready to turn your field operations into a growth engine?
Let's talk about how a unified operational intelligence, predictive intelligence and agentic AI layer could work for your business.
Get in touch ›Operational Excellence.
The account team's live view. Every contract is filtered through one master Power BI template — reactive performance, planned maintenance and site-level risk, ready before you walk into the room. The report is always current; the rebuild effort is zero.
Reactive performance
Callouts by site, attendance SLA, first-time fix rate and average hours on site — filterable by client, site, date and category, and refreshed in near real-time.
PPM management
Planned maintenance visibility end to end: shifts by month, PPM value, FTEs planned versus required, and pipeline by stage from planned through to closed and invoiced.
Risk register
Site-level risk moves from phone notes and one engineer's memory into a structured page — owner, severity and status visible across the whole account team.
Cross-sell view
Performance data surfaces gaps in non-contracted services, turning the review from a backward-looking recap into a forward-looking conversation.
Predictive Intelligence.
The asset-level view. Sensor feeds, service histories and fault patterns become a live health score and a forecast, so engineers are dispatched on a signal, not a date. Failures are flagged one to two weeks before they happen.
Asset data
Live sensor readings, service and maintenance records, fault and issue histories, and asset age and usage data — whatever you already have, we use.
Predictive engine
Condition monitoring, anomaly detection, failure pattern analysis and parts demand forecasting turn raw asset data into a forward view, not a diagnosis after the fact.
Intelligent action
An alert one to two weeks before service is due, with the right parts staged in advance — one visit, job complete, SLA and margin protected.
Sensor-optional
Forecasting works without live telemetry. Where sensors aren't installed, historical service records and fault histories drive the same prediction.
Intelligent Automation.
The admin layer. A live multi-agent workflow already reads and validates scanned parking tickets end to end. A quote-automation agent and an expense-claims agent are in progress, built on the same pattern. A human confirms before anything is submitted — no new login, no retraining, no reformatting.
Document intelligence
A multi-agent framework parses scanned parking tickets, extracts structured fields, and eliminates transcription errors — live today, running inside Microsoft Teams.
Quote automation
Job photos and site notes are turned into a structured, client-ready quote draft — currently in build, using the same agent pattern proven on parking tickets.
Human-in-the-loop review
Staff confirm or adjust AI outputs on one screen before final submission, preserving accuracy and trust on every agent we ship.
Excel-ready output & what's next
Structured data exports directly into existing FSM workflows, no reformatting, no double entry. An expense-claims agent is next on the roadmap, built on the same foundation.