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It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout.
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.
Field vehicle parking tickets were transcribed by hand — up to 30 minutes per ticket, prone to error, and diverting skilled staff from billable work.
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.
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.
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.
Three connected pillars replaced the manual stack, each one clickable for detail.
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 LookA 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 AIA 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.
The prep-time and admin-hours figures are measured outcomes from live engagements. The downtime figure is an industry benchmark for predictive maintenance.
Account managers now walk into every review with a live, pre-built Power BI report — no manual export or formatting required.
What condition-based alerts typically deliver once implemented — the outcome predictive intelligence could unlock here.
One agentic AI workflow cut ticket processing from 30 minutes to under one minute, decoupling admin cost from ticket volume.
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.
We map your FSM, CRM and ERP data landscape, current admin workflows and reporting cadence, and identify the highest-impact starting point.
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.
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.
Field staff transcribed scanned parking tickets by hand, ticket by ticket.
A multi-agent workflow parses scanned documents automatically; a human confirms before submission.
Account managers manually exported and formatted FSM data before every client meeting.
A master Power BI template filtered per client is ready before you walk in, risk register included.
PPM visits were scheduled by date, not condition — triggering ghost visits and missed failures.
Condition-based alerts flag at-risk assets one to two weeks ahead, with parts staged in advance.
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.
Let's talk about how a unified operational intelligence, predictive intelligence and agentic AI layer could work for your business.
Get in touch ›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.
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.
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.
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.
Performance data surfaces gaps in non-contracted services, turning the review from a backward-looking recap into a forward-looking conversation.
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.
Live sensor readings, service and maintenance records, fault and issue histories, and asset age and usage data — whatever you already have, we use.
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.
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.
Forecasting works without live telemetry. Where sensors aren't installed, historical service records and fault histories drive the same prediction.
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.
A multi-agent framework parses scanned parking tickets, extracts structured fields, and eliminates transcription errors — live today, running inside Microsoft Teams.
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.
Staff confirm or adjust AI outputs on one screen before final submission, preserving accuracy and trust on every agent we ship.
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.
