> ## Documentation Index > Fetch the complete documentation index at: https://docs.vh3.ai/llms.txt > Use this file to discover all available pages before exploring further. # VH3 AI for BigChange users > How VH3 AI reads your BigChange history, connects your operation, and puts answers in front of your team # VH3 AI for BigChange users If your operation runs on BigChange, VH3 AI starts with the data your team already depends on: completed jobs, engineer visits, worksheets, customer records, job types, notes, and the shorthand your coordinators use every day. BigChange remains the system of record for dispatch, scheduling, job tracking, and worksheet capture. VH3 AI sits alongside it: reads your job history, connects every record to the right customer, site, and engineer, watches for patterns across thousands of jobs, and puts answers in front of your team before they go looking. Connect BigChange once and your operational history starts feeding search, sentinels, reports, Connie, and the workflows your team uses to follow through. ## We know what your data looks like We built VH3 AI on BigChange data from live field service operations before the product had a name. That matters because real operational data carries years of local decisions. Over years of operation, the shape of your data changes. Custom fields configured at go-live drift in meaning. Worksheet structures get updated and historical answers stop mapping cleanly to how the operation runs today. Contact hierarchies evolve. Job types get renamed or consolidated. The same underlying fault gets described differently by different engineers at different points in time. That is how field service operations actually run. VH3 AI has to understand the drift, resolve the entities, and keep historical records useful without changing the source system. We have processed hundreds of thousands of BigChange jobs across live customer operations. The enrichment pipeline was built around operational reality: messy notes, renamed job types, changing worksheet structures, and long-running customer histories. When your jobs land, VH3 AI extracts the fault type, structures the work performed, identifies equipment context, and links engineers, sites, customers, and history into one connected picture. ## Operational data quality over time Long-term intelligence needs a place to hold corrections, mappings, and operational interpretation above the raw API records. Timing data is particularly unreliable in mature operations: jobs that stayed open because of admin backlogs, completion timestamps that reflect paperwork processing time, SLA calculations distorted by open records that were never formally closed. A job type called "Reactive Maintenance" in 2021 and "Emergency Callout" in 2023 may be the same work. An open job from eighteen months ago may have been completed but never updated. BigChange correctly preserves what was recorded at the time. That immutability is valuable — and accurate long-term intelligence needs more on top of it. VH3 AI holds those corrections above the source system: resolved entity mappings, corrected timing data, unified job categories across historical periods, and open-job handling that reflects operational reality over administrative backlog. When those corrections live in a prepared operation, search, reports, sentinels, and Connie all read from the same reconciled picture — and the intelligence improves as your history grows. ## What building on BigChange data involves Dispatch, scheduling, job tracking, and worksheet capture are one engineering problem. Turning that data into useful intelligence — patterns spotted, precedents findable, risks flagged, briefs ready — is another. Any team that has attempted it directly knows the work involved. Data needs to be fetched, shaped, and normalised before it can be used analytically. Jobs need to be linked to engineers, sites, customers, and history. Similar faults need to be findable even when they were described differently. Patterns need to be watched continuously and routed to the people who own them. All of it has to stay coherent as the data grows and evolves. VH3 AI packages that foundation so your team starts with a prepared operation, ready to search, watch, and brief from day one. ## The right first step into AI for your operation Most organisations thinking about AI for field service are looking for a practical first step. They have heard pitches. They have seen demos. They are rightly cautious about committing to something that locks them into a vendor, requires a long implementation, or produces results six months from now that nobody is sure how to measure. The practical starting point is your own BigChange history. You connect your BigChange account. We process your history, build customer and job profiles, and connect the dots across jobs, engineers, sites, customers, and outcomes. Within hours, you have a working operational picture: job patterns structured, customer knowledge generated, sentinels monitoring your operation, reports running on schedule, and Connie ready to answer questions in plain English against your actual data. Sentinels watch continuously with near-zero AI cost at detection, so your team sees what changed without paying for a model call on every check. Connie's answers come with citations. The source of every number is traceable. Trust builds from evidence: job references, customer names, structured outputs, and reports your team can check. Start with one use case. The discovery sprint gives you a structured operational profile from recent history before you decide what to build next. ## What changes in day-to-day work The value shows up in ordinary operating rhythms: * The morning performance picture arrives before the standup. * A drifting customer account is flagged while there is still time to act. * Repeat visits and SLA clusters appear in sentinel digests during the month. * Engineers arrive on site with a briefing that includes relevant history and similar faults. * Account managers walk into reviews with a report built from the operational record. The same intelligence is available through the API for teams that want custom dashboards, automations, or partner-facing surfaces. New reports and workflows build on the prepared operation. If your stack changes later, your enriched history gives you a stable place to connect the next source system. If BigChange adds new useful fields, they can be ingested. If an acquisition brings another field system, VH3 AI can connect that history into the same picture. ## First week with your BigChange data A simple first-week loop helps the team see the value quickly: 1. Run one discovery search for a fault your team remembers well and inspect the top three precedents. 2. Open Connie on a real customer question and check the job references she cites. 3. Review the first sentinel digest and pick one finding to action. 4. Read the weekly or account report and compare it with the story your team already knows. 5. Open a case for one follow-through item if your tier includes cases and teams. See [Working with your operation](/guides/working-with-your-operation) for the broader operator playbook. ## Time to value Onboarding starts with a discovery sprint. We take a sample of your recent BigChange history and run it through the enrichment pipeline. Within a few days, you have a structured operational profile of your business: top fault categories, repeat failure patterns by site, engineer performance signals, customer risk flags, and the questions your data can answer today. The discovery sprint uses your data and produces an operational profile your team can inspect before a longer commitment. From there, full historical ingestion brings in your complete job history, typically two to three years. Live polling keeps your operation current as new jobs complete in BigChange. Enrichment compounds over time. The patterns that were invisible in raw exports become visible in the connected picture. Automation templates give your team useful workflows from day one: daily briefings, sentinel digest emails, Slack or Teams notifications when something crosses a threshold, report distribution, and more. They are running workflows that you extend when you are ready. ## What we support you with Our team understands BigChange operations from the inside. We know how to map your job types, your worksheet structures, your customer hierarchies, and your custom field configuration so the output is useful from day one — mapped, enriched, and ready to query. That initial configuration work is part of what you get. For automation and integrations, the pre-built n8n workflow templates give most operations a working foundation on day one. If your operation needs something different, we build it. We connect to the tools your team already uses: accounting, CRM, communication, scheduling. Those connections deepen the intelligence over time by bringing more operational context into the same picture. For teams that want to go further, the platform is fully open through the API. Your developers can query the prepared operation, call Connie programmatically, trigger reports, build custom dashboards, and connect the intelligence to any surface your team works from. The starter kits for coding agents, Cursor, and Claude give your technical staff a running start. We work closely with the operations we onboard so the platform becomes part of how the business runs — and your team can own it day to day. ## Related The full BigChange data surface available through VH3 AI: jobs, contacts, finance, worksheets, and more. How VH3 prepares your operational data before any question is asked. Watches your BigChange operation around the clock and surfaces patterns before they escalate. How to get the most from Connie, discovery, and sentinels day to day. The free n8n node and pre-built workflow templates for BigChange operations. For technical staff and developers: APIs, agents, and custom surfaces.