An LLM reads your inbound update emails — from clients, clients, foremen, GCs — parses out the project-level changes, and writes them directly to the right row of your master tracker. This is the exact automation Qintova’s founder built and runs on the back office of a Mid-Michigan construction firm. From $7,500.
Every morning at a typical Mid-Michigan construction firm, the project coordinator opens 9 emails from the GC’s project team. Each one has a slightly different format. Each one touches 3 to 8 sites. Each one mentions project codes, milestones, schedule changes, delivery dates, engineering sign-off status, permit submittal, photo verification, and a dozen other fields that need to land in the master tracker as cell updates. Reading and parsing those 9 emails takes 45 minutes of focused work before he can do anything else. Updating the actual tracker cells — making sure the project code doesn’t auto-format as a date, making sure the column headers match today’s tracker version, making sure he doesn’t overwrite a change someone else made yesterday — takes another 90 minutes.
That’s before anything else happens in his day. The tracker is 2 hours behind reality at 9:30 AM. By the time he actually finishes the update at 10:45, the GC’s project team has sent another email with overnight changes. The tracker is perpetually a half-day behind the email pipeline. The owner looks at it Tuesday afternoon and sees Monday morning’s truth. The PM bases decisions on data that’s already wrong.
And when someone’s on PTO? The tracker drifts a full week. When a new email format lands — because the client changed their internal system or a new GC PM took over — the manual parsing time doubles. When two updates touch the same site, race conditions appear: which one is right? Did someone overwrite something? You don’t know until you audit, which nobody has time to do.
The work is mechanical. An LLM can do it. The exact pattern is already running in production at the founder’s day job: a Microsoft Graph API integration that monitors a specific sender’s mailbox, parses each inbound update into structured (site_id, field, old_value, new_value) tuples, and writes them to a multi-tab Excel tracker using openpyxl with a safe-save pattern. Exceptions get flagged. Everything else flows.
A 25-person subcontractor with a project coordinator spending 12 hours a week on email-to-tracker manual entry. Add the PTO drift, the data-quality cost, and the cost of leadership making decisions on stale data.
We typically eliminate 90%+ of the manual keying. The coordinator still reviews exceptions — about 15-25 minutes per day — but they go from being a typist to being a project coordinator again. The tracker stops lying to the rest of the company.
Microsoft Graph API or Gmail API with OAuth. Watches the specific senders or labels that carry your update emails. Pulls raw content into a queue. Built to survive auth refresh, rate limits, and the occasional bad attachment.
An LLM trained on your specific update format extracts the project-level changes. We document your tracker’s column taxonomy so ‘TPO Date’ in one email and ‘True PO Date’ in another both map to the same target column. Low-confidence parses flag for human review instead of writing wrong data.
The writer uses a tempfile + atomic move pattern so the tracker is never corrupted mid-write. It explicitly formats key columns (project codes, job numbers) to prevent Excel from auto-converting them to dates. It verifies the column headers before every write to handle the inevitable schema drift.
When the system can’t confidently apply an update, it lands in an exception inbox the coordinator reviews once a day in 5-10 minutes. Every write the system ever makes is logged with before/after values — so when someone asks ‘who changed this cell?’, you have an answer.
We pull 30 days of your real update emails (anonymized) and document the structure: senders, formats, field names, your tracker column conventions. We map every distinct field you receive to a target column in your tracker.
Graph or Gmail API integration. LLM parser trained on your taxonomy. Safe-save writer hitting a copy of your tracker. We shadow-run against the last 14 days of emails and compare bot output to your real tracker state.
Exception inbox built. Daily review session with the coordinator. Audit log running. We tune the confidence thresholds so the right items escalate.
Live in production against your real tracker. We watch every write for the first 5 business days alongside the coordinator. By end of week 2 it’s autonomous. 14 days of monitored support follows.
Essentials ($7,500) is one mailbox + one tracker — the right entry for a sub running a single client’s update-email pipeline. Standard ($18,000) handles multiple mailboxes (e.g. one feed from the GC, one from the client, one from foremen) with consolidated writes plus the exception dashboard. Turnkey ($42,000) is multi-tracker, multi-client, with custom field mappings per client and a unified review UI.
Yes. Microsoft Graph API is the cleanest path and the one we use most. We’ve also built against Gmail / Workspace, and we can read from a shared mailbox or a labeled folder. We don’t need to be on your domain — we authenticate as a delegated app.
That’s exactly the LLM strength. Generic rule-based parsers break when format changes. The LLM tolerates messy human formatting — abbreviations, missing punctuation, inline status changes mixed with chatty filler. As long as the meaning is in the email, the parser can pull it out. Low-confidence parses route to human review.
We don’t touch your tracker until you’ve seen the shadow-run results. We run against a copy for the first 14 days and you review the diffs. Cutover is when you’re comfortable, not before.
Two layers protect you. First, the parser is constrained to output only fields from your known taxonomy — it can’t invent column names. Second, low-confidence writes route to the exception queue instead of being applied. Worst case: an update gets delayed by an hour for human review. We’ve never had a wrong write in production.
Yes — that’s exactly the Essentials tier. One mailbox or one filter, one tracker. If it works (it will), Standard expands to multiple feeds. Most clients add the second feed within 90 days.
No. The pipeline runs on your tenant. We don’t store update emails, tracker state, or project data on Qintova infrastructure. The LLM call is the only external touch and it’s a stateless inference — nothing is retained provider-side beyond standard API logging windows.
Schema drift is inevitable in long-running trackers. The system verifies headers before every write and surfaces a clean error when columns have shifted. Most updates take 15-30 minutes to handle. The post-launch support window covers the first wave; ongoing schema work runs at $185/hr or under a monthly retainer.
Same pattern, different writer. We’ve built both. Google Sheets writes go through the Sheets API with the same safety guarantees (atomic batch updates, header verification, exception routing on conflict).
Three-minute assessment. Real coordinator-hours math. No sales call required.