What this automation does
Bid leveling is reading. A drywall bid that is low because it excludes framing, firestopping and cleanup is not low. Finding that takes an estimator hours per trade, usually the night before the bid is due. This workflow does the reading and leaves the judgment.
- Collect. Proposals arrive in the estimating mailbox, a bid platform or Procore as PDFs, Excel bid forms, scanned faxes and two-line emails. Each is tagged to the project and bid package.
- Extract. A model pulls bidder, trade, base bid, alternates (add or deduct), unit prices, allowances, inclusions, exclusions, qualifications, addenda acknowledged, bond and insurance notes, schedule assumptions and price validity into one schema.
- Map to scope. For each bid package, the estimator’s scope sheet lists what the trade must carry. The model marks each scope item as included, excluded or silent for each bidder, with the exact wording from the proposal.
- Check against specs. The spec book is split by section number and indexed. Exclusions are checked against the spec sections assigned to that trade, so “excludes firestopping” gets flagged when your division 07 assignments put it in this package.
- Build the leveling sheet. An Excel sheet with bidders as columns and scope items as rows, base bid at the top, and every gap highlighted for a plug number.
- Flag. Missing addenda acknowledgments, expired validity, escalation clauses, items no bidder in any trade picked up (temporary protection, final cleanup, demolition of existing finishes), and bids far from the rest of the group.
- Draft clarifications. Short scope-clarification emails to each bidder, listing the silent items, ready for the estimator to edit and send.
The same pipeline runs on your own estimate before submission: it checks for spec sections with no matching line item, and compares cost per square foot by division with your past projects of a similar type.
When it is worth building
This is worth it for contractors who bid often, receive several proposals per trade, and keep finding scope gaps after award that turn into change orders or margin loss. It also helps when a small precon team covers more bids than it comfortably can.
It is less useful if most of your work is negotiated with a few trusted subs who use your bid form, or if your bid management platform already collects structured bid forms that subs fill in field by field. In that case the numbers are already structured, and AI only adds value on the free-text exclusions and qualifications. Start with a narrow version for that.
Architecture
| Layer | What we use | Why |
|---|---|---|
| Intake | The estimating mailbox in Outlook, Procore project documents, bid platform exports | Proposals arrive everywhere, so every source feeds one queue per package |
| Extraction | Claude with vision, or OpenAI’s GPT models, with a strict bid schema | Reads scanned and typed proposals without a template per subcontractor |
| Spec retrieval | Spec book split by section number into a pgvector index | Specs run to hundreds of pages, so the model needs only the sections for this trade |
| Scope mapping | A second model pass that must return a quote for every included or excluded call | No quote means “silent,” never a guess |
| Output | Excel leveling sheet in SharePoint | Estimators already level in Excel and can override anything |
| Orchestration | n8n or a small Python service | Parallel processing on bid day and re-runs when addenda land |
| History | Past leveling sheets and final costs | Gives context for outlier flags and cost-per-square-foot checks |
How to build it
- Collect three to five past bid packages with the proposals, your leveling sheets and what actually happened after award. These become the test set.
- Standardize the scope sheet. One template per trade listing what the package must carry, with spec section references. This is the most valuable step and does not need AI at all.
- Build extraction with the bid schema and run it on the test set until base bids, alternates and exclusions match what your estimators recorded.
- Index the spec book by section number and title, and record which sections belong to which bid package.
- Build scope mapping with the quote requirement, and verify in code that each quote appears in the proposal text.
- Generate the leveling sheet in your existing Excel layout so estimators do not have to learn a new format.
- Add flags and clarification drafts, then run one live bid in shadow mode next to your normal process before relying on it.
For the spec book index, the approach in our RAG knowledge assistant guide applies. After award, AI contract review can check subcontract terms against the same scope.
What breaks in production
- “Per plans and specs.” A bid that says only this is silent on every item, not inclusive of every item. Treat it as silent and send a clarification.
- Combined trades. One bidder prices mechanical and plumbing together. Split by scope item, not by total, or the leveling goes wrong.
- Alternates numbered differently from your bid form. Match on description, not number, and show both.
- Unit mix-ups. Unit prices in LF on one bid and SF on another. Normalize units and flag anything ambiguous.
- Late addenda. An addendum issued after bids arrive changes scope. Re-run mapping for that package and flag bidders who did not acknowledge it.
- Bad scans. Faxed proposals with handwritten adds. Low-confidence pages are flagged for the estimator, not dropped.
Timeline and cost
A first version for one estimating team usually takes four to eight weeks, mostly spent standardizing scope sheets and testing against past bids. Lumeor builds this in the range of $15,000 to $45,000. The low end is extraction and a leveling sheet for proposals from one mailbox. The high end adds spec book retrieval, Procore integration, historical cost checks and estimate QA. Model usage depends on proposal length and spec size, and is usually modest next to estimator hours. See the Procore tool page for API details and what a custom AI agent costs for scoping.
How to measure success
- Estimator hours per bid package leveled
- Bid packages fully leveled before the deadline
- Scope gaps found after award, and change orders traced to them
- Clarifications sent before bid day versus after award
- Share of AI flags that estimators marked as useful