How EstimAI pricing works, in plain English

EstimAI is a quoting and pricing platform for trades. It helps you produce job prices, track actual outcomes, and improve future recommendations from your own business data.

Contractors, estimators, and office teams that want faster quotes with better control over margin and consistency.

No. You can start with manual inputs. QuickBooks just improves company-specific calibration and reconciliation.

No. Historical data helps confidence improve faster, but it is not required to begin quoting.

Set realistic monthly cost inputs, choose one pricing model, define a Job Minimum, and start logging completed jobs so calibration can improve.

Template pricing, All In Per Ft pricing, and Target Profit Margin pricing.

Fixed pricing logic based on rules you define. It is simple and consistent for teams standardizing process.

A single per-foot value that bundles material, labor, and overhead. Easy to operate, but less diagnostic when margins drift.

A margin-first model that estimates total cost and recommends a sell price to hit your margin goal.

Fixed pricing stays static until you edit rates. Target-margin pricing adapts as cost assumptions and company data change.

Many teams start with Template or All In Per Ft for stability, then move to Target Margin after tracking outcomes.

Target Profit Margin is usually best when your team consistently reconciles jobs and wants tighter control of realized margin.

Yes. EstimAI is built so you can evolve models as your operation matures.

You choose a target margin and EstimAI recommends a price that can support it based on estimated job cost.

DME is EstimAI's dynamic margin estimation layer. It blends your company baseline with job details and calibration signals.

Estimate cost, apply margin math, respect floor rules like Job Minimum, then return a recommended sell price.

Material assumptions, labor assumptions, and monthly overhead context are the biggest drivers.

You still may get a recommendation, but confidence drops and more settings remain review-only.

Because weak data plus full automation can damage margin. Review-first behavior is intentional.

Target margin is your normal goal, minimum margin is your floor, stretch margin is an upside target for strong opportunities.

High-impact settings stay human-reviewed when confidence is not strong enough for auto-apply.

Expected monthly material spend used for calibration context and reasonableness checks.

Recurring business costs not directly tied to one job, like admin, software, rent, and insurance.

Expected labor burden for the month. Accurate payroll context prevents labor-heavy underpricing.

Use QuickBooks-informed values when clean; use manual overrides when bookkeeping classification is incomplete.

Use practical manual baseline values now, then improve linkage and category hygiene over time.

Differences often come from date windows, mapping rules, or excluded categories used to keep pricing inputs clean.

The lowest total sell price allowed. It protects you from low-ticket jobs that still consume overhead and dispatch time.

Per-job overhead allocation used to spread monthly fixed burden across expected job count.

The monthly overhead estimate used as input for burden distribution and margin calibration.

Expected monthly production footage. It helps stabilize per-foot cost assumptions.

Where your monthly footage estimate comes from (manual, historical, or synced signal). Source quality affects confidence.

How labor is estimated in pricing math, such as fixed per-foot logic or payroll-informed methods.

These are core product-specific material assumptions. If they are too low, realized margin usually compresses.

Because EstimAI intentionally keeps high-leverage changes in review-only mode when evidence is weak.

Expense and job-cost context that supports pricing calibration and reconciliation workflows.

Company-specific calibration, realized margin analysis, and estimate-vs-actual comparison.

Not every accounting line automatically becomes pricing input. Some categories remain excluded until mapping is reliable.

Linked expenses improve job-level truth. Unlinked expenses reduce attribution quality and lower recommendation confidence.

The same expense linked twice can overstate cost and distort realized margins.

Some expenses are shared overhead or lack enough job metadata for safe auto-linking.

Use consistent customer/job naming, clean memos, and frequent reconciliation instead of month-end pileups.

No. EstimAI uses sync windows and cached snapshots for reliability and performance.

The analysis layer that compares estimate-time assumptions vs realized results.

A quality signal based on data completeness, linkage quality, and historical consistency.

Direction and urgency for potential pricing changes. Better data yields stronger signal quality.

The actual margin achieved after real costs and collections, not the estimate-time projection.

The gap between expected margin and realized margin; used to diagnose where pricing assumptions are leaking.

Scope changes, overtime, callbacks, procurement volatility, and missing links all cause divergence.

Sparse completed jobs, poor linkage coverage, unstable settings, and missing payroll/material context.

Use reconciliation screens to match by date, customer context, memo cues, and known work history.

Yes. Most jobs should include multiple linked costs across materials, labor, and support items.

You can link it later. Backfilling is normal and still improves calibration quality.

Yes. Business Insight surfaces coverage gaps so your team can reconcile later.

Job cost gets overstated and margin can look worse than reality. Duplicate-risk checks exist to prevent this.

A suggested expense-job pair with strong similarity signals. It is a recommendation, not an irreversible auto-link.

No. Your data calibrates your company baseline; it is not treated as a universal market price book.

Company baseline is your internal economics. Market pricing is outside demand behavior. EstimAI prioritizes your baseline first.

Because pricing is high-impact. Review-first design avoids sudden margin shocks from low-confidence automation.

When confidence is high, linkage coverage is solid, and the signal repeats across enough completed jobs.

When market conditions shift fast, scope is unusual, or data coverage is thin for the recommendation.

Usually mapping gaps or missing source data. Set a manual baseline and clean mappings in parallel.

Date-window differences, excluded categories, and normalization rules often explain the gap.

Most often because of sparse history, weak linkage coverage, or inconsistent settings.

Likely inconsistent job metadata, weak memos, or no reconciliation pass yet.

Those settings are high leverage, so EstimAI requires human confirmation unless confidence is strong.

Possible permission, mapping, or period-filter issue. Use a manual payroll baseline while fixing the data source.

Too many completed jobs are missing linked cost records; reconciliation frequency needs to increase.