Phenx
Use case · Controlled pricing decisions

Too high, you lose the job. Too low, you win it without the margin.

Every quote sits somewhere between those two losses. Operating rules, comparable historical work, and AI-assisted analysis make that tradeoff visible, so the small adjustments that decide the outcome are made on evidence — and an accountable person still sets the price.

Decision boundary

Inputs
Scope, specification, cost, location, and add-ons
AI decision support
Comparable work, price bands, and win probability
Expert judgment
Commercial tradeoff and exception review
Consequential control
A person sets and releases the quote

Anonymized implementation example

Controlled pricing, in motion

Win the work. Keep the margin.

See how scope, cost, and market inputs become one estimate record; how rules set the economic boundary; how AI compares the estimate with relevant historical work; and how moving the price up protects margin while moving it down improves the chance of winning — with an accountable manager deciding what price becomes the quote.

Phenx controlled pricing explainer00:58 · Narrated · Captions
The decision path

Move from estimate evidence to an approved commercial position.

Pricing becomes controlled when the inputs, comparison set, alternatives, exceptions, and final authority are visible instead of hidden in a spreadsheet.

01

Assemble

Bring scope, measurements, specifications, labor, materials, location, travel, and add-ons together.

02

Normalize

Allocate costs to the relevant sections and normalize historical pricing for time and market context.

03

Compare

Use AI to place the estimate among comparable work based on job type, size, specification, and location.

04

Evaluate

Review price bands, estimated win probability, price movement, and the current proposal position.

05

Decide and release

An estimator selects or overrides the price. The accountable manager approves the final quote.

Division of labor

Automate the analysis without automating commercial accountability.

Rules

Define the boundary

Establish required inputs, cost logic, policy limits, and conditions that cannot be bypassed.

AI

Structure the tradeoff

Find comparable work, identify the relevant pricing context, and estimate response across price bands.

People

Own the price

Interpret commercial context, challenge the comparison, override when necessary, and approve release.

Evidence

Make the decision reviewable

Preserve the estimate inputs, price position, alternatives considered, decision owner, and rationale.

Exception handling

Do not manufacture certainty when the comparison is weak.

  • Missing scope measurements stop the model from presenting a pricing comparison.
  • Unfamiliar job types, specifications, or markets require explicit expert review.
  • A price outside the modeled bands remains visible rather than being silently corrected.
  • Material changes to scope, cost, or specification require the pricing decision to be revisited.
  • The analysis never substitutes for estimator judgment or management release authority.
Operating improvement

Make pricing judgment more consistent and inspectable.

The goal is not a universal optimal price. It is a stronger commercial decision with clearer evidence, alternatives, ownership, and escalation.

01

Faster comparison

Bring cost, market, and comparable-work context into one decision surface.

02

Consistent context

Evaluate similar work using the same segmentation and normalization logic.

03

Visible tradeoffs

See how alternative price positions change the commercial decision.

04

Explicit authority

Keep override, approval, and quote release with accountable commercial owners.

Bring your decision

Which price still lives in a spreadsheet and one expert’s head?

In the fit call, we’ll discuss the inputs, cost boundary, comparable history, approval authority, exceptions, and commercial consequence. No documents or production access are required.