Winner, 2024 Snowflake Startup Challenge

DECISIONS · FRANCHISE

Give every franchisee the intelligence behind the decision.

Every franchisee with the power of Decision Intelligence

SignalFlare puts brand intelligence and decision modeling in every franchise group’s hands, so each operator can make the right move for the market they know best.

Talk to Sales

WHAT OPERATORS GET

Everything you need to make the right move for your market.

Pricing, menu, and promotion decisions, informed by what’s happening in each operator’s own trade areas.

BRAND INTELLIGENCE

Know what’s happening in your market.

Start with the question every operator asks: is it me, or the market? Then see traffic, spend, guest sentiment, and competitive activity for every trade area, alongside each store’s own sales.

YOUR STORES · THIS WEEK

Downtown

Lunch demand rising

OPPORTUNITY

Airport Rd

New competitor nearby

WATCH

Lakeside

Spend holding steady

STEADY

AI AGENTS

A second analyst on your team.

AI agents dig through your stores’ data with you to test a hunch and build the case for a change.

DECISION MODELING

Model a move before you make it.

The same decision modeling tools the brand uses, for pricing, menu, and promotion changes in your own stores.

BUILT FOR BOTH SIDES

Brand-approved. Operator-owned.

AI advises. People decide. Every recommendation is signed off by the brand before it goes out, and every operator chooses what to do with it.

RECOMMENDATION

BRAND REVIEW

FINANCE

EXECUTIVE SIGN-OFF

OPERATOR DECIDES

THE OPERATOR

Get recommendations built for their own stores.

Have an AI agent walk them through the decision and every option.

Accept, adjust within range, or send back a question.

See only their own stores, with no one else’s numbers in view.

THE BRAND

Approve recommendations before they go out.

Set the rules and ranges each group works within.

See how every recommendation lands across the system.

Answer every operator’s questions in one place.

EXAMPLE · A PRICING ROUND

From approval to submission, on one schedule.

01

Approve

The brand approves the recommendations and sets each group’s range.

02

Distribute

Each franchise group gets recommendations for its own stores.

03

Model

Operators dig into the signals and model changes within range.

04

Submit

At the deadline, saved edits and untouched recommendations go in.

See how SignalFlare handles pricing decisions →

See SignalFlare's approach to pricing →

“

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FROM A FRANCHISE SYSTEM

I have worked with all the major pricing solutions in the industry and SignalFlare.ai is the most accurate, actionable and cost effective.

I have worked with all the major pricing solutions in the industry and SignalFlare.ai is the most accurate, actionable and cost effective.

I have worked with all the major pricing solutions in the industry and SignalFlare.ai is the most accurate, actionable and cost effective.

JORGE ZAIDAN · VP STRATEGY, ARB

IN NAVIGATOR

NAVIGATOR

Let every operator test their own hunch.

Let every operator test their own hunch.

Franchisees work with Navigator’s AI agents on their own stores’ data, testing a pricing, menu, or promotion idea against what’s happening in their market before they commit.

Franchisees work with Navigator’s AI agents on their own stores’ data, testing a pricing, menu, or promotion idea against what’s happening in their market before they commit.

Meet Navigator

AS
SignalFlare AI
/
Mission Hill Group
/
Q3 pricing — SignalFlare Cafe
/ Scenario review
Modeled data
Project · 4 locations · updated Aug 6
Q3 pricing — SignalFlare Cafe
Everything behind the Q3 price decision: the modeled POS, the trade-area demand pull, last quarter's committee notes, and the reasoning from every chat in here.
Chats
Q2 read — what the last increase actually did JUL 22
Trade-area demand, four locations JUL 09
Artifacts Q2 pricing brief Elasticity by store tier
Ask Navigator… (@ for datasets & files)
Build
You9:14 AM
Navigator9:14 AM
Working
Reading modeled POS — 4 locations, 18 months
Running elasticity and cross-price models
Checking trade-area demand and regional inflation
Thought for 6 steps
Three ways to move Q3 price at SignalFlare Cafe, built on your modeled POS and trade-area demand. I've put them side by side.
Targeted fits your stores best: +2.4% to +3.1% revenue, with traffic risk held between −0.2% and −0.6%. Nine core items move; the value tier doesn't.
Across the board earns more on paper and puts more traffic at risk than your two Pink stores have absorbed before. The reasoning stays attached.
|Ask Navigator… (@ for datasets & files)
Build
Q3 pricing scenarios — SignalFlare Cafe DRAFT
Scenario comparison · 4 locations · modeled POS through Jul 31
Three ways to move price in Q3
SCENARIO A
Hold
No change. The comparison baseline.
Revenue
—
Traffic risk
—
Items moved
0
SCENARIO BRecommended
Targeted
+2.9% on nine core items. Value tier untouched.
Revenue
+2.4% to +3.1%
Traffic risk
−0.2% to −0.6%
Items moved
9
SCENARIO C
Across the board
+4.5% on the full menu, all four stores.
Revenue
+3.6% to +4.4%
Traffic risk
−1.1% to −2.0%
Items moved
61
Modeled ranges, not point predictions Revenue lift Traffic risk
Hold
Targeted
Across the board
−3%0+5%
By store quadrant
Green · 2 stores — can carry 6–8% Blue · 1 — follow Targeted Pink · 1 — hold at value
Reasoning attached — 6 steps: elasticity, cross-price effects, trade-area demand, regional inflation. You decide; nothing here is applied.
Open reasoning
Share
AS
SignalFlare AI
/
Mission Hill Group
/
Q3 pricing — SignalFlare Cafe
/ Scenario review
Modeled data
Project · 4 locations · updated Aug 6
Q3 pricing — SignalFlare Cafe
Everything behind the Q3 price decision: the modeled POS, the trade-area demand pull, last quarter's committee notes, and the reasoning from every chat in here.
Chats
Q2 read — what the last increase actually did JUL 22
Trade-area demand, four locations JUL 09
Artifacts Q2 pricing brief Elasticity by store tier
Ask Navigator… (@ for datasets & files)
Build
You9:14 AM
Navigator9:14 AM
Working
Reading modeled POS — 4 locations, 18 months
Running elasticity and cross-price models
Checking trade-area demand and regional inflation
Thought for 6 steps
Three ways to move Q3 price at SignalFlare Cafe, built on your modeled POS and trade-area demand. I've put them side by side.
Targeted fits your stores best: +2.4% to +3.1% revenue, with traffic risk held between −0.2% and −0.6%. Nine core items move; the value tier doesn't.
Across the board earns more on paper and puts more traffic at risk than your two Pink stores have absorbed before. The reasoning stays attached.
|Ask Navigator… (@ for datasets & files)
Build
Q3 pricing scenarios — SignalFlare Cafe DRAFT
Scenario comparison · 4 locations · modeled POS through Jul 31
Three ways to move price in Q3
SCENARIO A
Hold
No change. The comparison baseline.
Revenue
—
Traffic risk
—
Items moved
0
SCENARIO BRecommended
Targeted
+2.9% on nine core items. Value tier untouched.
Revenue
+2.4% to +3.1%
Traffic risk
−0.2% to −0.6%
Items moved
9
SCENARIO C
Across the board
+4.5% on the full menu, all four stores.
Revenue
+3.6% to +4.4%
Traffic risk
−1.1% to −2.0%
Items moved
61
Modeled ranges, not point predictions Revenue lift Traffic risk
Hold
Targeted
Across the board
−3%0+5%
By store quadrant
Green · 2 stores — can carry 6–8% Blue · 1 — follow Targeted Pink · 1 — hold at value
Reasoning attached — 6 steps: elasticity, cross-price effects, trade-area demand, regional inflation. You decide; nothing here is applied.
Open reasoning
Share

The outcome you can check.

Operators who own their decisions

Every group works from its own market, within the brand’s guardrails.

Recommendations that land

Approved centrally, adjusted locally, and in by the deadline.

One view of the system

How each recommendation landed, and what it’s worth to the brand.