Deepify
Deepify Product Creation Engine

Build the right product before you build the product.

Deepify turns a bank or credit union’s commercial objective into a tested, build-ready product specification.

Generate alternatives. Test the proposition. Model the economics. Challenge the assumptions. Select the strongest configuration.

Talk about a Product Lab See how it works
Commercial objective → tested product specification The step before configuration, origination and build.
The product creation process

One decision carried all the way through.

The difficult work happens before a bank knows exactly what it wants to build. Deepify structures that work into one connected process.

01
Define the commercial objective
Set the outcome, customer problem, constraints and current product position.
02
Generate credible alternatives
Create different propositions, price structures, benefits, eligibility rules and target segments.
03
Test the proposition
Compare each design against customer fit, the existing portfolio and observable competitor offers.
04
Model the economics
Carry through margin, funding value, acquisition, attrition, cannibalization and relevant capital use.
05
Find what breaks the case
Move uncertain inputs and identify the point where the recommendation changes.
06
Select and optimize
Choose the strongest configuration and make the trade-offs explicit.
07
Produce the build-ready specification
Turn the selected design into the product fields, rules, economics and evidence required for approval and downstream build work.
What comes out

The specification is the product of the work.

A decision a technology team can use.

The output is not a generic report. It is a structured product definition with the reasoning, economics, evidence and assumptions behind it.

That makes it useful to product, finance, risk, compliance and technology without forcing each group to reconstruct the work separately.

Target Deposit Product Build-ready specification
Target segment
Primary-bank households with $25K–$150K in liquid balances.
Rate structure
2.70% base rate; 3.10% with qualifying relationship.
Minimum balance
No minimum to open; $25K average monthly balance for relationship-rate eligibility.
Fees
No monthly account fee.
Eligibility
Personal customers, age of majority, Canadian resident.
Bundle rule
Relationship rate requires an eligible primary transaction account with qualifying activity.
Economics
Funding value, interest cost, acquisition, attrition and cannibalization attached to the selected configuration.
Illustrative structure. Product values are established from the institution’s objective, inputs, constraints and evidence.
Who it is for

Bought by leaders. Used by the people building the case. Reviewed by control functions.

Who buys it
Product, retail, strategy and growth leaders
A shorter path from “we should do something here” to a product definition their technology team can build, with a defensible reason for choosing one design.
Who uses it
Product managers, strategy, finance and analytics
Alternatives generated and tested in hours, with economics and assumptions visible throughout the work.
Who signs it off
Risk, compliance, model risk and finance
A clear record of facts, assumptions, estimates, calculations, sources, sensitivities and prior runs.
Illustrative worked example

Four viable deposit designs. One stronger configuration.

Northline Bank is fictional. The example shows how product alternatives are compared and how the selected design becomes a specification.

Commercial objective

Increase stable retail deposits from existing households without relying on a short-term headline-rate promotion or materially increasing expected annual funding cost.

Candidate A

High promotional rate

4.20% for 120 days, then 1.25%.

High initial volume. Higher expected runoff after the offer period.

Candidate B · Selected

Relationship savings

2.70% base; 3.10% with qualifying relationship.

Best balance of funding cost, retention and relationship depth.

Candidate C

Tiered balance rate

2.35%–3.25% across three balance tiers.

Higher cost concentrated in existing large-balance customers.

Candidate D

Cash acquisition bonus

$300 at $25K retained for 180 days.

Attractive acquisition proposition. Weaker economics below target retention.

Why B wins

At the modelled target volume, Candidate B provides the strongest balance of stable funding, expected cost and retention. The recommendation changes if relationship adoption, cannibalization, retention or alternative funding value move beyond their tested breakpoints.

What follows

The selected configuration becomes the product specification above: target segment, rate, eligibility, balance rules, fees, bundle requirements, target volume, economics and the assumptions supporting the case.

Where Deepify sits

Before the systems that configure, originate and run the product.

Commercial intent
The institution decides what it wants to achieve.

Funding need, growth objective, customer problem, portfolio gap or modernization opportunity.

Deepify
Decide what the product should be.

Generate alternatives, test them, model the economics, challenge the assumptions, select the design and produce the specification.

Core and delivery
Configure, originate and run the selected product.

The specification becomes an input to the institution’s downstream delivery process and technology stack.

The same logic applies during core modernization. A bank should examine and rationalize its product catalogue before spending heavily to recreate yesterday’s products on new technology.

Why the output can be challenged

The answer shows its workings.

01 / ECONOMICS

Carry the business case through the product design.

Margin, funding value, acquisition, attrition, cannibalization and relevant capital use remain attached to the selected configuration.

02 / BREAKPOINTS

Show where the recommendation changes.

The engine identifies the values at which another design becomes stronger.

03 / SENSITIVITY

Move uncertain inputs together.

Sensitivity testing reports how often the same answer survives when the uncertain inputs move within their defined ranges.

04 / EVIDENCE

Separate facts from assumptions.

Figures are classified as facts, assumptions, estimates or calculations. Competitor evidence retains its source, date and confidence.

05 / MISSING INPUTS

Do not disguise an unknown.

If an answer cannot be computed from the information provided, the system says so and names the input that would settle the question.

06 / LINEAGE

Keep the history of the decision.

Each run retains its inputs and outputs so versions can be compared and the decision can be reconstructed later.

People decide. Deepify produces the analysis and the product specification. Nothing is launched, priced or changed on anyone’s behalf.
Deepify Product Lab

One real product. One commercial objective. Thirty days.

$25K–$50K
depending on scope
01 Recreate

Take one real product problem your institution has already lived through.

02 Run

Generate designs, test them, model the economics and stress-test the assumptions.

03 Specify

Produce the target product specification and the decision record behind it.

04 Compare

Compare the time, work and output against your normal product-creation process.

Talk about a Product Lab
Questions

The practical questions.

Is this software or consulting?
Deepify is software. The 30-day Product Lab includes hands-on work to establish the institution’s inputs, recreate a real product problem and test the engine against the existing process.
What do you need from us to start?
One existing product, one commercial objective and enough information to understand the current proposition and economics. Missing information is identified explicitly.
How does this fit with the core platform we already have or are buying?
Deepify works upstream of the core. It helps determine what the product should be. The resulting specification is intended to feed the downstream process that configures, originates and runs the product.
What happens to our data?
The Product Lab begins by agreeing what information is required and how it will be handled for the engagement. Data requirements and controls should be confirmed before institution information is supplied.
How is this different from what our analytics team does?
Analytics remains an important institutional capability. Deepify carries product definition through alternatives, economics, market evidence, sensitivity, breakpoints and specification while keeping those elements connected.
Who owns the output?
Commercial and ownership terms should be established in the Product Lab agreement. The intended output is a product specification the institution can review, challenge and use in its own approval and delivery process.
Does Deepify make the product decision?
No. Deepify performs the analysis, compares configurations and shows the basis for the recommendation. The institution decides what proceeds.
Why start with deposits?
Deposit economics are legible, competitor offers are observable, pricing matters materially and funding matters to every bank. That makes deposits a practical first market for testing the full product-creation process.
Deepify Product Creation Engine

Bring one commercial objective. Leave with a product specification.

The Product Lab is a 30-day way to test Deepify against a real product problem before making a larger commitment.

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