Find the price changes worth making — before customers see them.
We don't ask teams to trust a recommendation blindly. Parbat discovers opportunities, simulates business impact before customers are exposed, and ships only what's safe and worth doing — evidence before exposure.
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Problem
Pricing has extraordinary leverage.
Demand response is the unknown.
~9%
A 1% price increase can move operating profit ~9%
but commerce pricing is still run reactively.
Bestsellers remain untouched
Margin leaks because teams are afraid to touch winners.
Discounting destroys avoidable margin
Brands cut price to move stock, even when it destroys gross profit.
Decisions are delayed or made by instinct
Teams suspect a move is right, but lack the evidence to ship it.
The unanswered question is not “Can price affect profit?” It’s “What will demand do when this price changes?”
Current tools answer adjacent questions. Parbat closes the decision gap.
AnalyticsWhat happened?
RepricersWhat does the market charge, and what rule should run?
IntelligemsWhich selected price wins on live traffic?
ParbatWhich moves deserve action, what are they likely worth, and do they clear our constraints before exposure?
Parbat is the pre-exposure decision layer. Live testing remains available when experimental confirmation is worth the traffic and time.
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How it works
Parbat turns pricing into an Evidence-Before-Exposure system.
01
Discover
Normalize pricing signals across traffic, conversion, margin, inventory, price history into a canonical pricing input.
02
Score
Identify candidate price moves which are most likely to improve incremental gross profit using a demand-response model.
03
Simulate
Convert those candidate prices into projected business impact before launch, using brand-calibrated simulated shopping sessions.
04
Ship
Launch only the price changes that are economically attractive, and safe. Every shipped move is backed by an Evidence Pack.
05
Measure
Capture realized outcomes from shipped price changes.
06
Learn
Feed measured outcomes back into scoring and simulation so future pricing decisions become more accurate, more reliable, and more brand-specific over time.
Every recommendation is backed by an Evidence Pack that shows Projected Impact, Verified Safety and Decision Context.
Projected Impact
Verified Safety
Decision Context
Evidence PackDecision workspace
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Traction
Live on Shopify. Now proving realized economics.
Jul 31, 2026
Shopify public app launched
Intentionally unlisted until beta concludes
1
Design partner in production
Operating live end-to-end
20+
Shipped moves
Measurement in progress
8
Beta brands planned
Ready to onboard
First projection-versus-outcome readout from our design partner lands: Oct. 2026
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Why now
AI made recommendations cheap. Trustworthy execution is still scarce.
Shopify provides a standardized data and execution surface
Modern demand modeling makes catalog-wide screening feasible
Governance, calibration, and accountability are now the bottlenecks
The missing layer is not another recommendation engine. It is a controlled system that simulates and governs changes before exposure.
Pre-exposure pricing decisioning is feasible now in a way it wasn’t a few years ago.
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Ideal customer profile
Start where pricing pain and usable evidence overlap.
$2M–$20M in annual Shopify sales
15+ products each doing 20+ orders a month
40%+ gross margin on target products
Real price-change history across the catalog
80%+ of revenue through their own storefront
Founder, GM, or head of ecommerce can approve changes — including markdowns
We land with one narrow promise: help ship a small number of profitable, safe price changes quickly.
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Market size
A $60–$120M U.S. Shopify beachhead before international and product expansion.
~10,000
Matching U.S. Shopify DTC brands*
×
$6K–$12K
Annual Contract Value
=
$60–$120M ARR
Serviceable addressable market
Expansion paths
Global Shopify pricing
Multi-store and enterprise governance
Broader measurable commerce decisions
* Filtered for $2M–$20M Shopify revenue, order density (15+ products at 20+ monthly orders, proxied externally by store traffic), margin, and decision authority. Manually verified fit rate on a 100-store sample.
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Business model
One modest pricing win per month can cover the software.
$12K
Standard ACV
/ year — annual subscription
~$6K
Beta conversion
in year one (50% pre-agreed beta discount)
$1,000
Customer break-even
per month incremental gross profit to break even at $12K ACV
Expansion: Additional stores, catalog complexity, approval workflows, governance, and integrations
Annual SaaS subscription. No performance fee in the initial model.
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Go-to-market
Land beta brands → prove lift → scale through Shopify-native distribution.
Our go-to-market strategy focuses on a disciplined, three-stage approach to acquire and grow customer relationships.
Land
Founder-led outreach to design-partner referrals, heads of eCommerce, and brands in the beachhead segment.
Prove
Generate measurable pricing wins and turn them into strong case studies with quantified impact.
Scale
Grow through a robust Shopify-native presence, partner agencies, and customer proof points.
Key GTM metrics
Install to first Evidence Pack
Beta-to-paid conversion
Time to first shipped move
Onboarding cost
Gross retention and expansion
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Compounding advantage
The compounding asset is brand-specific calibration.
Every shipped move creates a proprietary intervention record:
Decision context
Candidate price
Projected outcome
Uncertainty
Policy state
Brand decision
Realized outcome
Parbat tracks forecast bias, error, and interval coverage by brand. As the system observes more interventions, brand-specific calibration should improve and uncertainty should narrow only where the evidence supports it.
Why switching has a cost
A new provider can access historical orders. It does not inherit Parbat's exact projection, policy, approval, rollback, and outcome lineage—or the calibrated model state produced by those interventions.
The moat exists only if calibration error improves with use. We measure that directly.
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Vision
Pricing is the first decision category. The infrastructure is broader.
Parbat starts with Shopify pricing because pricing is frequent, high-leverage, measurable, and under-operationalized. But the core system is not limited to price changes.
Simulate Economic Impact
Predict the outcome of proposed commercial actions before they go live.
Apply Safety & Governance
Ensure changes align with business guardrails and policies.
Ship Optimized Decisions
Launch only what is economically attractive and validated.
Learn from Outcomes
Continuously refine models based on realized results.
That same loop can extend from pricing into promotions, markdowns, merchandising, inventory, and other profit-sensitive commerce decisions.
We're building simulation-led decision infrastructure for commerce — starting with pricing, and expanding only where the economics can be measured.
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Why us
Built from the inside of a live pricing process.
Parbat did not start as a generic AI pricing idea. We spent the last three years embedded with a live design partner, observing how pricing decisions were actually made in practice: too much manual judgment, too little evidence, and no safe path to ship.
That led us to a specific conviction: brands do not need more pricing analytics. They need a system that helps them evaluate, verify, and ship pricing decisions with confidence.
Farrukh Zaman
Co-Founder and CTO (full-time)
Production systems at Cisco and Salesforce; product, architecture, and execution
Zahara Malik
Co-Founder (advisory → full-time post-raise)
Research, commercial strategy, measurement, and GTM experience from Dynata and Ipsos
This is not a market thesis we formed from the outside. It's a decision process we learned firsthand and built into software.
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Ask
Raising $500K SAFE to turn one live loop into repeatable, paid proof.
$500K
SAFE
to turn one live loop into repeatable, paid proof
Milestones
Complete the 8-brand beta
Generate 20–30 measured pricing interventions
Publish 3 quantified case studies
Convert 10–15 paying brands (founding cohort + first post-beta brands at list)
Reach approximately $120K–$180K ARR
Establish repeatable onboarding and calibration benchmarks
The next financing milestone: measured, repeatable pricing outcomes across brands.
Appendix available on request — simulation architecture, measurement methodology, market-sizing method, beta design.
