Pricing infrastructure for modern commerce.
Parbat helps Shopify brands grow gross profit by simulating price changes before they go live.
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.
A 1% price increase can move operating profit ~9%¹ — but commerce pricing is still run reactively.
Every week, brands face hundreds of pricing decisions without a reliable way to know which moves are safe, profitable, and worth shipping.
Underpriced bestsellers
Margin leaks because teams are afraid to touch winners.
Over-discounted inventory
Brands cut price to move stock, even when it destroys gross profit.
Debated price increase
Teams suspect a move is right, but lack the evidence to ship it.
Inefficient process
Pricing becomes slow, inconsistent, and hard to scale across the catalog.
The problem is not one catastrophic pricing mistake. It's hundreds of small, unmade or mis-made decisions compounding week after week
¹ McKinsey: average 1% price increase translates into 8.7% operating profit increase, assuming no loss of volume.
Current tools show signals. They do not produce economically attractive and safe pricing decisions.
Current solutions break at the exact point where a brand needs help: decision-making and safe execution.
Dashboards
Show what happened.
Do not tell the team what to do next
Repricers
Automate changes.
Often without enough business context, trust, or governance.
Experimentation Tools
Learn on live traffic.
After customers are already exposed.
Manual Workflows
Depend on judgment.
Slow, inconsistent, and hard to run across a catalog.
The missing layer is evidence before exposure. Parbat simulates what a move is likely to do to the business before it goes live — then governs what's safe to ship
Parbat turns pricing into an Evidence-Before-Exposure system.
Discover
Normalize pricing signals across traffic, conversion, margin, inventory, price history into a canonical pricing input.
Score
Identify candidate price moves which are most likely to improve incremental gross profit using a demand-response model.
Simulate
Convert those candidate prices into projected business impact before launch, using brand-calibrated simulated shopping sessions.
Ship
Launch only the price changes that are economically attractive, and safe. Every shipped move is backed by an Evidence Pack.
Measure
Capture realized outcomes from shipped price changes
Learn
Feed measured outcomes back into scoring and simulation so future pricing decisions become more accurate, more reliable, and more brand-specific over time.
Parbat does not stop at insight. It helps teams ship pricing decisions with evidence and control.
Three entry points into the same system.
Daily Briefing
A ranked daily list of the highest-impact catalog opportunities, each backed by projected impact, safety status, and a recommended next action.
On-demand Recommendation
“What price should we set for this product or group right now?”
Simulate a Move
“We’re thinking about a 5% increase or a move to $79. Is it safe, and does it look worth doing?”
Every workflow produces the same core artifact: an Evidence Pack, and a decision-ready path to ship.
A pricing decision workspace, not a black box.
Every recommendation is backed by an Evidence Pack that shows Projected Impact, Verified Safety and Decision Context
The market is ready for pricing to become a system, not a spreadsheet exercise.
Margin pressure is forcing discipline
Brands can no longer rely on blanket discounting and reactive promotions to protect growth.
The technical stack is finally ready
Shopify-native data, modern workflows, and production-grade infrastructure now make continuous pricing systems possible.
AI is accelerating, but trust is the bottleneck
The missing layer is not another recommendation engine. It is a controlled system that simulates and governs changes before exposure.
Brands want workflow-native tooling
Teams want software that fits how pricing decisions are actually made: review, validate, approve, ship, and learn.
Pre-exposure pricing decisioning is feasible now in a way it wasn’t a few years ago.
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 — with acquisition cost as an important economic input, not the first workflow.
We're building simulation-led decision infrastructure for commerce —starting with pricing, and expanding only where the economics can be measured.
Why Parbat is different.
Simulation-led
We do not just suggest a price. We simulate likely business outcomes before exposure.
Economics-aligned
Moves are evaluated against incremental gross profit, not vanity metrics or generic automation goals.
Governed for production
Evidence, guardrails, auditability, and controlled execution are built in from the start.
Trustworthy by design
Parbat separates projected upside from verified safety, so teams can understand why a move is worth shipping.
Others help teams analyze, automate or test. Parbat helps decide what is economically attractive and safe — before exposure.
The compounding asset is brand-specific calibration.
Mechanism
Every shipped move creates a proprietary intervention record:
Decision context
Candidate price
Projected outcome
Uncertainty
Policy state
Merchant decision
Realized outcome
What improves
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.
Beachhead: Shopify brands with enough catalog complexity to need a system.
We're starting with US Shopify DTC brands with 75-400 active SKUs. The initial sell is not autonomous pricing. It is a safer, more credible path to better pricing decisions.
Catalog complexity makes pricing pain real.
Sufficient traffic enables product-level decisions.
Lean teams lead to manual pricing.
High willingness for software that boosts margin without added headcount.
We land with one narrow promise: help the team ship a small number of profitable, safe price changes quickly.
Land pilots → 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.
Start with decision support. Expand into governance, controlled execution, and closed-loop learning.
One modest pricing win per month can cover the software.
Initial pricing hypothesis
Standard ACV
$12K / year — annual subscription
Beta conversion
~$6K in year one (50% pre-agreed beta discount)
Expansion
Additional stores, catalog complexity, approval workflows, governance, and integrations
Customer break-even
per month
incremental gross profit to break even at $12K ACV
Annual SaaS subscription. No performance fee in the initial model.
The beta page already promises participants a pre-agreed first-year continuation price at 50% off. A $6K beta-conversion ACV and $12K standard ACV form a coherent hypothesis.
Live on Shopify. Now proving realized economics.
Shopify public app launched
Intentionally unlisted during beta
Design partner in production
Operating live end-to-end
Shipped moves
[M] completed measurement windows
Beta brands committed
Onboarding underway
First complete cross-brand projection-versus-outcome readout: Sep 30, 2026
We earned this insight from how pricing decisions actually get made.
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.
We then built that conviction into a live Shopify workflow.
Farrukh Zaman
Co-Founder and CTO (Full-time)
Built and operated production-grade enterprise platforms at Cisco and Salesforce.
Brings the systems depth required to make AI-native pricing trustworthy in production.
Zahara Malik
Co-Founder (Advisory)
Brings research, commercial strategy, and GTM discipline from Dynata and Ipsos, helping turn customer insight and early outcomes into repeatable commercial motion.
This is not a market thesis we formed from the outside. It's a decision process we learned firsthand and built into software.
Pricing should be a system, not guesswork.
We're starting with Shopify brands.
Our wedge is simulation-led pricing that helps teams move faster without giving up control.
Over time, Parbat becomes the system of record for how brands discover opportunities, simulate outcomes, govern risk, ship changes, and continuously improve profit.
We’re building the infrastructure that makes AI-native pricing trustworthy enough to use in production.
Parbat