Parbat
Pricing infrastructure for modern commerce.
Parbat helps commerce brands ship profitable price changes with Evidence Before Exposure
We do not ask teams to trust a recommendation blindly. Parbat discovers opportunities, scores likely upside, simulates business impact before exposure, and ships only what is safe and worth doing.
Problem
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.
01
Underpriced bestsellers
Margin leaks because teams are afraid to touch winners.
02
Over-discounted inventory
Brands cut price to move stock, even when it destroys gross profit.
03
Debated price increase
Teams suspect a move is right, but lack the evidence to ship it.
04
Inefficient process
Pricing becomes slow, inconsistent, and hard to scale across the catalog.
The problem is not one catastrophic pricing mistake. It is hundreds of small, unmade or mis-made decisions compounding week after week¹ McKinsey: average 1% price increase translates into 8.7% operating profit increase, assuming no loss of volume.
the missing layer
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 is safe to ship
how it works
Parbat turns pricing into an Evidence-Before-Exposure system.
Discover
Normalize pricing signals across traffic, conversion, sales, inventory, margin, discounting and 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-specific shopping agents.
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.
CORE Workflows
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.
Product lens
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
Why now
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.
WHY PARBAT WINS
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.
MOAT
The moat is the brand-specific learning loop.
Any tool can surface a pricing recommendation. What compounds is what happens after that.
Every reviewed, blocked, shipped, reverted, and measured move improves the system.
The model starts the conversation. The moat is the accumulated brand-specific decision history — paired with governance, approvals, and realized outcomes.
The moat is not a static model.
It is a pricing system of record that gets smarter as brands use it.
IdeaL customer profile
Beachhead: Shopify brands with enough catalog complexity to need a system.
We are starting with US Shopify DTC brands with 50–300 active products. 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.
Go-To-Market
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.
business model
A small number of good decisions can justify the software.
Business Model
- Subscription software
- Usage-aware packaging
- Catalog / opportunity tiering
- Premium controls for larger brands
- Optional later: Performance-linked pricing for select accounts
Illustrative ROI example
For a brand with:
- 150 active products; top 30 products driving most demand
- Average gross profit per order = $24
- Move 1: +$1,800 monthly gross profit
- Move 2: +$1,200 monthly gross profit
2 shipped moves / month → $3,000 GP lift → $1,000 software cost
A small number of shipped, profitable pricing moves can justify the subscription. Incremental Gross Profit is the core value lens.
We are selling profit improvement, confidence, faster decision cycles, and governance — not just analytics.
roadmap
From design-partner pilot to controlled beta.
Parbat’s end-to-end workflow is live in production. Now we are proving shipped decisions, measured outcomes, and repeatability.
Today
- End-to-end workflow built and live on Shopify
- 1 design-partner pilot live
- Decision-ready Evidence Packs
- Safe, profitable shipped moves
Proving now
- Outcome measurement vs. projection
- Model + workflow tuning
Next
- Controlled beta with 3–5 brands
- Parallel onboarding and repeatability
- Case-study-quality wins
- First repeatable Shopify go-to-market motion
The question now is not whether Parbat can run the loop. It is whether the loop can produce repeatable, measurable wins across brands.
why us
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
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 and Advisor
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 is a decision process we learned firsthand and built into software.
Pricing should be a system, not guesswork.
We are 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.