We do not ask teams to trust a recommendation blindly. Parbat discovers opportunities, scores demand, simulates business impact before exposure, and ships only what is safe and worth doing.
Every week, brands face hundreds of pricing decisions without a reliable way to know which moves are safe, profitable, and worth shipping.
Margin leaks because teams are afraid to touch winners.
Brands cut price to move stock, even when it destroys gross profit.
Teams suspect a move is right, but lack the evidence to ship it.
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 week
¹ McKinsey: average 1% price increase translates into 8.7% operating profit increase, assuming no loss of volume.
Current solutions break at the exact point where a brand needs help: decision-making and safe execution.
Show what happened.
Do not tell the team what to do next
Automate changes.
Often without enough business context, trust, or governance.
Learn on live traffic.
After customers are already exposed.
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
Normalize pricing signals across traffic, conversion, sales, inventory, margin, discounting and price history into a canonical pricing input.
Identify candidate price moves which are most likely to improve incremental gross profit using a demand-response model.
Convert those candidate prices into projected business impact before launch, using brand-specific shopping agents.
Launch only the price changes that are economically attractive, and safe. Every shipped move is backed by an Evidence Pack.
Capture realized outcomes from shipped price changes
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.
A ranked daily list of the highest-impact catalog opportunities, each backed by projected impact, safety status, and a recommended next action.
“What price should we set for this product or group right now?”
“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.
Every recommendation is backed by an Evidence Pack that shows Projected Impact, Verified Safety and Decision Context
Brands can no longer rely on blanket discounting and reactive promotions to protect growth.
Shopify-native data, modern workflows, and production-grade infrastructure now make continuous pricing systems possible.
The missing layer is not another recommendation engine. It is a controlled system that simulates and governs changes before exposure.
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.
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.
Predict the outcome of proposed commercial actions before they go live.
Ensure changes align with business guardrails and policies.
Launch only what is economically attractive and validated.
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 are building simulation-led decision infrastructure for commerce —starting with pricing, and expanding only where the economics can be measured.
We do not just suggest a price. We simulate likely business outcomes before exposure.
Moves are evaluated against incremental gross profit, not vanity metrics or generic automation goals.
Evidence, guardrails, auditability, and controlled execution are built in from the start.
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.
Generic pricing recommendations are easy to produce. Trusted pricing decisions are not.
Over time, Parbat becomes harder to replace because it is not just a model sitting on top of Shopify data. It becomes the system of record for how a brand makes, governs, and learns from pricing decisions.
The moat is not the first recommendation.
It is the accumulated decision history, outcome data, calibration, and governance layer that compounds with use.
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.
Our go-to-market strategy focuses on a disciplined, three-stage approach to acquire and grow customer relationships.
Founder-led outreach to design-partner referrals, heads of eCommerce, and brands in the beachhead segment.
Generate measurable pricing wins and turn them into strong case studies with quantified impact.
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.
- Subscription software
- Usage-aware packaging
- Catalog / opportunity tiering
- Premium controls for larger brands
- Optional later: Performance-linked pricing for select accounts
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.
Parbat’s end-to-end workflow is live in production. Now we are proving shipped decisions, measured outcomes, and repeatability.
- End-to-end workflow built and live on Shopify
- 1 design-partner pilot live
- Decision-ready Evidence Packs
- Safe, profitable shipped moves
- Outcome measurement vs. projection
- Model + workflow tuning
- 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.
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.
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.
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.
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.
Parbat helps commerce brands ship profitable price changes with Evidence Before Exposure