Agentic AI for Decision Intelligence

Smarter commercial decisions. Compounding intelligence.

Most AI is built to cut cost. We're built to grow revenue.

We pull together data scattered across your SaaS tools, build AI agents that reason across it, and tell you exactly what to do, why, and what it's worth.

THE PROBLEM

Commercial strategy decays the moment it's implemented.

Pricing, portfolio, and promotion decisions are made episodically — a consulting engagement, a strategy document, an implementation plan. Then the market moves. Competitors reprice. Consumer behavior shifts. Demand patterns change. By the time the erosion shows in your numbers, you're six months behind and back in a consulting cycle.

Value leakage isn't a one-time event. It's continuous — and most companies discover it far too late.


The Decision Loop

Signals become a diagnosis. A diagnosis becomes a recommendation. Humans decide. Outcomes are measured. The next recommendation gets smarter.

1 · Signal

Commercial pressure detected

  • Competitive price down 4.8%
  • Demand up 18% WoW
  • Inventory / capacity down 12%
  • Conversion down 90 bps
  • Margin at risk $42K / week

Signals are moving out of sync.

2 · Diagnose

Revenue gap identified

  • Pricing gap vs. competitors confirmed
  • SKU / portfolio underperformance located
  • Demand-supply mismatch quantified
  • Conversion leakage traced to root cause
  • Commercial gap estimated: $38K / week

Signals become a specific, quantified diagnosis.

3 · Recommend

Mitigation options prepared

  • Hold price: +3.2 pts margin
  • Shift demand: +$28K recovery
  • Reallocate spend: −14% waste
  • Protect priority segment: 92% service
  • Recommended path: B + C · 74% confidence

Current signals combine with similar past decisions.

4 · Decide

Human judgment applied

  • Shift 20% demand / spend
  • Set margin floor 32%
  • Run for 7 days
  • Protect top 10% accounts / SKUs
  • Owner assigned

Humans approve, modify, or override — and the rationale is captured.

5 · Act

Commercial move executed

  • Price posture +2.5%
  • Demand shifted 20%
  • Supply / capacity escalated across 12 nodes
  • Portfolio alternatives: 3
  • Action logged #042

The decision enters the operating workflow.

6 · Measure

Business impact tracked

  • Revenue captured +$31K
  • Margin protected +2.8 pts
  • Conversion recovered +65 bps
  • Utilization / fill rate +7 pts
  • Spend efficiency +11%

Outcomes are measured against baseline.

7 · Remember

Learning becomes reusable

  • Pattern saved: pressure + constraint
  • Playbook saved: shift + reallocate
  • Rationale saved: 32% floor / 7 days
  • Outcome attached: +$31K / +2.8 pts
  • Confidence updated: 74% → 81%

The next similar decision starts with better context.

Decision Memory

  • Pattern saved
  • Rationale saved
  • Outcome attached
  • Confidence updated
  • Comparable case created

↻ Next similar event → better recommendation

Where It Applies

Built for recurring commercial decisions

The same agent architecture, applied wherever a business is making a recurring decision.

Revenue Intelligence

Pricing, demand, and capacity decisions in capacity-constrained industries.

Examples

  • Pricing posture and demand activation
  • Market / corridor actions
  • Capacity-aware decisions
  • Competitive response
  • Utilization and revenue/hour

Best fit

Private aviation Travel Logistics Capacity-constrained services

Brand Intelligence

Competitive and market share signal for consumer brands.

Examples

  • Competitor pricing and promotion tracking
  • Market share shift detection
  • Positioning and claims gaps vs. competitors
  • Review and sentiment trend signal

Best fit

Consumer brands CPG Multi-brand portfolios

Claims Intelligence

Product review, rating, and claims signal for R&D teams.

Examples

  • Review and rating pattern detection
  • Claims risk and substantiation gaps
  • Product defect / complaint signal
  • Early warning before issues reach commercial teams

Best fit

R&D and product teams Regulated consumer categories

Price-Pack Intelligence

Portfolio, pack architecture, and margin decisions.

Examples

  • SKU / pack architecture support
  • Price-pack elasticity
  • Subscribe & Save optimization
  • Margin leakage detection

Best fit

Consumer brands RGM teams Multi-SKU portfolios

Shopper Intelligence

End-to-end shopper behavior and purchase-path signal.

Examples

  • Path-to-purchase mapping
  • Basket and repeat-purchase patterns
  • Channel and format behavior
  • Demand signal across touchpoints

Best fit

Retail Pharmacy Multi-channel consumer businesses

Conversion Intelligence

Website and funnel conversion diagnostics.

Examples

  • Search and discoverability optimization
  • PDP (product detail page) conversion
  • Funnel drop-off diagnosis
  • Page experience and content gaps

Best fit

Ecomm teams Amazon-led portfolios DTC brands

WHAT OUR DIAGNOSTICS FIND

Real findings. Real commercial data.

We don't audit from the outside. We run the diagnostic inside your commercial data and show you exactly where value is leaking.

PRIVATE AVIATION

10%+

premium revenue uplift


A membership-based private aviation company with pricing that rarely changed — leaving peak demand undermonetized and slow periods unfilled.

Diagnostic finding

Revenue gaps across four simultaneous commercial levers:

Premium Extraction Yield Recovery Utilization Competitive Intelligence

CONSUMER GOODS — $3B COMPANY

8%

revenue uplift across the portfolio


A $3B consumer goods company making portfolio, pricing, and content decisions disconnected from real purchase behavior and competitive market dynamics.

Diagnostic finding

Revenue gaps across five simultaneous commercial levers:

Price Pack Architecture Competitor Market Share Subscribe & Save Search Optimization PDP Conversion

WHY AGENTIC AI

Not dashboards. Not consulting. Something structurally different.

Three reasons why agentic AI is uniquely suited to commercial decision intelligence — and why neither traditional tools nor point-in-time engagements can replicate it.

Works at the Seams

Value leakage happens between teams — where pricing meets inventory, spend meets purchase behavior, competitive signals meet portfolio decisions. Agents span those boundaries by design. No single dashboard or team owns all of it.

Acts Before It's a Loss

Dashboards surface what already happened. Consultants diagnose last quarter. Agents detect signals continuously and trigger recommendations while recovery is still possible. The difference isn't intelligence — it's timing.

Compounds, Not Decays

Every consulting model is static the day it's delivered. An agent learns from every outcome — automatically improving its next recommendation. Value in month twelve is structurally higher than month one. That's the opposite of consulting.

How We Work

Expert-led pilots. Configurable decision loops.

We start with one high-value recurring decision, deploy and operate the loop around your data and workflow, then measure whether the recommendations improve outcomes.

1

Diagnose the decision

Define the recurring decision, current workflow, decision owners, and business metric.

2

Deploy and operate the loop

We deploy agents into your commercial workflow — connecting signals, recommendations, human approvals, actions, and memory. We operate it, not hand it off.

3

Prove the learning

Run the loop, measure outcomes, and turn what works into a reusable operating system.

Consulting-grade problem solving, productized through reusable agents.

Who We Are

Built by operators who have lived commercial complexity.

Amit Saini

Amit Saini

Ex-AWS · Enterprise AI · Supply Chain & Ops

20+ years building and scaling enterprise software for complex operating environments.

Punit Ruia

Punit Ruia

Ex-Booz · Ex-Google · Consumer Brands & Pricing

Serial entrepreneur focused on pricing, growth, and commercial execution for consumer businesses.

Rohit Saini

Rohit Saini

Ex-Deloitte · Ex-EY · AI/ML SaaS Builder

Builds scalable AI, data, and SaaS systems that turn intelligence into usable workflows.

Decisions are leaking value in your business right now.

Every quarter without a diagnostic is a quarter you can't recover. We'll show you exactly where — in 2 weeks, not 6 months. Real findings from your actual data.

Based on evidence, not promises.