Vertical AI for retail

Helping build the AI native retailer

One workflow at a time

Freespan partners with the most ambitious retailers to design and operationalize their AI transformation journeys. We bring a retail-specific semantic layer, a library of retail-specific agents, and a team of forward deployed engineers who make these work in your environment and own the business outcome.

Who we are

Retail experts. AI experts.

We are retail experts with deep knowledge of merchandising, operations, finance and e-commerce, and of the connecting thread between them. We are AI experts who have built products, platforms and data products, and led forward deployed engineering motions.

Former C-suite executives, through to where the work actually gets done.

Nearly all work in retail crosses multiple systems.

Where others fail

Three ways the market comes up short.

Copilots from platform vendors are limited by the data their vendors own. Most of your team's work cuts across different platforms, and cannot be automated in silos.

Custom AI dev-shops can create long-term governance and interpretability challenges, with differing semantic and ontological layers.

Consultants do not bring a pre-built, retail-specific platform.

What we are building

The first agentic fabric for retail.

An ontology and semantic layer built for the industry, running on world-class model abstraction. End-to-end agentic workflows on top of it. And senior retail leaders who handle the integration, execution, transformation and value capture.

01

Retail semantic layer

Canonical model, ontology, relationships and metrics. What a SKU is. How a planogram relates to a store cluster. How a labor hour maps to a task. Encoded once, reused everywhere.

02

Workload library

Custom agents built on a pre-existing, retail-specific agent library spanning merchandising, store operations, finance, pricing and supply chain.

03

Value capture

Former retail operators, not generalist consultants, who define the value creation program and stay until the finance organization confirms the number.

Our approach

Business-led, not platform-led.

01

We start with the problem, not the product. A real and quantifiable business problem, chosen with you, that you can hold us accountable for. The business outcome, not the technology outcome.

02

We install, and then we run. Our people work inside your building, alongside your teams, through the change. Technology does not book the savings.

03

Deployment two costs a fraction of deployment one. Because it inherits the semantic layer built for the first, and every one after it compounds.

40%
of merchants' time goes to data consolidation and repetitive spreadsheet work
McKinsey merchant survey, n=114
71%
say existing AI merchandising tools have had limited to no effect on their business
Same survey
67 / 33
production rate for vendor-led AI deployments versus internal builds
MIT NANDA, 300+ initiatives
Where we start

Example places we could start.

One workload, with a number attached.

Value leaks

Trade funds and deduction recovery

Every vendor agreement audited against what was billed, earned and collected: co-op, markdown money, slotting, chargebacks, cost-file mismatch.

You already outsource this. Test us against your current third-party vendor on accuracy and on price.

Measured onHard dollars recovered, traceable to a bank statement
Efficiency

The merchandising analyst loop

Performance and margin reporting, competitive read, assortment gaps, promo and markdown recommendations, vendor scorecard and category review pack, generated overnight and sourced.

Measured onMerchant hours returned per category per week · time-to-CBR · negotiated terms
Value leaks

Vendor and lease audits and payments

Monthly review of every major vendor agreement and every lease, audited against contract terms and current business, to determine the exact payment or refund owed.

Measured onHard dollar savings

A free span is the distance you cross with nothing holding it up in the middle.

Perspectives

What we think, in the open.

The Narayan Enterprise AI Brief. Ten essays, published since March, archived here in full.

All perspectives
The team

Business, technology and AI leaders.

We are a team of business, technology, digital, AI and transformation leaders who are passionate about bringing the benefits of generative AI and agentic AI workflows to the retail industry.

Narayan Iyengar

Narayan Iyengar

Founder

25+ years across retail, consumer operations and technology. Senior operating roles at Albertsons, Disney and DISH, spanning digital, merchandising, e-commerce, digital marketing and operations. Ex McKinsey; serves on the board of an NYSE-listed retailer.

The team and current openings
Curious?

Contact us.

Whether you are a retailer, invest in this space, or are an operating / AI leader, we would love to hear from you.