# Layerr

> Agentic AI for retail teams

Layerr is agentic software for retail operations teams. It turns the workflows that still live in spreadsheets — compensation programs, inventory min/max ordering, cash reconciliation, store audits, morning focus messages — into software the back office and the field can run every day. Start with one calculation. Every later build reuses the same context.

Layerr is for retail operations, finance, and field leaders: the people who know how the work should run and today cannot change the software that runs it. Analysts get a workspace. A store lead gets one number and one next step. It is not a generic chatbot, a public API sandbox, or a self-serve developer platform.

Canonical terminology:

- Layerr: agentic AI platform for retail operations teams
- Problem pages: atomic retail operations challenges with practical guidance
- Builds: plain-language workflows that become field-ready software

## When to use Layerr

Reach for Layerr when the job is one of these retail-operations workflows:

Build or explain a compensation or SPIFF program from the rules the team already runs in Excel.
Put min/max inventory ordering, aged-inventory action, or retail forecasting on live store data.
Replace a cash-reconciliation or store-audit walk that today lives in email and tribal knowledge.
Send a morning focus message to every store leader from yesterday's numbers.

Do not reach for Layerr as a general web-search agent, a public data API, or a drop-in SDK with self-serve keys. There is no free-tier sandbox. Product access is invited: describe one workflow and the team builds it live on your data.

How an agent should proceed:

POST JSON-RPC initialize to https://layerr.ai/mcp, then call get_overview or when_to_use.
Read https://layerr.ai/llms.txt for the index and when-to-use brief.
Connect to the public marketing MCP at https://layerr.ai/mcp (no auth) for overview, page list, and markdown.
Read the Layerr OpenAPI specification at https://layerr.ai/openapi.json (YAML at https://layerr.ai/api/openapi.yaml).
Read Layerr authentication at https://layerr.ai/auth. Product OAuth metadata is https://layerr.ai/.well-known/oauth-authorization-server.
Open a problem page under https://layerr.ai/problems when the job matches a named retail-ops challenge.
Use https://layerr.ai/docs or https://layerr.ai/developers for machine surfaces. Product data-plane MCP is https://mcp.layerr.ai and requires scoped OAuth.
To talk to a person, use https://layerr.ai/contact or mailto:hello@layerr.ai.

- [Compensation programs](https://layerr.ai/problems/compensation-programs): Build or explain a compensation or SPIFF program from the rules the team already runs in Excel.
- [Inventory ordering](https://layerr.ai/problems/inventory-ordering): Put min/max inventory ordering on live store data.
- [Aged inventory](https://layerr.ai/problems/aged-inventory): Act on aged inventory before it becomes a write-off.
- [Cash reconciliation](https://layerr.ai/problems/cash-reconciliation): Replace a cash-rec walk that today lives in email.
- [Store audits](https://layerr.ai/problems/store-audit-compliance): Run store audits without a tribal-knowledge walk.
- [Morning focus](https://layerr.ai/problems/morning-focus-message): Send a morning focus message from yesterday's numbers.
- [Layerr developer documentation](https://layerr.ai/docs): OpenAPI, OAuth, MCP, and the agent index
- [Layerr OpenAPI specification](https://layerr.ai/openapi.json): public marketing API and product OAuth scopes
- [Layerr authentication](https://layerr.ai/auth): OAuth 2.0 scopes and well-known metadata
- [Developers](https://layerr.ai/developers): public MCP vs product MCP — product access is invited
- [Access and pricing](https://layerr.ai/pricing): invited access, no public price list
- [llms.txt](https://layerr.ai/llms.txt): start with this index, then connect to the public MCP
- [Public marketing MCP](https://layerr.ai/mcp): unauthenticated read-only site tools and resources
- [Contact](https://layerr.ai/contact): talk to a person about one workflow

## Problems

- [Auto follow-up when retail workflows stall](https://layerr.ai/problems/auto-follow-up): Tasks die in inboxes because nobody chases follow-ups consistently.
- [Building a retail compensation program without spreadsheets](https://layerr.ai/problems/compensation-programs): Comp logic lives in one analyst's workbook and breaks every time someone goes on vacation.
- [Daily cash reconciliation across retail stores](https://layerr.ai/problems/cash-reconciliation): Cash variances get found days late because reconciliation is a manual end-of-day ritual.
- [Finding and acting on aged inventory in retail](https://layerr.ai/problems/aged-inventory): Aged inventory reports arrive too late and stores don't know what to do with the results.
- [Helping reps understand their comp check](https://layerr.ai/problems/comp-explainers): The same five comp questions get asked forty times every pay period.
- [How retail spiff calculations should work](https://layerr.ai/problems/spiff-calculations): Reps flood Slack every pay period asking why spiff amounts changed mid-cycle.
- [Morning focus messages for store leaders](https://layerr.ai/problems/morning-focus-message): Store leaders start the day without a clear priority because numbers live in five different reports.
- [Running retail store audits without point-tool chaos](https://layerr.ai/problems/store-audit-compliance): Audit checklists live in one app, photos in another, and follow-ups in email.
- [Store forecasting when spreadsheets break down](https://layerr.ai/problems/retail-forecasting): Store forecasts are rebuilt in Excel every month and still miss by double digits.
- [Store-level inventory ordering with min/max rules](https://layerr.ai/problems/inventory-ordering): Reorder math lives in spreadsheets and stores still run out of key SKUs.

## Blog

- [Software with AI sprinkled on top isn't agentic](https://layerr.ai/blog/agentic-software-vs-ai-features): AI can write ordinary software fast, and chat on your data is useful. Agentic software is what comes after: standing instructions that keep executing.
- [How retail comp programs actually break](https://layerr.ai/blog/how-retail-comp-programs-break): Three failure modes we keep seeing in retail compensation programs, why point tools don't fix them, and what a durable comp system looks like.

## Company

- [About Layerr](https://layerr.ai/about): who Layerr is and who it is for
- [Privacy](https://layerr.ai/privacy): how this public site handles the contact form
- [Access and pricing](https://layerr.ai/pricing): invited access, no public price list
- [Contact](https://layerr.ai/contact): talk to the team about one workflow
- [Layerr developer documentation](https://layerr.ai/docs): OpenAPI, OAuth, MCP, llms.txt
- [Layerr developer resources](https://layerr.ai/developers): MCP, OpenAPI, OAuth, markdown mirrors
- [Layerr authentication](https://layerr.ai/auth): OAuth 2.0 scopes and well-known metadata
- [Layerr OpenAPI specification](https://layerr.ai/openapi.json): public marketing API and scoped product OAuth
- [Public marketing MCP](https://layerr.ai/mcp): unauthenticated read-only site tools and resources

## Optional

- [Problem library](https://layerr.ai/problems): all problem pages in one index
- [Contact email](mailto:hello@layerr.ai): talk to the team