Milestones · Analytics · Agents

Agentic AI workflows for data-driven restaurant marketing

Upload sales data, explore analytics-backed signals, then run workflow milestones that produce structured outputs—schedules, angles, and copy—through agentic pipelines, chat, and reusable artifacts.

What the platform is

MilestonesWorkflows with steps that ingest data, run models, and save outputs for what comes next.
One stackWeb app, GraphQL API, and Python agent service share the same workspace data—analytics plus agents.
ArtifactsChat to steer work; review and edit generated captions, ideas, and plans as reusable assets.

Most restaurant marketing still runs on spreadsheets, static playbooks, and gut feel—without agentic automation that turns live sales data into repeatable workflow outputs. Teams miss demand signals, burn budget on the wrong promos, and never get from insight to publish-ready assets in one system.

Menuyukti is built as a workflow platform: workflows are sequences of milestones that fetch context, run AI steps (LangGraph-style agent service), and persist structured milestone data—not only chat. Your flagship use case is restaurant marketing: sales and menu signals feed those workflows so you get Instagram-oriented schedules and content you can refine in the app and reuse across locations.

How it works

1. Connect data

Upload POS exports or spreadsheet sales reports into your workspace—the GraphQL API persists them for analytics and for agents to query.

2. Get analytics-backed signals

The platform computes menu, mix, and trend-style views from your files so priorities are tied to what actually sells.

3. Run workflow milestones

Open or continue a workflow: each milestone can prepare context, run agentic AI steps, and save structured outputs—schedules, ideas, copy—for Instagram-style execution and iteration in chat.

Analytics, agents, and deliverables together

The product focuses on composable pipelines: structured data in, milestone results out—grounded in how your locations actually perform.

Sales- and menu-native analytics

Insights computed from uploaded POS or spreadsheet reports—what sells, mix shifts, and patterns worth promoting before you brief creative.

Agentic milestone pipelines

Prepare and run workflow steps with LLM-backed graphs: plan-and-execute style flows, reflection where drafts are critiqued and revised, and outputs written back as milestone data.

Chat, artifacts, and reuse

Steer agents in conversation, iterate on drafts, and keep schedules and copy as artifacts you can revisit—not a one-off thread that disappears.

Why teams use Menuyukti

A workflow platform first: milestones and milestone data, plus chat and artifacts—built for restaurant marketing as the flagship product.

Real sales data, not generic benchmarks

From signals to publish-ready artifacts

Agent pipelines wired to your workspace data

Locations, workflows, and assets together

Common questions

What data do I need to upload?

Typical POS or spreadsheet exports work—sales by item, category, or ticket over time. The GraphQL-backed workspace stores what you import so analytics and agents can use the same source of truth.

Is my sales data shared with third parties?

Your data powers your workspace and insights. We do not sell your data. See our Privacy Policy for details.

Is Menuyukti only a chatbot?

Chat is how you collaborate, but the core model is workflows: ordered milestones that produce stored, structured outputs—schedules, profiles, evaluations—not only messages. Agents call platform APIs when they need your data.

Who is it for?

Restaurant marketers, in-house creatives, and agencies running multi-location brands who want analytics plus agentic automation in one place—not disconnected dashboards and ad hoc prompts.

Can my team collaborate?

Yes—work across locations, workflows, milestones, and brand assets as your organization scales.

Explore your workspace

If you've been invited, sign in to connect locations, upload sales data, and run workflows with agentic support—analytics, milestones, and editable outputs in one stack.