AI-native operations

Your store, stock and buying, run by agents you control.

SaaS gives you screens to work through. SaaS with AI adds a chat box. In Makeships, agents do the routine work (reordering, approving, matching, cataloguing) within limits you set, and your team starts the day with an inbox of what was done and what needs them.

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Inbox · this morning Example

Done
Drafted PO-00412 for 200 × Cotton kurta from Anand Textiles (₹52,000)
Done
Matched invoice INV-7781 to its PO and goods received
Done
Added “Kitchen › Mugs” to “Stoneware mug, 350 ml”
Needs you
Approve “Packaging restock” (₹1,80,000): above the ₹50,000 auto-approve ceiling
Needs you
Reorder “Brass diya” at 40, 120 at a time (sales doubled this month)

SaaS, SaaS with AI, or AI-native

Adding a chatbot isn't the same as software that does the work.

Most business software added AI as a feature. Makeships is built around agents taking actions, with people setting the limits and checking the exceptions.

	Traditional SaaS	SaaS with AI features	Makeships, AI-native
Who does the routine work	Your team, screen by screen	Your team, with a copilot suggesting text	Agents, within limits you set; people handle exceptions
What you open first	Lists of records to work through	The same lists, plus a chat box	An inbox: what agents did, and what needs you
Money and stock decisions	Manual, or rigid rules	Suggestions a person still has to carry out	Carried out up to your ceilings, asked above them
When the AI gets it wrong	No AI, so no AI mistakes	You find out later, and fix it by hand	One-click undo, refused safely if things moved on
Why something happened	Audit log of who clicked what	Often a black box	Every action with its reasoning and evidence
Store, stock and buying together	Separate tools joined by exports	Separate tools, each with its own AI	Sales update stock; low stock becomes a purchase request
Your data and models	In the vendor's cloud	Sent to the vendor's AI, resold as credits	In your cloud, on your own model keys, billed at cost
Bring your own agents	Limited API	The vendor's assistant only	MCP servers and an open API to the same actions

✓ strength · ~ depends · ✕ weakness

What the agents do

In each app, today.

Commerce

The store keeps itself in order.

The AI Brain reads each product and adds the right categories on its own when it’s confident, or asks you when it isn’t.
Ask the admin assistant to change prices, stock or products; it asks you in chat, makes the change, and you can undo it from the inbox.
Checkout holds stock while the customer pays, so a sale never runs on stale numbers.
A shopping assistant answers customers’ questions and finds products for them.

In SaaS, catalogue upkeep is a job someone does by hand, and store apps can’t see your warehouse.

Explore Commerce →

Inventory

Stock that reorders and tunes itself.

The reorder agent drafts a purchase order when an item hits its reorder point, on its own under your limit, and asks above it.
Reorder levels are suggested from actual sales and lead times, so they keep up as demand changes.
Dead stock and cycle-count gaps are flagged with the numbers behind them.
Say “received 50 blue widgets at the main warehouse” and it drafts the movement for you to approve.

In SaaS, low-stock alerts are emails someone has to act on, and reorder points are set once and forgotten.

Explore Inventory →

Procurement

Buying that moves without chasing.

The approval agent approves requisitions within your policy ceiling and categories, and routes the rest to the right approver with the reason.
Every invoice is matched against its purchase order and what was received, and mismatches are flagged.
Describe what you need in plain English and it writes the draft requisition.
Unusual spend with a vendor is flagged, and outreach emails are drafted for each RFQ.

In SaaS, approvals wait in someone’s queue and three-way matching is a spreadsheet exercise.

Explore Procurement →

Why together matters

Agents are only as good as what they can see.

A reorder agent in a separate inventory tool can't see this week's sales. Because the three apps share one platform, each agent works from the whole picture.

01 A sale is paid

Stock is held at checkout and taken out when paid.
02 Stock hits its reorder point

One purchase request goes to Procurement, never two.
03 The request is within policy

The approval agent approves it; above policy, it asks.
04 The invoice arrives

It's matched to the PO and delivery; once approved, its cost updates the item.

You stay in charge

Autonomy you can dial up, check and reverse.

Start with agents that only suggest. Raise their limits as their approvals prove them right.

Limits you set

Auto-reorder and auto-approve ceilings, allowed categories, and hard limits in the deployment that no setting can exceed.

A person above the line

Anything outside policy waits in the inbox with the agent’s reasoning and evidence. Approve, edit the numbers, or dismiss.

Undo for every change

Draft POs, category changes, stock moves, approvals and invoice matches can be undone, and undo is refused if something built on it since.

A full record

Every action is logged with who or what did it, why, what it changed, and what the AI call cost.

It all runs in your own cloud, on your own model keys. Why that matters for AI →

Coming next

What we're building now

One operator across the suite that follows a product from sale to reorder to receiving to cost.
Goals instead of rules, like “14 days of cover on top sellers”, with agents planning towards them.
Order exceptions in Commerce: failed payments, delayed shipments and cash-on-delivery risk.

To be fair

When SaaS may suit you better

You have a handful of products and orders, and nothing routine enough to hand to an agent.
You'd rather not set limits or review an inbox at all.
You need something running in the next hour with no setup.

Questions

About AI-native

What does AI-native mean here?

The software is built so agents do the routine work and people handle the exceptions. Each app opens on an inbox of what agents did and what needs you, every change is one the agent can make and you can undo, and the limits on what agents may do alone are yours to set.

Will an agent spend money without asking?

Only within the limits you set. Auto-reorder and auto-approve are off until you turn them on, have a ceiling and optional categories, and sit under a hard limit in the deployment itself. Anything above goes to a person.

What happens when an agent is wrong?

You undo it from the inbox. Undo reverses the change, for example cancelling a draft PO or putting reorder levels back, and is refused if something has built on it since, like the PO already being approved.

Which AI models does it use?

Yours. The apps call your provider with your own keys, so you choose the model and the usage bill comes straight from the provider, with no markup from us.

Can I use my own agents?

Yes. Inventory and Procurement include MCP servers with the same actions the built-in agents use, including reading the inbox and approving or undoing actions, as a named team member with that person’s permissions.

See a morning's inbox for a store like yours.

We'll show what the agents would handle, where they'd ask, and the limits we'd start you on.

Book a demo See the Operations Suite