Purchase Follow-up Agent

For

E-commerce managers at small direct-to-consumer brands

Solves

One-size-fits-all thank-you emails miss the chance to turn one-time buyers into repeat customers.

Delivers

Personalised follow-up messages and managed reply conversations

Built in

10 days of creation time, MVP in 2 days

Investment

$5,000 for the MVP, $15,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For e-commerce managers at small direct-to-consumer brands, turn order data, product descriptions and customer history into personalised follow-up messages that ask the right question at the right time.

1. Read each new order with product and customer details.
2. Draft a personalised follow-up message per order.
3. Offer tone options trained on the brand's voice.
4. Let users review drafts or set auto-send rules.
5. Handle replies with suggested responses for approval.
6. Track open, reply and repeat purchase rates.

What goes in, what comes out

The workflow

1. In
   Start with

   Order data, product descriptions, customer history and brand voice examples

2. 1

   Connect the store

3. 2

   Sync new orders

4. 3

   Review drafts

5. 4

   Set auto-send rules

6. 5

   Send messages

7. 6

   Handle replies

8. 7

   Review performance

9. Out
   Finish with

   Personalised follow-up messages and managed reply conversations

AI does the heavy lifting, people stay in charge

Use language models to draft messages based on order details, product descriptions and customer history. Train on the brand's voice using past emails and style guides. A store owner or manager reviews drafts before auto-send is enabled, and checks suggested replies before they go out. The agent learns from open and reply rates to improve tone and timing.

What your team sees

Key screens: Order feed, message drafts, reply inbox, performance dashboard. Use a list of recent orders on the left, a central preview of the drafted message, and a right-hand panel showing customer history and product details. Let users approve, edit or set auto-send rules per brand. Display draft, sent and replied states. Provide a reply inbox where the agent handles incoming messages. In this product, the first view is order feed, followed by message drafts and reply inbox.

Accounts and administration

Store accounts, message templates, auto-send rules, tone settings, approval states, reply permissions, performance reports and an audit trail of all sent messages.

Integrations and data access

Start with Shopify and WooCommerce order systems, then add email platforms like Gmail or Outlook, and later connect to loyalty and subscription tools.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case; first modules: order feed and message drafts. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   5 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will E-commerce managers at small direct-to-consumer brands use it to solve "one-size-fits-all thank-you emails miss the chance to turn one-time buyers into repeat customers"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. A paid pilot with three DTC brands proves it by comparing reply and repeat purchase rates against their previous blast email campaigns over 30 days.
4. Measure, then decide. Track reply rate on follow-up messages and repeat purchase rate within 60 days of the original order. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer: e-commerce managers at small DTC brands. One use case: follow-up email per order. First two modules: order feed and message drafts. Manual review of every draft before sending.

After the MVP. After the paid pilot, automate reply handling with suggested responses, add reorder reminders based on purchase cycles, and integrate with loyalty programmes and subscription tools.

What the build depends on. The build needs access to order APIs, a language model for drafting and replies, a brand voice training pipeline, and a simple review interface for store owners.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at $99 per month for up to 500 orders, scaling by order volume, as a hypothesis to validate with early pilots.

Message to test

Turn every purchase into a conversation that brings buyers back.

Where to find buyers

E-commerce communities, DTC brand forums, Shopify app marketplace, and social media groups for small online retailers.

Lead magnet

A free sample follow-up message for the store's last ten orders, showing how personalisation could look.

The first 30 days

1. Week 1: Build the Shopify integration and order feed.
2. Week 2: Develop the drafting engine with brand voice training.
3. Week 3: Build the review interface and auto-send rules.
4. Week 4: Run a pilot with three stores and measure reply rates.

Paid pilot

A paid pilot with three DTC brands proves it by comparing reply and repeat purchase rates against their previous blast email campaigns over 30 days. The decision to continue depends on a 20% higher reply rate and a 10% higher repeat purchase rate.

Success metrics

Reply rate on follow-up messages and repeat purchase rate within 60 days of the original order.

Retention and expansion

The agent keeps learning from each order and reply, improving message quality and timing, so stores rely on it for ongoing customer retention rather than one-off campaigns.

Why clients would pick it

The more orders and replies the agent processes, the better it learns each brand's tone and each customer's preferences, making the output harder to replicate with a generic tool.

Alternatives and positioning

People use blast email tools with templates or hire copywriters for high-value orders. This differs by personalising every message at scale and handling replies without extra staff.

Main delivery costs

Main delivery costs are AI inference for drafting and replying, integration maintenance for Shopify and WooCommerce, and onboarding support for each store.

Store owners approve drafts before auto-send, set limits on message frequency, restrict replies to approved topics, and the agent must not send promotional content without explicit approval or share customer data outside the store's systems.