Surplus Stock Agent

For

Inventory and sales leads at industrial manufacturers and distributors

Solves

Slow-moving stock sits unlisted because sales teams lack time, while buyers waste days chasing quotes.

Delivers

Searchable listings, buyer responses, negotiated deals, packing slips and ERP updates

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $17,000 for the full product

Run it

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

For inventory and sales leads at industrial manufacturers and distributors, turn surplus inventory files, photos and emails into searchable listings, negotiated deals and fulfilled orders.

1. Extract SKUs, condition and specs from files and photos.
2. Enrich items with public catalog data.
3. Publish listings to chosen channels.
4. Answer buyer questions in plain language.
5. Negotiate within set margin floors.
6. Generate packing slips and update ERP on sale.

What goes in, what comes out

The workflow

1. In
   Start with

   Inventory files, photos of shelves or pallets, forwarded emails with overstock lists

2. 1

   Upload inventory file

3. 2

   Extract and enrich items

4. 3

   Review auto-generated listings

5. 4

   Publish to channels

6. 5

   Respond to buyer inquiries

7. 6

   Negotiate within guardrails

8. 7

   Confirm sale with logistics handoff

9. Out
   Finish with

   Searchable listings, buyer responses, negotiated deals, packing slips and ERP updates

AI does the heavy lifting, people stay in charge

Use vision models to identify parts from photos and language models to parse spreadsheets, emails and chat. Enrichment pulls from public catalogs. Negotiation follows rule-based guardrails. A human reviews extracted data and listing accuracy before publishing, and approves any deal outside set floors.

What your team sees

Key screens: Inventory inbox, listing board, negotiation console. Use a central inbox for uploaded files and photos, a listing board with statuses (draft, live, negotiating, sold), and a negotiation console showing chat threads, price guardrails and margin floors. Provide a buyer-facing storefront preview. In this product, the first view is inventory inbox, followed by listing board and negotiation console.

Accounts and administration

Seller accounts, inventory access levels, listing approval states, negotiation guardrails, margin floor settings, transaction logs, audit trail for all agent actions and human overrides.

Integrations and data access

Start with CSV/Excel imports and email; later connect to ERP (e.g. SAP, NetSuite), shipping APIs, and procurement platforms.

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. 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

   6 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 inventory and sales leads at industrial manufacturers and distributors use it to solve "slow-moving stock sits unlisted because sales teams lack time, while buyers waste days chasing quotes"?
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. Run a 30-day paid pilot with one distributor: baseline is their current time-to-list and quote response rate.
4. Measure, then decide. Track two measurable outcomes: time from inventory upload to first buyer inquiry and and percentage of inquiries handled without human intervention. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer type (mid-size distributor), one use case (email spreadsheet to storefront listings), first two modules (extraction and listing, basic Q&A), manual review of all extracted items before publishing.

After the MVP. Automate negotiation within guardrails, integrate pickup scheduling, auto-update ERP, add voice reorder for warehouse staff, and expand to multiple procurement channels after the paid pilot.

What the build depends on. Reliable multimodal extraction, access to public industrial catalogs, and a secure storefront with buyer chat.

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 subscription at USD 500 per month per seller, scaled by inventory volume, plus a 2% fulfillment fee per transaction after the first sale.

Message to test

Turn your surplus stock into a self-selling agent that lists, answers and negotiates while you sleep.

Where to find buyers

Industry trade associations, procurement forums, LinkedIn groups for distributors, and direct outreach to mid-size manufacturers.

Lead magnet

A free demo that turns one uploaded spreadsheet into three live sample listings with extracted specs.

The first 30 days

1. Week 1: Build extraction pipeline for spreadsheets and photos.
2. Week 2: Create listing board and storefront.
3. Week 3: Add basic Q&A agent and manual review workflow.
4. Week 4: Run a live test with one distributor and collect feedback.

Paid pilot

Run a 30-day paid pilot with one distributor: baseline is their current time-to-list and quote response rate. Success is at least 20 items listed and 5 buyer inquiries answered without human intervention, with a decision to expand if response time drops below 2 hours.

Success metrics

Two measurable outcomes: time from inventory upload to first buyer inquiry, and percentage of inquiries handled without human intervention.

Retention and expansion

Subscription renews monthly as long as listings stay active; fulfillment fees create recurring revenue on each sale, and the system becomes the default channel for all surplus inventory.

Why clients would pick it

Each transaction trains the system on real buyer questions and negotiation patterns, building a proprietary dataset of industrial pricing and buyer behaviour that competitors cannot replicate quickly.

Alternatives and positioning

Manual listing by sales teams, third-party liquidators, or spreadsheets and email chains. This differs by removing all manual handoffs and letting sellers set automated negotiation limits.

Main delivery costs

Cloud inference for vision and language models, catalog data licensing, integration development, and support staff for manual review during pilot.

Limit negotiation to predefined price floors and margins, require human approval for any deal outside guardrails, restrict access to authorised sellers, log all agent actions, and must not publish items without human review or share buyer data outside the platform.