Solutions portfolio

Solutions Map

How AI plugs into every stage of how a business operates. Each node is a production-tested workflow deployed in a real business.

116

Solutions

Deployed

Industries

References

Find Customers

Lead generation, prospecting, and market identification

Win Work

Screening, bidding, scoping, and proposals

Close & Contract

NDAs, contracts, pricing, and agreements

Deliver

Operations, project execution, and service delivery

Get Paid

Billing, collections, coding, and revenue recovery

Run the Business

Monitoring, compliance, reporting, and intelligence

Status

Deployed

In progress

Designed

Industry

Healthcare

Financial Services

Industrials

Business Services

Consumer & Retail

Run the BusinessIn progressConsumer & Retail

Demand Planning & Bulk Material Buying

A direct-to-consumer retailer that buys material in bulk and packs it into several finished sizes plans its purchasing in one large spreadsheet that one person understands. The math is real (demand by finished product, converted into bulk need, netted against what is on hand and on order), but it is refreshed by hand, hard-codes exceptions, and can quietly drop demand. The agent reproduces the planner's own logic exactly, then runs it on live inventory from the ERP and the fulfillment system and on current sales, producing the bulk buying list in an app the purchasing team works from. The goal is to replace the spreadsheet without changing how the team decides.

Trigger

Monthly planning cycle, or a planner asks what to buy now

Inputs

Weekly sales by finished product from the storefront
Bulk and finished on-hand from the ERP and fulfillment system
Open purchase-order lines and active bills of materials
The planner's workbook rules: conversion ratios, lost-sales adjustments, manual overrides

Outputs

Bulk-to-order list with quantities in each item's purchase unit
Projected demand by finished product
Side-by-side check against the planner's last workbook
Findings where the workbook's formulas drop or double-count demand

Integrations

ShopifyOdooShipHero

Under the hood

The workbook arrived as values only, with no formulas, so its logic was reverse-engineered from the numbers and then proved by reproducing the planner's own output row for row before any live data was used
Finished-goods surplus in one pack size is netted against need in another for the same bulk item, with on-hand counted once, and mixed purchase units (pounds, grams, counts, each) are normalized before anything is summed
Source systems stay read-only; live supply is a dated snapshot of on-hand, open purchase-order lines and active bills of materials, paced around the fulfillment system's API rate limit
Found a hard-coded day-count test that zeroed lost-sales demand for a 30-day month on 147 products (about 11,000 units); the live plan corrects it while the workbook comparison keeps the original logic
Checked kits before adding them and showed kit volume was already inside monthly actuals, which avoided a double count

Reference implementations

Direct-to-consumer garden products retailer

Direct-to-consumer retailer — the purchasing team's bulk buying plan rebuilt from its own spreadsheet logic and run on live inventory

Reproduced the planner's workbook exactly on 4,203 of 4,203 finished products and 2,043 of 2,043 bulk items; the first run on live inventory (October 13, 2026) produced the buying list for 606 bulk items. Under review by the planning team.