Internal sourcing assistant
Match live contracts to stock you can actually fulfil.
This dashboard finds public-sector contract opportunities, saves the contract/tender record, checks whether auction and clearance stock can cover the full requirement before the submission deadline, protects a 45% ROI target, drafts the bid pack, and learns from the lots you approve or reject.
Step 2
Saved contract and tender opportunities
Select the contract or tender you want to fulfil before ranking stock. The agent checks quantity, value, deadline, ROI, and whether fulfilment needs one or multiple sources.
Saved data foundation
Opportunity record ledger
Every saved contract or tender becomes a structured opportunity record with requirement, value, stock coverage, ROI, bid status, and learning history.
Step 3
Goods contract matcher
Search government procurement routes for anchor contracts RentalReady can realistically win as a startup: housing, temporary accommodation, void-property, FM subcontract, and appliance/material supply opportunities.
Contract opportunities
Most viable live opportunities
This board is for contracts and tenders the agent can action. It ranks opportunities by startup-fit route, goods fit, stock coverage, cost against contract value, and delivery schedule.
Fetch or rank contract opportunities, then select one to review the full contract figures, goods match percentage, service risk, stock source plan, and bid-pack action.
Manual opportunity fallback
Step 4
Stock fulfilment agent
Match John Pye general/trade auctions, BPI, BidSpotter, i-bidder, William George, Eddisons, NCM, or similar stock to the selected contract/tender. The agent checks full quantity coverage, submission timing, and maximum safe bid before anything moves to purchase review.
Step 6
Contract fulfilment shortlist
Approve the stock you would actually use to fulfil the selected contract or tender. Rejections matter too: the assistant adjusts future scoring based on your selections.
Continuous learning loop
What the sourcing assistant is learning.
Every test run, approval, rejection, submitted bid, won contract, lost contract, and no-bid decision improves the model. Export and import the learning model so the agent keeps improving across deployments and browsers.