Every sourcing decision, evidence-backed and on the ledger. A five-year plan to $100M ARR — and the machine that makes it compound.
SEED · JULY 2026 · CONFIDENTIAL — PLANNING TARGETS, NOT FORECASTS. ↓ or arrow keys to advance.
Buyers can't compare private-label alternatives attribute by attribute. Substitution risk is judged from thumbnails and vendor promises.
Freight, duty, and terms buried in spreadsheets. Two quotes for the same SKU aren't comparable, and disputes eat margin.
Distributors carry receivables with no live view of exposure. Banks price the risk without the data that actually predicts it.
Import brokers and distributors do this work by hand today — the knowledge lives in inboxes and leaves with the people.
Buyers upload item lists, invoices, and catalogs. AI extracts, normalizes, and matches — then validators and reviewers promote approved facts into an append-only, hash-chained ledger. Every number traces to a source document.
Versioned landed-cost quotes with itemized components, assumptions, and evidence links.
Quote ↔ PO ↔ invoice ↔ BOL ↔ payment matched automatically; humans see exceptions only.
Net terms underwritten from the platform's own payment ledger, not a credit bureau's guess.
Multimodal models read messy catalogs, spec sheets, and certificates reliably enough to staff a pipeline — when wrapped in validation and review.
Operators and distributors are pushing into private label to defend margin — and hitting the sourcing-complexity wall.
ERPs hold POs, not equivalency, evidence, or supplier truth. The ledger seat is open.
The catalyst: Section 301 tariff exclusions on many Chinese goods lapse in November 2026 — landed costs jump, and cross-supplier sourcing goes from nice-to-have to urgent.
US foodservice disposables is a $15–18B wedge (Grand View 2024, Mordor 2025) inside $382B of foodservice distribution. Because the product is procurement software, the real ceiling is ~$250B of adjacent categories — jan-san, packaging, PPE, MRO.
Sized from published research — full sourcing, ranges, and the $2.0B sanity check on the market page. Bottom-up from buyers × frequency × basket, cross-checked against category size.
Where the volume lives today. Wins on inertia; loses the moment a buyer sees their own data as a savings report with evidence attached.
PO workflow and spend control at $2–8B scale. No vertical ontology, no attribute-level equivalency, no landed-cost truth, no credit.
Breadth and ordering rails, but specs are vendor-asserted, prices aren't landed, and nothing is cross-supplier equivalent or evidence-backed.
Bunzl (~$15B rev) proves the category's size — but it's a distributor, not neutral software. Structurally can't be the cross-supplier system of record.
Nobody accumulates evidence, equivalency, and payment truth across suppliers. That's the seat — and it compounds.
Each material value links to the document, page, and span it came from. Auditable by design.
Low-risk facts auto-approve; new suppliers, safety claims, and money always see a human.
Every approval and correction becomes evaluation data — match quality rises with volume.
~3.5% blended take on sourced GMV. Aligned with provable savings on every quote.
Net terms and credit, underwritten from proprietary DSO and payment history.
Ledger seats for buyer ops teams; supplier intelligence and category benchmarks.
Take rate opens the door. The ledger's data makes the fintech and software lines possible — and defensible.
The take rate is fair because the price is glass. A Gigabite quote is one figure that hardens in three stages — like a renovation quote — read by every module instead of recalculated, so the estimate, the order, and the settlement can't disagree.
Held 14 days; factory time reserved against it.
Re-checked against the market at that moment, then locked.
Estimate vs actual reconciled; savings rebated.
Each year is defined by what the next one demands: prove the ledger, become a platform, make GTM repeatable, scale nationally, compound.
Every stage gate is defined before the money is spent — the full backwards logic.
Approved matches train attribute-level equivalency no catalog vendor has.
Certification scope, quality, and landed-cost history compound into predictive scores.
Receivables data underwrites credit at margins outsiders can't price.
Pre-revenue and honest about it. What exists is the part most teams skip: a build-ready architecture and a scoped wedge — so the seed buys execution, not exploration.
Ledger, storage planes, canonical data model, HITL governance, service topology, build sequence — decided and documented.
Event taxonomy v1, review policy v1, and a testable done-state: upload → evidence-linked match → defensible quote → on-ledger.
Required-attribute schemas drafted for the first disposable categories; anchor-buyer profile defined.
Locked design system across product, docs, and this plan — built to look like the ledger it is.
[One line: years in foodservice distribution / private-label sourcing — the operating scar tissue behind the wedge.]
[One line: ledger and data-platform engineering — systems of record at scale.]
Plus both founders selling. Everyone reviews facts — the review queue is the onboarding program.
Sequenced against gates: first AEs at G1 · credit lead at G2, ahead of the embedded-finance launch · risk & compliance at G3.
PLACEHOLDER BIOS — TO BE FILLED BEFORE THIS DECK LEAVES THE BUILDING.
Ship the build sequence (Phases 0–5), land the first 20 anchor buyers, and exit Year 1 at a $2M ARR run-rate with repeat-order cohorts.
Backwards on paper. Forwards from Monday.
GIGABITE.AI · CONFIDENTIAL · JULY 2026 — This page is a plan summary, not an offer of securities. Detailed business, marketing, sales, operations, and financial plans are on this site.