Bid response, from weeks to same day.
Edward Don & Co. reached full ROI in 5 months.
Bid prep used to be weeks of manual archaeology.
A 200-line bid arrives as a PDF, spreadsheet, or competitor's Excel. A rep cross-references item by item against the catalog. Hours become days; days become weeks. The largest bids never get a competitive response — they're triaged out, lost to whoever turns around faster.
Manual cross-referencing
Manual cross-referencing
Three places manual quoting costs you. AutoRFQ closes all of them.
Distributor revenue | Annual large RFQs | Couldn't respond (~30%) | Revenue we walked from |
|---|---|---|---|
$50M | ~100 | ~30 | $600K |
$100M | ~200 | ~60 | $600K |
$200M | ~400 | ~120 | $2.4M |
$250M | ~500 | ~150 | $3.0M |
$500M | ~1,000 | ~300 | $6.0M |
$1B | ~2,000 | ~600 | $12.0M |
Distributor revenue | Bids responded annually | Add'l wins from speed | Annual GP from faster wins |
|---|---|---|---|
$50M | ~70 | ~4 | $77K |
$100M | ~140 | ~9 | $158K |
$200M | ~280 | ~18 | $317K |
$250M | ~350 | ~21 | $370K |
$500M | ~700 | ~44 | $774K |
$1B | ~1,400 | ~87 | $1.53M |
Revenue | Labor reclaimed | Bids recovered | Speed-to-Bid GP | Annual impact |
|---|---|---|---|---|
$50M | $36K | $120K | $77K | $233K |
$100M | $72K | $240K | $158K | $470K |
$200M | $144K | $480K | $317K | $941K |
$250M | $181K | $600K | $370K | $1.15M |
$500M | $361K | $1.2M | $774K | $2.34M |
$1B | $722K | $2.4M | $1.53M | $4.65M |
Your reps have the catalog memorized. Your inbox has the bids. The work in between still takes weeks.
Three things have to happen fast — intake, matching, and a commercially-aligned quote out the door. Each one is a step your existing stack wasn't built for.
Match line by line. Cap out at human speed.
Listed in the ERP. Missing what you'd need to actually match against it.
Bids in every format imaginable. Hours gone before matching even starts.
Four steps, from document in to quote delivered.
Every match runs through live catalog state, your customer rules, margin guardrails, and an override loop that learns from every rep correction.
Import
Match
Prioritize
Review, Send & Track
The model is the easy part. The harness is the product.
Every part of the bid pipeline. One quote workflow.
Built for distributors and the catalog, ERP, and CRM they already run.
Format-Blind Ingestion
PDFs, Excel, Word docs, photos of handwritten orders, competitor exports — all parsed line-by-line. No reformatting. The unusual formats are where manual triage burns the most hours.
Structure-Aware Extraction
Section headers carry context that changes matching constraints — "Section 3: Bakery Items" tells the engine something a raw text parser can't see. AutoRFQ reads document structure, not just characters.
Field-Level Decomposition
A single ambiguous line — "12oz compostable hot cup, printed, lid req'd, 1000/cs" — becomes 8 structured fields: product type, size, material, print option, accessory, pack size, UOM, quantity.
Cross-Line Context Resolution
AutoRFQ infers vendor preference at the RFQ level in addition to the line level. A brand cue on a single line informs sourcing decisions across the entire document.
Catalog Matching at Scale
Each line matched against your full catalog with confidence scores and transparent reasoning every rep can defend. Matching logic improves with every rep correction.
Margin & Brand-Priority Engine
Brand and vendor priorities applied automatically. Margin guardrails enforced per customer, per category. Cross-sell suggestions surfaced at the line level — every quote commercially aligned before a rep reviews it.
Quote Composition
One-click adjustments. Branded quote document generated automatically — your template, your terms, your logo. Send via email or trackable link without leaving the workflow.
Engagement Tracking
Real-time signal on every quote sent. Opens, views, forwards, time-on-page — surfaced back to the rep so follow-up lands at the right moment, not three days late.
Customer & Bid Memory
Each customer's preferred SKUs, format quirks, and historical pricing remembered. Rep corrections captured as institutional knowledge — not lost when senior reps retire.
Bid ROI Dashboard
Real-time visibility — bids responded to, win rate, time-to-quote, GP per bid. Per-rep, per-customer, per-vertical breakdowns.
Every place a bid actually arrives.
Five intake surfaces. One engine. Each one tuned to where the RFQ lives — all landing in the same clean, send-ready quote.
Inbox Capture
Web Workspace
In-CRM Quoting
Clean Submit
Customer-Facing
8 months, 210K+ products matched. 5 months to full ROI.
90%+
68%
210K+
5 mo.
Structured RFQ Automation
Knowledge Sharing
Manage by Exception
Human-in-the-Loop Learning
Connects to any ERP. Works with the CRM you already use.
Real-time API, scheduled imports, or hybrid. No ERP replacement, no CRM change.
SAP
Microsoft Dynamics
NetSuite
DDI
Epicor
+ More
Enterprise-grade. Always.
- Role-based access with SSO
- Encryption at rest and in transit
- Full data isolation — your data never commingles
- Complete audit trail on every prediction and action
- SOC 2 Type II audit in progress
- Regular penetration testing and vulnerability assessments
Questions buyers ask before they book.
How is this different from OCR or basic document extraction?
Different problem. OCR turns a PDF into text. AutoRFQ turns 247 unstructured lines into 247 specific SKUs from your catalog, with confidence scores, brand priorities applied, margin guardrails enforced, and a substitute recommended when there's no direct match.
Document extraction is the first 5% of the work. The other 95% — matching, prioritization, commercial alignment — is what AutoRFQ is built for and what generic OCR can't do.
What if our product data isn't clean?
Match quality doesn’t depend on it. Before SETVI ever sees a bid, our agents enrich your catalog from authoritative sources — manufacturer data sheets and verified product information online — pulling pack sizes, dimensions, certifications, brand mappings, and substitutes as an enrichment layer that sits behind the matching engine.
When a bid comes in with a vague line like “40x46 1.5mil blk liner,” the engine isn’t matching against your patchy product name field. It’s matching against the enriched representation. Your original catalog stays untouched. Most distributors don’t have clean data — most don’t need to.
Will our reps actually trust the AI's recommendations?
They do, once they see the reasoning. Every match comes with a confidence score and explanation reps can read in two seconds. The rep stays in the loop: they review, adjust, and send. They aren't asked to rubber-stamp anything. Edward Don's deployment scaled across the sales org because reps saw it as a tool that gave them their largest bids back, not one that replaced their judgment.
How long does implementation take?
Typical timeline: 4–6 weeks. Weeks 1–2: ERP and catalog data integration, AI enrichment of product records. Weeks 3–4: brand priorities, margin guardrails, and customer-tier pricing configured; pilot bids run through the system. Weeks 5–6: rep enablement and rollout, CRM sync live. Edward Don reached full ROI in five months from go-live.
What ROI can we expect?
Most teams see meaningful time savings in the first month and full ROI within six. Edward Don, the flagship deployment, reached ROI in five months: 68% faster turnaround, 210K+ products matched, 7,000+ hours saved, CIO 100 Award. The bigger lever isn't time saved on existing bids — it's the largest bids that used to get triaged out becoming routine work.
How is this different from running RFQs through ChatGPT or a generic LLM?
Here's what breaks when you try it: a generic LLM has no idea what's discontinued in your catalog, who's on what brand restriction, where your margin floors sit, or which substitute is allowed for which customer. And it can't know what it doesn't know — so it returns plausible-looking matches with no flag that anything is wrong. The rep won't catch the bad line until the customer does.
AutoRFQ isn't better language understanding. It's a runtime harness around the model — your live catalog state, your customer rules, your margin guardrails, a full audit trail, and an override loop that learns from every correction.
The model is the easy part. The harness is the product.
What about formats AutoRFQ has never seen — handwritten orders, photos, weird PDFs?
Handled. AutoRFQ ingests PDFs (clean or scanned), Excel in any layout, Word docs, images, photos of marked-up catalog pages, and competitor exports. Vision-capable extraction handles handwriting and photos. Reps see flagged items for review when extraction confidence drops — never silent failures.
What happens when a rep disagrees with a match?
One click to override, with a reason captured. Every override trains the model — for that rep, that customer, and the broader catalog. Tribal knowledge that used to live in one rep's head becomes a system-wide rule after a handful of corrections. Six months in, the system reflects how your best reps actually work.
Do we need to change our ERP or CRM?
No. AutoRFQ sits alongside your existing systems — product data flows from your ERP (SAP, Dynamics, NetSuite, DDI, and others); quotes sync to your CRM (Salesforce, HubSpot, and leading CRMs) auto-linked to contacts and opportunities. Your team keeps working where they already are.
How is our data protected?
Fully isolated, encrypted at rest and in transit. Role-based access controls with SSO and a complete audit trail on every match and rep action. SOC 2 Type II audit currently in progress. Customer data never commingles with other tenants and never trains shared models — your catalog, customer history, and pricing logic stay yours.
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