The problem
Sales teams doing cold outreach either send generic messages or burn hours researching each prospect by hand across search engines, professional networks and review sites. We built LeadIntel as our own productized answer: upload a lead list, pay once, and receive a complete research dossier by email — no account, no subscription.
Researching a single prospect properly takes roughly 15-20 minutes of manual searching across search engines, professional networks and review platforms.
Generic cold outreach ignores real, evidence-backed pain signals that are publicly available but time-expensive to find.
Existing data vendors sell contact rows, not usable research — no context, no sources, no confidence rating.
Same-name company collisions make manual research error-prone: reviews and news frequently belong to a different business with the same name.
What we built
We built a fully automated research pipeline behind a one-shot checkout: no accounts, no dashboard to learn — the deliverable is an Excel dossier in the customer's inbox. We chose a custom backend over workflow tools because the quality problems (entity disambiguation, evidence-graded pain signals, calibrated confidence) demand fine-grained control that no-code automation cannot express.
Research dossier
Sample rows
| Prospect | Pain signal | Evidence | Confidence | Source |
|---|---|---|---|---|
| Operations Director, regional logistics firm | Hiring three dispatch coordinators | Evidenced | High | Careers page |
| Head of Sales, B2B software company | Public complaints about slow demo follow-up | Evidenced | Medium | Review site |
| Owner, multi-location clinic group | Likely manual appointment reminders | Inferred | Low | Data gap noted |
Storefront and checkout
Payment, tier selection and honest post-payment states
Research pipeline
Web search, page extraction and AI synthesis for each lead, run in parallel batches
Entity disambiguation engine
Multi-gate filtering that detects the prospect's true company domain and rejects same-name look-alikes
AI synthesis
Structured analysis producing a company snapshot, role priorities and evidence-graded pain signals, with a confidence calibration layer
Report generator
Formatted Excel dossier with quality flags, source links and data-gap notes
Order system
Payment verification, replay protection, queue-based processing with crash recovery, and branded confirmation emails
How it works
From trigger to result.
How the system works
7 stages
01Upload and checkout
TriggerCSV or Excel lead list, paid through Stripe.
02Payment verification
LogicVerified server-side. A webhook independently records every payment.
03Order queue
DataOrder stored and enqueued with crash recovery.
04Research worker
LogicTargeted web searches, true-domain detection and extraction of the most relevant pages.
05AI synthesis
AIStructured output with evidence-graded pain signals and hard confidence caps.
06Report generator
Result26-column Excel dossier with sources and data-gap notes.
07Delivery
ResultStored privately and emailed to the customer.
Our approach
How we worked.
Build
Core research pipeline, quality mechanisms, landing page, and checkout.
Hardening
Replay protection, webhook safety net, queue-based crash recovery, order tracking, honest failure states.
Launch
Live Stripe keys, real-money verification, first paid order processed end-to-end.
Iterate
Confirmation emails, FAQ sections and support flows, shaped by founder feedback.
The outcome
What changed.
First real customer payment processed, researched, and delivered end-to-end without manual intervention.
Every order is traceable from payment to per-lead research status, so support questions resolve in minutes.
Reports state what is verified versus inferred, with source URLs and honest data-gap notes on every lead.
Questions
What people ask.
Can AMZU build a complete paid SaaS product end to end?
Yes — LeadIntel is the proof. We designed, built, and launched the entire product: AI research pipeline, Stripe payments, reliability engineering, and the customer-facing site. It processes real orders today with no manual steps.
How does the AI keep the research accurate?
Every claim must trace to a real source. The pipeline detects the prospect's true company domain and rejects same-name look-alikes, grades each pain signal as evidenced or inferred, and applies hard caps to confidence scores so thin data can never look authoritative.