mail.seges.ai · done-for-you outbound
Turn outbound intoan agent that checksits own facts.
Not another email tool. A done-for-you outbound system built on one rule — never fabricate. You hand over the accounts; an agent runs verified research → personalized cold email → automation → delivery tracking.
agentmail × Outreach-Intel × Salecraft × Social-Engineering — grounded in verification.
The problem
Cold outbound is broken — and one more email tool won't fix it.
Fabrication kills trust and deliverability
Blast sends, invented rankings and relationships, the wrong name — recipients spot it instantly and spam filters punish it. Credibility burns in a single shot.
Generic copy gets ignored
Without a real insight about this one company, every send lands in the trash. Personalization needs actual research — not swapped variables.
Self-built senders have no brand avatar
To show a logo in Gmail you need BIMI — DMARC plus a ~US$1k/yr VMC — or Google Workspace. A purely self-hosted sender 'looks unofficial' to a Taiwan audience.
Taiwan buyers prefer Workspace / LINE
So selling an 'email client' is uphill. What's actually sellable is the service that gets the work done for them.
The product isn't a tool — it's done-for-you. mail.seges.ai is that service's front door.
The wedge
Agent-as-a-Service: you give the accounts, the agent runs the whole loop.
One Agent SDK (Claude or Kimi) drives our remote plugin library — Outreach-Intel + Salecraft + Social-Engineering — to run, on the client's behalf:
Why a service
Clients don't want to learn a tool or build a team. They want 'N more qualified conversations next month.'
Why us
The full verification discipline + the plugin library + an already-deployed, shared service-system. Everyone else starts from zero.
Why now
Buyers are starting to ask ChatGPT 'which company should I pick?' Whoever builds the AI-search data layer first gets recommended first.
Already built & verified
This isn't a concept — the system runs today, and every link opens.
Plus a probiotics demo page, quotations and outreach packs — all generated and verified. Every item is a real asset, not a screenshot.
The loop
You give the accounts; the agent runs this line — with a consent gate at every send.
- 1Reconscreenshot / scrape / 8-layer extract→
- 2Verifyanti-hallucination gate→
- 3GEO/AEO diagnosis→
- 4Qualify & scorepay × want × gap × leverage→
- 5Compose→
- 6Owner consent gate→
- 7Send→
- 8Track / replies → CRM
Heavy lifting on the server
Playwright/Chromium, screenshots, image extraction and sending all run on the already-deployed, always-warm service-system. The client is a thin shell — lowest cost.
Two gates before send
(1) an outreach-auditor refutes every claim; (2) legal-engineering reviews factual / comparative / regulated claims (pass / fix / block).
Owner consent gate
Before any real send, the judgment + citations + draft become a password-gated HTML decision report emailed to the owner — sent only after they confirm.
Sending identity · the trust & deliverability edge
Hybrid sending: ops on our rails, client mail from the client's own Workspace.
Deliverability is a trust problem. So we split the sending identity by purpose — and that split is the differentiator.
Ops & internal
Our own automation, owner-confirmation reports and internal notifications go through agentmail (Resend outbound / Cloudflare inbound) — already LIVE in production on seges.ai.
Client-facing cold email
The real outreach is sent from the client's own Google Workspace via the Gmail-MCP draft → send model: the agent writes a draft into the client's mailbox, and only a confirmed draft is sent — as the client's own domain, with its existing SPF/DKIM/DMARC reputation and brand avatar.
The result: maximum deliverability and trust — the mail comes from a real, reputable domain the recipient already recognizes — while every send still passes our verification and consent gates. No BIMI bill, no 'unofficial' self-built sender.
One system, not point tools
Five plugins + one shared compute layer, run as a single system.
Outreach-Intel
engineRecon · verify gate · GEO/AEO diagnosis · qualification scoring · compose · owner → send loop.
Content-Gen
assetsgpt-image-2 imagery · landing pages · quotations · deploy. The demo pages are its output.
Salecraft
brand / conversionBrand · campaigns · the stripe landing-page pipeline · pricing · conversion analytics.
Social-Engineering
psychology / legalProfiling · influence · copy · cross-culture · the legal-engineering claim-review gate.
agentmail
sending14 tools · LIVE in seges.ai production (Resend outbound / Cloudflare inbound).
service-system
shared compute~260 routes · deployed & always-warm · build once, every client shares it → near-zero marginal cost.
Honest labels: built vs planned
What we promise — and what's still on the way.
Honesty is the pitch: we mark exactly what exists today and what's on the roadmap — which is itself part of being a 'verifiable' brand.
Trust-first land & expand
Anchor to real prices, start at zero up-front, grow into a retainer.
Free
A free demo page + diagnosis first. Zero up-front, zero risk. Anchored to the cost of being invisible in AI search.
First order
NT$25k–35k
One GEO landing page.
(JAMBO's first milestone NT$28k, paid on go-live.)
Retainer
Monthly
Done-for-you outbound + monthly GEO content + tracking.
Recurring fees aren't discounted.
Flagship
Happy client → testimonial set → become Seges's public case study for that vertical (like DrSkin).
Agent-as-a-Service packaging: a one-time build fee + a monthly operating fee (per agent seat) + per-activity usage. Every price anchor comes from a real, already-issued quote (JAMBO NT$28k / 52k / 90k three-tier · a comparable GEO page NT$25–35k · a probiotics upgrade NT$12k) — not numbers pulled from thin air.
Why the margin is high
The front door has near-zero idle cost; the heavy work reuses compute we already built.
Infrastructure cost (very low)
- mail.seges.ai = Cloud Run, min=0 / 256Mi / 1 vCPU / max~3 → scale-to-zero
- Front-door idle cost ≈ NT$0; single-digit US$/mo at low traffic
- Heavy compute (recon / screenshots / images / sending) reuses the already-deployed service-system — not rebuilt per client
Variable cost (estimable, controllable)
- Per-campaign LLM tokens (Claude / Kimi) — the main variable
- Optional BYO-key services: Hunter free tier, Sheets (service-account, free)
- Human-in-the-loop consent / review time (falls as automation grows)
≈NT$0
front-door idle infra cost
1×
service-system built once, shared by all clients
High
retainer − (tokens + free-tier APIs) = high margin
What it means for connact.ai: low marginal cost + existing shared infrastructure = nearly pure margin per additional client.
The moat
Verification discipline: competitors blast, we verify.
This system exists to prevent the failure classes it learned from a real outbound campaign that broke when claims were made without checking. Every factual claim is tagged [VERIFIED] / [LIKELY] / [UNCERTAIN] and must survive an independent outreach-auditor trying to refute it before it can be sent.
Real failures it prevents
- Inventing 'Mr. Li' from a registry — the person actually signs as Jason
- Fabricating a 'Gemini ranks you #5' that was never run
- Calling a supplier ↔ agency relationship 'competitors'
Real catches in one recent round
- Corrected a site mislabeled http-only (HTTPS was fine)
- Caught an agency /contact page returning a 404
- Left an email blank rather than fabricate one (iOutback)
- Removed an unverified competitor-results claim from a draft
In a world flooded with AI content where anyone can blast, verifiable is the premium and the deliverability. That discipline can't be copied.
Let's run a free demo
Hand us one prospect. We'll send back a verified outbound draft.
Tell us a target account and your goal. We'll run verified research and a personalized draft — owner-gated, never sent without your say-so.
- Free first demo + diagnosis — zero up-front
- Every claim verified before it reaches you
- Sent from your own Workspace, owner-confirmed