Unilateral rule risk
One algorithm change, one category sweep, one mistaken suspension, and years of work can reset overnight. The appeals process is written by the same platform.
Independent-site AI operations
One agent.Your entiresite operation.
From cold outreach to SEO content to inquiry follow-up — an operations teammate that never resigns, running on your own server and domain, with every byte of data yours.
- real tools, zero mocks
- 0+
- live data sources monitored
- 0
- LLM providers, hot-swappable
- Custom
- outbound lead-gen engines
- 0
Build on someone else's platform, and the rules are never yours.
Marketplace platforms solve early traffic, and plant a structural risk: your store, rankings, customers and settlement all run on rules someone else writes.
Customers you can't keep
Buyers belong to the platform, not to you: contact details are walled off and repeat purchases get redistributed. Nothing you build there leaves with you.
Margins bleeding upward
Commissions, ad slots, deposits, payout cycles — the rates only move one way. The bigger your business grows, the weaker your bargaining position becomes.
An independent site is the only way to take the leverage back: your domain, your data, your customer relationships. TradeMax Agent hands the hardest parts to an operations system that never resigns — deployed privately on your own server, where not even we can hold you hostage.
Who it's for
Top-tier operations, by default.
Operational judgment that takes years to build is codified into the agent's workflows as built-in SOPs: beginners start from an expert baseline, and seasoned operators hand execution to the system to compound their leverage.
0 → 1 · Starting from zero
An expert's playbook from day one
Keyword research, E-E-A-T content standards, inquiry triage, follow-up cadence — professional judgment that normally takes years is built into every workflow as SOPs. You don't have to learn SEO before doing SEO: the agent proposes each action with its reasoning on display, every write waits for your confirmation, and every run doubles as training.
- Best practices built in: E-E-A-T dimensions, search-intent classification and A/B/C inquiry scoring
- A compressed learning curve: AI-proposed actions behind a human confirmation gate — learn by doing, without the costly mistakes
- One instruction, one complete workflow — no technical background required
1 → N · Seasoned operators
Multiply your throughput to team scale
Your experience sets the strategy; the agent executes around the clock. Lead generation, content, follow-ups and monitoring run in parallel, repetitive execution is fully delegated, and you step in only at the decision points. Once it's running, the confirmation queue takes about ten minutes a day — the system runs the rest.
- Operating leverage: acquisition, content, follow-up and monitoring pipelines run in parallel, drawing on 76+ real tools
- Follow-ups never lapse: outreach sequences advance on day 0/3/7 and stop the moment a customer replies
- Routine work runs itself: weekly reports and scheduled checks fire on time, with 11 data sources under continuous watch
Capabilities
Everything a site operations team does. One agent.
Eight capability clusters, all wired to real APIs — no mocks, no demos-only features.
Core engine
Autonomous agent runtime
A single instruction triggers a continuous tool loop until the task completes — with budget guardrails, streamed reasoning, visualized tool calls and step-by-step run replay. Built in-house, not a framework wrapper.
Module details →Content
SEO content pipeline
Keyword to outline to draft to publish, with E-E-A-T checks, search-intent classification and cannibalization pre-flight built into every article.
Module details →AEO
AI-engine optimization
Get cited by ChatGPT, Perplexity, Gemini and Claude: llms.txt generation, a 10-point AI-readability score per page, citation tracking across engines and bot crawl logs.
Module details →Outbound
Twin lead-gen engines
A keyword engine reverse-searches buyers; a LinkedIn engine targets decision-makers. Fit-scored, then email-first dual-channel outreach — unanswered mail auto-escalates to a LinkedIn connect. Human approval is the only outbound gate, with global dedupe across every batch.
Module details →Pipeline
Inquiry intelligence + CRM
Heuristic A/B/C lead scoring at zero LLM cost, a kanban with follow-up tasks, and closed-deal outcomes fed back into the scoring prompt.
Module details →Closed-loop email
Inbound mail auto-classified into inquiries, one-click replies, timed outreach sequences that stop the moment a customer answers.
Module details →Analytics
Data command center
GA4, Search Console, Clarity, Google Ads, SERP rank tracking, content-decay detection, competitor and backlink monitoring — one pane of glass.
Module details →Trust
Cost governance & safety
Per-task model routing with a hard monthly budget cap, six-level role permissions, and mandatory human confirmation on every write operation.
Module details →How it works
Four loops, running while you sleep
The agent operates your site as a continuous cycle — each phase feeds the next.
01
Discover
The agent hunts for buyers before they hunt for you.
- Keyword engine: reverse-search buyers from company-site keywords
- LinkedIn engine: target decision-makers directly
- Fit scoring, then automatic first outreach
02
Create
Content that ranks on Google — and gets cited by AI engines.
- Keyword → outline → draft → publish pipeline
- Schema.org structured data, injected and reversible
- llms.txt + AI-readability scoring for ChatGPT-era search
03
Convert
Every inquiry scored, routed and answered — nothing leaks.
- A/B/C inquiry scoring at zero LLM cost
- Inbound email auto-threaded into the CRM
- Sequences stop automatically when a customer replies
04
Compound
Results feed back in. The system gets sharper every week.
- Closed deals re-tune lead scoring — no retraining
- Content-decay radar flags fading pages for refresh
- Weekly reports, rank tracking and competitor feeds on cron
Data flywheel
Sharper with use isn't a slogan — it's 10 places data flows back
The bar is strict: for a loop to count, the data has to change what the system decides next — scoring, routing, generation — not just land in a report. Every deal you close, every misjudgment you correct, every AI citation you earn feeds back into the model. Ten live spokes, each owning one stretch, and the system is sharper every week than the week before.
Prospect-score calibration
Who replied and who actually closed, aggregated by industry / seniority / countryThe next batch's fit scores calibrate accordingly
Reply-copy evolution
Outbound emails that genuinely won a reply or a dealAI reply drafts learn the structure that works — not a template
Smart assignment
Each rep's real 90-day conversion rate and current loadLeads route to whoever is likeliest to close — with newcomer protection, no winner-take-all
Classifier self-correction
Misses recovered by hand and false spam flagsEmail classification repeats the same mistake less and less
Content win-rate
The queries you've already won in search over 90 daysNew outlines align to validated angles instead of gambling
Outreach openers
The openers in your sequences that actually earned repliesNew sequences learn the opening that lands
AI-citation evolution
The gap between your AI citations and competitors'Content evolves toward what actually gets cited by AI
Grading calibration
The real conversion rate of A / B / C inquiriesThe grading scale self-checks — neither inflated nor buried
Closing-MVP weighting
Knowledge docs a reply cited that then helped close a dealThe next reply reaches first for knowledge that has closed before
Buyer language
How buyers actually describe this model and category in real inquiriesProduct copy and taglines speak the buyer's own words
The cold start is honest: with little data, output tracks a general model; the flywheel only bites once a few dozen deals, corrections and citations have accumulated.
Architecture
A MaaS stack: seven layers, one base.
Model-as-a-Service is the foundation — models plug in, route per task and answer to a budget. Every layer above owns one job, and any layer can be swapped or audited without touching the rest.
Tenant & white-label
Brand, audience, product field model and palette are all configuration, hot-reloaded on save. One platform white-labels for any independent site.
- Hot-reload config
- Two-tier branding
- Multi-site reuse
Governance
Six roles enforced per endpoint, confirmed writes, an anti-hallucination protocol and four-dimension cost audit — cutting across every layer below.
- Six-role RBAC
- Confirmed writes
- Anti-hallucination
Capability modules
Fifteen modules, from SEO, AEO and sourcing to CRM, email and scheduled tasks — each standalone, each feeding the next.
- 15 modules
- Standalone
- Composable
Agent runtime
A hand-written agent loop: one instruction drives up to 25 consecutive tool calls behind four guardrails, resumes from checkpoints, and replays end to end.
- 25-round loop
- Four guardrails
- Full replay
Data & tools
76 real tools wired to 11 live data sources; a tool with missing credentials stays out of the registry — it never fakes data.
- 76 tools
- 11 data sources
- Zero mocks
Model layer
The MaaS core: Anthropic, OpenAI-compatible, DeepSeek or any endpoint plugs in with automatic failover; task-level routing sends light work to cheap models, and the monthly budget is a hard brake.
- Pluggable providers
- Auto failover
- Per-task routing
Private infrastructure
A single container on your own server and domain — snapshot backups, health-gated releases, automatic rollback, and data you can take with you whole.
- Your own server
- One-click rollback
- Portable data
Platform
Built like infrastructure, not a demo
The agent core is hand-written — no framework wrappers — and every capability is wired to real APIs.
Multi-provider LLM core
Anthropic, OpenAI, DeepSeek or any OpenAI-compatible endpoint. Conversation history stays provider-clean, so switching is seamless.
Human-in-the-loop writes
Every write operation returns a pending action that a person must explicitly confirm — the core defense against prompt injection.
Hallucination defenses
Intent-aware temperature, data-grounding verification and tool-markup stripping keep answers tied to real data.
Private by deployment
Runs on your own server and domain — every byte of data is yours and portable at any time. Single-container Docker with snapshot backups, health-gated deploys and automatic rollback.
Cost under control
Cheap tasks route to cheap models; a hard monthly budget cap pauses spend before it surprises you.
Yours to rebrand
Brand, industry, audience, product field model and palette are all configuration — edit in the console, save, and it hot-reloads. One platform white-labels for any independent site.
Get started
See it run on your site
Tell us about your independent site and we'll walk you through a live agent session on real data.
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