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Go-to-market infrastructure you own.
Theo is an AI agent fleet that plugs into the email, CRM, and databases you already run. It identifies and researches your markets, verifies leads, drafts cold and warm outreach, tunes your website and campaigns, and carries proposals from RFQ to finished document — with a human approving everything that sends. Deployed as infrastructure into your tenancy, not rented as SaaS.
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What is AI go-to-market infrastructure?
An agent system that runs the assembly work of going to market — signals, research, verification, drafting, proposals, reporting — deployed into the systems you already run rather than rented as a subscription. The agents, prompts, playbooks, and logs live in your tenancy. We install it, operate it with you, and train your team to own it. There is no platform to migrate to, no per-seat product to cancel, and no engine that leaves when an agency does.
The first production numbers
Small batches, measured honestly. The volume playbook sends thousands of emails from burner domains and books a meeting per few hundred sends. Precision inverts that.
Fifty emails, one week, three qualified meetings with high-ticket prospects — a 6% meeting rate. High personalisation from real research, sent from the client’s own identity, every contact verified before a word was drafted.
We run Theo on our own business first. The same rails, the same guards, the same human pressing send — and the meetings that came out of it are how this page exists.
Deliverability is a by-product of discipline: multi-source verification, freshness-stamped signals, and off-ICP guards mean the list is clean before the first draft — no burner domains required.
The meeting isn’t the finish line. The same fleet carries the thread through to the proposal, the RFQ or RFT response, and the reporting — so booked interest becomes signed work.
How a Theo run works
Every run follows the same governed pipeline. The guards sit before the drafting, and the human sits before the send.
Five guarantees, enforced in the tooling
Not policies. Not habits. Hard rails in the architecture — the same rails we run on our own business every day.
Own the engine vs rent the stack
The outbound agency market runs on a rented stack of 2020-era SaaS — enrichment sheets, sequencers, secondary sending domains — bundled into $5,000+ monthly retainers with 3–6 month minimums. That generation of tooling predates frontier agents. This one doesn't.
Two ways to deploy it
As infrastructure in your tenancy (default)
The fleet is installed into your own cloud accounts and wired to your email, CRM, and databases from day one. We operate it with you under Partner Tier, train your team, and document everything. You own the engine outright; we are the builders and superintendents, never the landlords.
Start with the feasibility evaluation →As a hosted pilot, then migrated
Where speed matters, the first campaigns run on our production infrastructure — the same rails, the same guarantees — while your tenancy is prepared. Once the evidence is in, the system migrates to you with history and logs intact. Same destination: you own it.
Talk through which fits →Either way, it's installed through the Horizon Method: a fixed-price feasibility evaluation sizes your systems and data first (Block One, $8,000 all-in for month one), install phases are fixed-priced from ~$5,000, and operation runs under Partner Tier at $2,000/month — month-to-month, everything exportable.
It pays for itself — then funds everything built on top
GTM is deliberately the first install, because it's the one that generates revenue in weeks. The new pipeline pays for the install, and then funds the systems that need to exist underneath a scaling business — in order.
Most AI engagements start with the plumbing and ask the client to fund months of groundwork on faith. We run it the other way: the go-to-market install produces revenue first, and that revenue underwrites the data foundation, the service-delivery systems, and the scale work — each phase fixed-priced through the Horizon Method, each one standing on the last.
Built on the frontier, improving daily
Theo runs on the current Claude 5 family — Fable 5 today — and the rails are model-agnostic by design: when a better model ships, the fleet adopts it without a rebuild. The system also improves itself operationally: agents build and refine agents inside the same governed workspace, with humans reviewing anything before it is promoted to production. The stack the outbound industry standardised on was assembled between 2020 and 2024, before frontier agents existed. The gap compounds every quarter.
Common questions about Theo and owned GTM infrastructure
What is AI go-to-market infrastructure?
An agent system that runs the assembly work of going to market — signal monitoring, market research, lead verification, outreach drafting, proposal and tender assembly, reporting — deployed into the systems you already run rather than rented as a SaaS subscription. You own the engine: the agents, prompts, playbooks, and logs live in your tenancy, and a human approves everything that sends.
How is this different from hiring a cold email agency?
Outbound agencies typically run your campaigns on a rented stack of 2020-era SaaS tools bundled into a $5,000+ monthly retainer with a 3–6 month minimum — $15,000–$30,000 before you can properly evaluate results — and when the engagement ends, the engine leaves with the agency. Theo is installed into your tenancy as infrastructure you own, works through your real email identity on both warm and cold relationships, is fixed-priced in phases with evidence at every gate, and stays — fully documented — if we part ways.
How is Theo different from a Clay, Smartlead, or Instantly stack?
Those tools each solve one slice — enrichment, sequencing, sending volume — and someone still has to wire them together, pay for each seat, and police what the stack does. Theo is one governed fleet built on current frontier models: research, verification, drafting, proposals, and reporting in a single system with shared context, deployed into your tenancy. No per-tool subscriptions to stack, and the governance is structural: guards before drafting, drafts-only outbound, read-only CRM by default, append-only log.
Is AI outreach compliant with the Australian Spam Act?
The architecture is built for it. Nothing emails itself — output is drafts-only and a human presses send, which keeps a person accountable for consent, identification, and unsubscribe requirements under the Spam Act 2003 (and CAN-SPAM or GDPR where relevant). Off-ICP contacts never see copy because guards run before drafting, stale triggers fail loudly rather than firing, and the append-only run log means you can always answer why any message was drafted.
What happens to the system if we stop working with you?
You keep it. The infrastructure is deployed into your tenancy from the start, and everything — agents, prompts, playbooks, run logs, documentation — is yours with full export. Partner Tier operation is month-to-month on 30 days' notice. That's the structural difference between infrastructure you own and a retainer or subscription you rent.
What results is Theo getting?
Early production cohorts, measured honestly: a client campaign booked 3 qualified meetings with high-ticket prospects from its first 50 emails in one week — a 6% meeting rate — and running on our own pipeline, Theo booked 4 meetings in its first week. These are small, precision batches by design: every contact verified before drafting, copy personalised from real research, sends from the real sender's identity with a human pressing send. The volume playbook typically books a meeting per few hundred sends from secondary domains; precision inverts that.
Which AI models does Theo run on?
Current frontier models — the Claude 5 family (including Fable 5) today — and the rails are model-agnostic by design, so the system improves as models do. Theo also self-improves operationally: agents building and refining agents inside the same governed workspace, with human review before anything is promoted to production.
Adjacent reading
AI infrastructure as a service
The homepage overview of the model: lay the infrastructure, implement it, train the team.
See the overview →AI for engineering & advisory firms
The same governance rails applied to a compliance-grade vertical — the firm brain, ring-fenced tiers, engineer sign-off.
See the vertical →AI Visibility Scorecard
How AI engines see your business today — the demand side of the same GTM problem.
Check your visibility →