Context Engineering for GTM
Your GTM Operating System
Your ICP, positioning, competitive landscape, and first-party data, structured once, circulating across every function, and run by agents instead of re-explained every session.
Book a strategy callNo pitch, just strategy.
Your motion doesn't compound.
Nothing compounds.
Last week's research doesn't make this week's work better. Every campaign starts from a blank page.
Your data is trapped.
The asset that could drive traffic and authority is sitting in a database nobody queries.
Functions are siloed.
Sales intel doesn’t reach content. Content insight doesn’t reach outbound. The same context gets rebuilt three times.
The AI sounds generic.
It’s never met your business, so it writes like it. You re-explain your ICP every single session.
Context layer → operating system → compounding.
A GTM Operating System is built in that order. First comes the context layer, holding your ICP, positioning, competitive landscape, and proof, structured once. Then come the skills and agents that draw from it, so account research, content, outbound, and competitive intel all run on the same source of truth. Then it compounds, because every output enriches the context for the next one. You own the layer, and the model stays swappable.
Advising a portfolio? This is the infrastructure that standardizes an AI-native GTM motion across portcos without rebuilding it from scratch in each one — so a portco can expand into new verticals and geographies before committing to a full marketing buildout. The same handoff model runs across the portfolio, so the work is less dependent on any one operator's hours.
Book a strategy call →The machine, in six parts.
Generic capabilities, built into your repo and wired to your stack. Build the ones you need first; the rest are already designed to chain in.
Context Layer
Your business, structured once, referenced everywhere.
A repo-resident knowledge layer — ICP, positioning, competitive intel, proof points, voice. Calls and research feed it continuously, so every agent draws from one source of truth. The part everyone skips, built first.
Data-to-Asset
Package your proprietary data into traffic-seeking content assets.
Programmatic pages and content products built on first-party data you already own, turning a dataset that just sits in a database into a standalone content asset and a newsletter flywheel. (See the proof section for what one of these has done.)
Content Production Loops
Consistent expert content in your voice, without the stall.
An SEO-and-context-driven content map, then research → draft → polish loops producing long-form, LinkedIn, and a weekly newsletter without losing your strategic voice or going stale. Each piece enriches the context for the next.
Competitive Intelligence
Always know what the competition is shipping.
Agents that scrape competitor posts, whitepapers, and thought leadership, then synthesize landscape maps, battlecards, and displacement angles indexed to your positioning.
Sales Enablement
Every rep discovery-ready before every call.
Account research that turns 45–60 minutes of manual prep into a 3–5-minute brief: company profile, tech stack, buying committee, MEDDPICC map, and pain hypotheses linked to your proof.
Signal-Based Engagement
Use account signals to prioritize better-timed outreach.
Social listening and trigger detection wired to account research, so outreach leans on real intent signals instead of cold cadence.
What this has actually produced.
A newsletter system
Drafted, quality-gated, and shipped every week by agents. Ten issues and counting.
Read the latest issue →
A data scraper into an automated website
A scraped, deduplicated events database that publishes and updates a consumer site on its own schedule.
Visit the live site →A signal engine that tells sales who to call today
Engagement, hiring signals, market news, and CRM activity in — one ranked list of accounts showing buying intent out, delivered to Slack every morning.
A full ICP campaign, end to end
From a real CRM's deal history to ready-to-send outreach. Every step run by the system; every touch reviewed by a human.
This website
The site you’re reading was built and audited by the system it sells — 250+ pages, link-checked and gated before every ship.
Browse the build →A music video, end to end
It started as a prompt. A finished music video came out the other side — written, generated, and cut by the system.
Full video, 5:06
The daily ops schedule, plus a consumer site you can click through right now.
You own the system. No lock-in, no per-seat tax.
Two models, both ending with you in control. Either I hand you the full system — the repo, skills, context layer, integrations, and security setup — and you run it. Or I spend focused time ingesting your context, building collaboratively, and tuning the skills to your business. Most builds reach a working system in about four weeks. Either way you walk away owning that system on your own private repository, yours to modify, extend, or hand to the team you hire once the motion is proven — with no per-seat fees and no vendor lock. Scope comes out of a strategy call, not a price list.
The questions buyers actually ask.
Pulled from real engagement conversations.
What does it cost?Scope
Engagement pricing comes out of a scoping conversation, not a rate card I publish, because the build depends on your data and your motion. Engagements are fixed-scope builds measured in weeks, not retainers. The self-serve repo has a public price on the pricing page if budget shapes the decision.
Who owns the repo when we’re done?Ownership
You do, permanently, with no per-seat fees. I keep the methodology I arrived with. Every playbook and skill configuration the engagement produced stays in your private repository.
How do we actually get our hands on it?Ownership
Two models. Either I hand you the full system and you run it, or I spend focused weeks ingesting your context and building collaboratively with your team. Both end with you in control.
Does my data train your AI model?Security
No. The system calls the model under commercial terms that exclude training on your data. Your files live in your repository, on your accounts, and the model reads what a session needs rather than absorbing your business.
Does our data go anywhere outside the organization?Security
The repository lives on your infrastructure and the integrations run under your credentials. What leaves is the specific context a task needs, sent to the model under no-training commercial terms. Integrations can start read-only and widen only after you trust the outputs.
What if we want to revoke access?Security
Everything runs on your keys and your repo, so revoking me is rotating credentials and removing a collaborator. There’s nothing to hold hostage, and that’s deliberate.
Is this Claude-specific? The model landscape keeps shifting.Model churn
The context layer is plain files by design. The same repository already runs against multiple engines — a single shared instructions file drives Claude Code, and an identical companion file drives Codex and Antigravity, so the same context layer answers to whichever engine you point at it. Switching engines is closer to a day of work than a rebuild. You rent the engine. You own the harness.
How do reps actually use this day to day?Adoption
They mostly don’t touch the system at all. One operator runs it, and the team consumes its outputs where they already live, in email and the CRM and the deliverables themselves. A rep gets a call brief in their inbox, not a terminal window.
Honestly, is this a lot? Are we ready for it?Adoption
It’s a lot if you adopt it all at once, so you don’t. The build order is the context layer first, then the one workflow that matters most to you, then expansion as trust builds. One commercial leader who asked a version of this was connecting his own integrations and building his own reports within weeks. The system teaches itself to you as you use it.
Can one operator really run this?Capacity
That’s what the system is designed around. One person with the harness runs the research and outbound motion at a volume that previously took a team, and the playbook accumulates in the repo as they go. When you eventually hire, the methodology is already written down.
What happens if the person who runs it leaves?Capacity
The motion lives in the repository, not in their head. The context and the decision history stay put, and the next operator inherits a running machine with a manual. One client generated their own user manual from the system to onboard teammates.
You’re one person. How many of these can you run at once?Capacity
A few, sequentially and with focus, which is exactly why the model is built the way it is. The system goes into your repo from week one, so delivery never bottlenecks on my calendar after handoff. And the repo handoff path exists for teams that want the architecture without waiting on mine.
Book a strategy call.
No pitch, just strategy. We'll map your context, your data, and the fastest path to a motion that compounds. If a GTM Operating System isn't the right move, I'll tell you.