TP4 · FIT CHECK
We're best suited for B2B companies where a simple lead search isn't enough to find your ideal customers, and for gtm teams that want to bring software-engineering discipline into their daily operations.
U8 · GTM AS CODE
We turn your definition of a customer into a small application an AI agent can run: custom probes we build for the evidence nobody sells, plus the APIs of the tools you already use. Best when your team knows who to sell to and simply needs more of them to prospect.
FIG · U8 · run log
U9 · CUSTOM LIST BUILDING
We reverse-engineer your ICP into datapoints, then build them from wherever the raw data sits: a public registry, a database, a site we have to scrape. No data source too hard. You get accounts and people, evidence behind each, and the definition written down.
FIG · U9 · tam map
U10 · DIAGNOSTICS
A short engagement with two shapes: find where your GTM is stuck, or go deep on one problem you're living with today. Diagnosis & recommendations included.
FIG · U10 · lens
U11 · GTM CONTEXT ENGINEERING
Operate and develop your GTM strategy & systems right out of a terminal window. We'll pre-build a context operating system tailored to your company & pre-build necessary tools for the AI to work with your tech stack.
FIG · U11 · tree split
U12 · TRAINING
Hire us to pass on our knowledge to your team. Whether your team is new to AI or already living in the terminal, we run a practical program tailored to where they are.
FIG · U12 · mixer
Have a custom need that isn't listed?
Let's talkTP5 · SAID BY CLIENTS
“Richard is the best GTM engineer I’ve worked with. He did a project for us at Zero, and he’s ridiculously technical and creative. He comes up with ideas you wouldn’t have thought of, then actually builds them end-to-end.”
“Richard built the foundation, then made it easy to build on. Once the data and automation layer was in place, we rolled out new AI tools to the sales team at a pace we would not have managed alone. Practical, fast, and he hands over cleanly.”
“Richard built out a chunk of our GTM automation, mostly wiring together Clay, HubSpot, and Gong. The highlights for us were his help with outbound prospecting and data enrichment, both of which save our BDRs a bunch of time every week. Richard is systematic about how he works and a great communicator, so we never had to guess where things stood or what he needed from us. 10/10 would work with him again!”
“Richard combines data engineering knowledge, deep revenue ops understanding, and the street smarts of a startup founder. He helped us build the fundamentals of AI in go-to-market: a Claude Code setup that connects all our customer knowledge into one place and turns it into action in outreach and revenue operations. We still run the system he built, and we’ve been able to expand it ourselves.”
TP6 · WORKED WITH
P1 · FOUNDER
Richard Makara
founder · gtm engineer
Richard brings hands-on experience from leading marketing departments, from founding a SaaS company that raised a €1M pre-seed and had to sell its way forward, and from software engineering in demanding in-house growth and gtm engineering roles.
BUILD LOG · a pre-AI-era gtm engineer, one of the first to build
TP7 · THE SHOP
Schematics is a boutique gtm engineering shop that brings technical excellence to gtm use cases.
Most solutions end up simple, yet customized to your company’s context. We build them with you.
schematics
FIG · TP7 · the mark
TP8 · HOW WE WORK
Start with the commercial decision
The system exists to help a real operator make or execute a consequential GTM decision.
Make judgment explicit
Definitions such as “good account,” “credible signal,” or “right person” must be turned into rules, bounds, evidence requirements, and disqualifiers that can be inspected.
Observe broadly; promote narrowly
Only sufficiently trustworthy evidence should be allowed to influence a qualification decision or consequential action.
Separate fact from interpretation
Fact → interpretation → hypothesis → decision. Humans can see where disagreement actually lives.
Treat exceptions as learning material
False positives, false negatives, missing evidence, source failures, and operator disagreements reveal how the definition should improve.
Revise the contract and rerun
When a rule is wrong, update the explicit definition, rerun the affected stage, and compare the result.
Encode validated logic
Only logic that has survived evidence and review should become a durable component.
Keep humans responsible for policy and action
AI can accelerate research, extraction, classification, and comparison. Humans remain responsible for policy, promotion of evidence, exceptions, and consequential actions.
Build the smallest useful system
Use existing tools and providers where they are good enough. Add custom code where the commercial definition, evidence, safety, or workflow requires it.