Which marketing system is the best? The one that tells you,

what to do next

I am Grigoriy Dobryakov — an engineer who doesn't do marketing by gut.

So I built a next-move engine — a pipeline that always outputs one ranked action. AI passes classify, deepen intel, and synthesize hypotheses. Concrete actions out.

See the system ↓ The lesson

Marketing runs on opinion.
Engineering runs on data.

Most personal marketing is vibes: post something, wait, hope. No feedback loop. No signal processing. No prioritization logic.

I'm an engineer. I don't know how to work like that.

So I didn't.

without a system

Post and pray
Gut-feel prioritization
No feedback loop
Unknown signal-to-noise
Can't explain decisions

with this system

Classified signals daily
Typed, executable actions
State tracked in git
Hypotheses with confidence
Every decision explainable

A proper ETL pipeline. For market intelligence.

Five layers. Every layer answers one question and hands off to the next.

Layer 1

Inputs

Raw market data, collected daily

· LinkedIn posts from targets
· Facebook conversations
· Job vacancies parsed
· Inbound messages

Layer 2

Signals

Each input classified by type and strength (model on a fixed vocabulary)

hiring-surge
leadership-change
public-complaint
budget-signal
tool-search

strength: strong / moderate / weak

Layer 3

Intelligence

Deep per-lead analysis — model-executed skills, structured outputs

· Who is this person, real agenda
· Pain behind the public signal
· Claims fact-checked
· Fit against products & cases

Layer 4

Hypotheses

Synthesized market model — model over versioned state

· Macro — structural market shifts
· Meso — company-level patterns
· Micro — individual lead hypothesis

Each has a confidence score.
Low confidence → no outreach.

Layer 5

Actions

Concrete, typed, immediately executable

AI — outreach

who · channel · exact message · KPI · stop rule

Account Intelligence, not magic: drafts from the model; human sends or edits.

PI — positioning

which narrative to fix · what content to write

Dependency graph included.
AI-9 unlocks AI-10. No guessing.

What an action item actually looks like:

AI-1 — outreach action

Who: Felix Mornebrik, Co-founder / CTO, Zalvernix
Channel: LinkedIn DM or comment
Angle: AI agent blast-radius without allowlists
CTA: One closed question on tool contracts vs guardrails
KPI: Any reply in 7 days
Stop: Silence → log in people/, no retry

PI-5 — positioning action

Type: Content positioning
Hypothesis: Agent-readiness framing emerging
Action: Write post on "agent-readable processes" as EM KPI
Why now: C-level using this language publicly
Metric: Post engagement + DM replies

This is a live system. Not a concept.

Current state of the market-state database, versioned in git.

21

buy signals
tracked

15

companies
in watch

14

hypotheses
synthesized

23

outreach
actions

11

positioning
items

git

full history,
every decision

AI didn't replace the thinking.

It replaced the legwork.

The system runs on a set of custom Claude Code skills. Each one is a specialist that handles one job and hands off to the next. Each has a SKILL.md — a spec with inputs, outputs, constraints, and quality criteria. Every invocation is a language-model run; the spec is what keeps the behavior bounded enough to trust.

Skill	What it does	Layer
vacancy-intel	Read a job posting as a market signal — who's hiring and why, pain behind the requirements	Signals
lead-intel	Deep profile of an inbound lead — pain, agenda, fact-check of claims, fit against products & cases	Intelligence
market-analyst	Synthesize changes in market-state/ into micro / meso / macro hypotheses with confidence scores	Hypotheses
action-planner	Convert hypotheses into typed, executable AI (outreach) and PI (positioning) action items	Actions
action-update	Log status updates back into the right action file — finds relevant file, classifies input, updates state	Actions
evidence-finder	Pull proof points from cases and products before writing any content — always runs before publishing	Content
fb-post-writer	Write expert content posts with correct voice, structure, and audience calibration	Content
humanizer	Strip AI patterns before anything goes public — runs last, before every publish	Content
chronographer	Archive every published post automatically — no manual logging required	Archive

Key principle:

The human decides strategy. The system executes and logs. Every output is versioned in git. Every decision is explainable.

The lesson

"If you know how to design systems,
any domain is just the schema."

The domain was unfamiliar.

I hadn't built a marketing system before. I have built data pipelines, event-driven architectures, and feedback loops.

The problem was the same.

Raw events need classification. Patterns need to surface from noise. Decisions need to be based on state, not intuition.

It took two weeks to go live.

From zero to operational. It now runs daily. Every output has a success criterion and a stop condition.

Grigoriy Dobryakov

Engineering Manager. 25+ years in the industry. Now building with AI.

Engineering Manager with a background in delivery, architecture, and team building across startups and enterprises. Led engineering at Askona (15M customers, 20+ distributed teams), UMI (50% revenue growth), Sprinthost, Personaclick, and Korus Consulting.

This project started as a side experiment: can I apply engineering discipline to my own positioning? It became a working system I run every day.

I write about this — and about AI-augmented engineering — on Facebook and LinkedIn.

LinkedIn Facebook GitHub YouTube

Active signals by type

hiring-surge

14

leadership-change

4

public-complaint

2

budget-signal

1

tool-search

1

Latest hypothesis

Macro · moderate confidence
C-level IT leaders in transition are producing AI narratives on LinkedIn — not just content, but a signal of positioning for next role or consulting market.

→ Generated 2 outreach + 3 positioning actions