AI Buyer-Agent Optimization · Agentic Buying

Win the agentthat buys for your customer.

AI buyer-agent optimization for B2B SaaS — get shortlisted, compared and chosen when your buyer delegates research and purchasing to an AI agent. Get ready before it scales.

Book a readiness audit →What I do

Forward-looking. Plant the flag before 2027.

Agent readinessBeta
DiscoverableBuilt agent finds you
ComparableBuilt clear pricing
VerifiableBuilt machine proof
SelectableBuilt chosen by agent
Experience across
Netpeak GroupAcademyOceanDataImpulseGoogle for StartupsSaaSFintech
01 / The shift

Soon, the buyer researching you will be a machine.

B2B buyers are starting to delegate research, shortlisting and comparison to AI agents - and analysts expect agentic buying to become material by 2027-2028. When the evaluator is an agent, the rules change: it rewards machine-readable proof, clear pricing and clean data, not a slick landing page.

01

Agents read differently than humans

An AI agent parses structured facts, pricing, docs and proof - not your hero animation or brand video.

02

Unclear pricing and packaging lose

Agents comparing vendors drop the ones whose pricing, terms and capabilities are not clearly stated and machine-readable.

03

No agent-readable proof

If your security, integration and outcome proof is not extractable, the agent cannot verify you and moves on.

The point of view

Optimize to be chosen, not just found.

When AI agents do the buying research, winning means being discoverable, comparable, verifiable and selectable to a machine: clean structured data, transparent pricing and packaging, extractable proof, and strong presence across the engines agents use. This is the next layer beyond AI search optimization - and it is early, which is exactly why you plant the flag now.

02 / What I do

AI buyer-agent optimization, end to end.

Make your SaaS the one an AI agent shortlists and picks.

Discoverability● Built

Agent discoverability

Be present and parseable in the AI engines and sources buyer agents pull from. Builds on AI search.

Pricing● Built

Clear, machine-readable pricing

Pricing and packaging stated so an agent can compare you accurately, not guess.

Proof● Built

Extractable proof

Security, integration and outcome proof structured so agents can verify it.

Data● Built

Structured product data

Capabilities, integrations and specs in clean, machine-readable form.

Comparison● Built

Comparison positioning

Frame your category and differentiators so agents place you favorably.

Monitoring● Beta

Agent-selection monitoring

Track how agents shortlist and describe you as agentic buying emerges.

03 / Process

How a readiness engagement runs.

1

Assess readiness

Audit how an AI agent would currently find, parse, compare and verify you.

2

Fix the gaps

Clean up structured data, pricing clarity and extractable proof.

3

Strengthen presence

Build the AI-search and source signals agents rely on to shortlist.

4

Monitor & adapt

Track agent behavior and adjust as agentic buying matures.

When the buyer is a machine, the best data wins.
// Andrii Byzov — AI-native fractional CMO
4×REVENUE GROWTH YoY (operator track record)
5×LOWER CAC DELIVERED
0→14TEAM BUILT & LED
Andrii Byzov, AI-native fractional CMO for AI buyer-agent optimizationAndrii Byzov  /  Agent optimization
Who you work with

Early on the next search shift.

I am Andrii Byzov — an AI-native marketing operator who has led growth across B2B SaaS and fintech, including CMO roles at DataImpulse and AcademyOcean (Netpeak Group), and worked with Google for Startups.

This is a forward bet - agentic buying is emerging, not mainstream yet, which is why owning it early matters. It extends my AI search optimization work; read my thinking on my blog.

15+ yrsFINTECH & SAAS
0→14TEAM BUILT & LED
AI-nativeFROM DAY ONE
04 / Fit

Is agent-readiness worth it yet?

A strong fit if

  • +You are a B2B SaaS that wants to be early on agentic buying.
  • +Your pricing, data and proof are not machine-readable today.
  • +You already invest in AI search and want the next layer.
  • +You take a forward, own-it-early view of new channels.

Not a fit if

  • −You need pipeline this quarter and nothing forward-looking.
  • −Your buyers are nowhere near using AI agents to buy.
  • −You have no AI-search foundation to build on yet.
  • −You want guaranteed near-term ROI from an emerging channel.
05 / FAQ

AI buyer-agent optimization, answered honestly.

What is AI buyer-agent optimization? +
It is getting your B2B SaaS ready to be discovered, compared, verified and chosen by AI agents that research and shortlist vendors on a buyer's behalf - through clean structured data, clear pricing, extractable proof and strong AI-search presence.
Is agentic buying real yet? +
It is emerging, not mainstream. Analysts expect it to become material around 2027-2028. This is a forward, own-it-early play - small impact today, potentially large later.
How is this different from AI search optimization? +
AI search optimization targets AI answers to humans. Buyer-agent optimization targets being selected by an autonomous agent doing the research and comparison - the next layer.
What do you actually change? +
Mostly the machine-readable side: structured product and pricing data, extractable security and outcome proof, comparison positioning, and presence in the sources agents pull from.
Should we do this now or wait? +
Do the foundational work now - clean data, clear pricing, strong AI-search presence - because it pays off in human AI search today and positions you for agents tomorrow. Treat the agent-specific layer as an early bet, not a near-term revenue line.
Start here

Plant your flag in agentic buying.

A readiness audit shows how an AI agent would find, compare and verify you today - and the foundational fixes that pay off now and position you for what is coming.

Book a readiness audit →

Operator-led · AI-native · B2B SaaS