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Volodymyr Pavlyshyn

Work with me

Four ways in. All of them are the same problems the books are about, applied to your system instead of a page.

pavlyshyn@gmail.com

Agentic memory architecture

Design partner, ongoing

Designing the memory an agent actually needs: episodic, semantic and procedural layers, the knowledge-graph substrate underneath them, and the extraction-consolidation pipeline that keeps the whole thing from rotting. Plus retrieval that does more than nearest-neighbour — graph traversal, PageRank and community detection fused into one query.

You might want this if

  • Your agent forgets things it was told last week.
  • Retrieval returns plausible chunks and the agent still cannot answer multi-hop questions.
  • You have a vector store and have started to suspect that is not a memory.

Agent identity & trust

Project or advisory

Identity for agents, and trust between them. DID methods — did:web, did:webvh, did:webs — KERI, verifiable credentials, GLEIF vLEI, agent reputation and trust registry design. Built from founding-engineer work on a production SSI platform, including a COVID verifiable-credentials system taken from zero to a live airport integration in four weeks.

You might want this if

  • Your agents need to prove who they are to something that is not your own backend.
  • You are designing a trust registry and want to know what breaks at scale.
  • Someone said "just use OAuth" and you suspect that answer is incomplete.

Architecture review

Time-boxed, written findings

A senior read of an existing system, ending in a written document you can hand to your board or your team — what is sound, what will hurt, what to do first. Frequently the cheapest way to find out whether you need anything else on this page.

You might want this if

  • A system works but nobody can say why, or what happens next.
  • You are about to commit to a data architecture you cannot easily undo.
  • You need an outside opinion your team will actually respect.

Workshops & training

Multi-day, on-site or remote

Teaching engineering teams the material behind the books: agent architecture, knowledge graphs as agent memory, and local-first and edge AI. The same programme shape currently being built inside an engineering organisation rather than assembled for a conference slot.

You might want this if

  • Your team is shipping agents by pattern-matching on blog posts.
  • You want a shared vocabulary before a big architectural decision, not after.
  • You would rather your engineers learned this from someone who has shipped it.

How it works

  1. A short call to work out what you actually need — often not what the enquiry said.
  2. A written proposal with scope, shape and price.
  3. The work, with whatever cadence of contact suits you.

No price list. The right number depends on scope, duration and what the work is worth to you, and I would rather find that out on a call than guess in public.

Where this comes from

Founding engineer at Affinidi — self-sovereign identity, DID and Sidetree protocol work, and a COVID verifiable-credentials platform taken from zero to a live airport integration in four weeks.

Founding engineer at Mykin.ai — a privacy-first personal AI, with on-device semantic memory: a personal knowledge graph with vector search and clustering, running on a phone.

Before that: Zalando, TomTom, LiveIntent. Teams of five to fifteen, systems serving forty million customers, and three million events a minute.

The full CV → Tools I have shipped →

Start with a conversation

Tell me what you are building and what is going wrong. If I am not the right person, I will say so and try to point you at who is.

pavlyshyn@gmail.com LinkedIn