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Strategic AI systems architecture & advisory

Stop burning runway on speculative AI experiments. Production architecture, feasibility audits, and stack strategy.

I'm a hands-on systems builder, not a slide-deck consultancy. I evaluate your operational bottlenecks, rule out unviable use cases before you write code, design production pipelines you host, and vet vendors — so you stay free of SaaS lock-in.

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  • Vendor-neutral: no kickbacks, no affiliate deals
  • Stack chosen for your economics, not mine
  • Engineering blueprints, not slide decks
The capital drain

Speculative AI experiments burn runway when they start with hype instead of architecture.

Generalist consultancies sell theoretical roadmaps, while teams overpay for frontier models on tasks a fine-tuned small model can solve faster and more cheaply.

The frontier model margin drain

Routing high-frequency, unglamorous B2B workflows (data parsing, compliance classification, support triage) through a flagship model quietly destroys gross margin, request by request.

The fix

A feasibility audit that costs each workflow against a domain-adapted small model and deterministic code before you commit engineering time to any of them.

The multi-tenant cloud lock-in

Off-the-shelf "enterprise AI" platforms store your workflows and customer data inside a third-party silo with per-seat licensing you don't control.

The fix

Architecture blueprints for dedicated virtual infrastructure, self-hosted n8n clusters, and private vector stores that you own completely.

The PoC graveyard

Internal teams spend quarters building brittle prototypes with unmonitored scripts and ad-hoc wrappers that fail under production concurrency and edge-case testing.

The fix

Production-ready architecture specifications — deterministic guardrails, structured schemas, fallback logic — written before a line of production code.

Advisory engagements

Technical advisory tracks designed for immediate operational impact.

Every engagement delivers concrete architectural diagrams, schema contracts, and implementation blueprints.

Feasibility, unit economics & small-model distillation

Feasibility audit

For Founders, CTOs, and product leaders planning new automated product lines or internal AI tooling.

What's included (4)
  • Algorithmic triage: evaluating whether a problem requires a frontier model, a domain-adapted small model, or a simple deterministic SQL/Python script.
  • Unit economic modeling: projecting compute cost and latency for a frontier model versus a self-hosted small model at your real scale.
  • Data readiness audit: evaluating whether your internal documents and schemas support high-accuracy RAG or fine-tuning.
  • A definitive "build vs. buy vs. discard" technical roadmap with a delivery date agreed up front.
  • Unit Economics
  • Small-Model Fine-Tuning
  • Model Distillation
  • Token Projections

Production architecture & infrastructure

Systems design

For Engineering teams building high-throughput agent workflows that cannot afford downtime or hallucinations.

What's included (4)
  • Pipeline topologies: resilient n8n orchestration, vector stores (pgvector/Qdrant), and fallback routing.
  • Private deployment strategy: self-hosted architectures on dedicated VPS/Linux containers (Proxmox/Docker) built for GDPR and data-privacy readiness — you remain the compliance owner.
  • Deterministic guardrail design: schema validation (Zod), output parsers, and automated circuit breakers.
  • Observability architecture: trace logging (Langfuse), latency alerts, and dead-letter failure queues.
  • n8n Cluster Architecture
  • Docker / LXC
  • pgvector
  • Langfuse
  • Zod Schemas

Legacy modernization & automation

Workflow strategy

For Established mid-market companies running traditional ERPs, legacy CRMs, or on-premise relational databases.

What's included (4)
  • Interfacing autonomous AI agents with legacy databases and software lacking native AI capabilities.
  • Custom REST webhook and middleware bridging without touching critical legacy core systems.
  • Operational friction mapping: identifying repetitive, manual data entry tasks that can be fully automated.
  • Phased rollout and change management plan to ensure internal operational adoption without workflow disruption.
  • REST Middleware
  • Legacy Database Sync
  • Webhook Translation
  • Air-Gapped Workflows

Fractional AI systems architect

Ongoing oversight

For Growing organizations that require senior architectural direction and technical oversight without hiring a full-time executive.

What's included (4)
  • Weekly technical reviews with engineering leads to evaluate pull requests, unblock design decisions, and review pipeline logs.
  • Independent technical vetting of third-party software vendors and agency proposals to protect against bloated quotes and fragile wrappers.
  • Continuous model benchmarking: evaluating newly released open-weight and frontier models against your pipeline requirements.
  • Direct asynchronous technical sounding board via Slack/Teams.
  • Technical Due Diligence
  • Architecture Oversight
  • PR Code Review
  • Vendor Vetting
Audit excerpt

One page from a feasibility audit.

Every workflow in scope is costed and routed before anyone writes production code. This page covers an invoice pipeline handling forty thousand documents a month.

Every task is routed on purpose
Code where the rules are fixed, a small model where the pattern is narrow, a frontier model only where judgement is needed.
Costs use your volume, not a demo
The audit runs on your real monthly volumes and current provider prices, and shows the maths so your team can re-run it.
Each line ends in a decision
Build, buy or discard, with the reason written down, including the ideas that should not be built at all.
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audit / unit-economics / invoice pipeline [SAMPLE DATA]
Task Per month Route Decision
Extract totals and VAT from structured PDFs 24,400 / month Deterministic parser Build
Classify line items to cost centres 14,800 / month Small model, fine-tuned and self-hosted Build
Resolve disputed or unreadable invoices 800 / month Frontier model, a person approves Build
Chat assistant for supplier questions — / month None, no proven demand Discard

Share of volume sent to a frontier model

2%

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Engagement timeline

From initial diagnostic to validated architectural blueprint in 3 weeks.

  1. Step 1
    Days 1–3

    Operational diagnostic & systems review

    Interview key operational stakeholders, audit your existing tech stack, and map data flows to identify your core friction points.

  2. Step 2
    Week 1

    Feasibility & unit economic modeling

    Simulate API loads, model token consumption, and separate high-yield automation targets from technically unviable ideas.

  3. Step 3
    Week 2

    System blueprinting & infrastructure specs

    Produce end-to-end architectural schematics, database schemas, fallback logic flows, and vendor evaluations.

  4. Step 4
    Week 3+

    Executive roadmap & engineering handoff

    Deliver the implementation roadmap, run a technical walkthrough with your engineering team, and define production milestones.

FAQ

Frequently asked questions.

Anything not covered here gets answered on the call.

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Do you write code during a consulting engagement, or do you only advise?

Advisory engagements focus primarily on architecture, feasibility analysis, vendor vetting, and system design. Deliverables include concrete technical assets: system schematics, API payload schemas, validation rules, and proof-of-concept starter nodes — not theoretical slides. If full end-to-end execution is what you need, I can transition into an engineering build sprint.

Our company operates in a strictly regulated industry. Can we keep our data private?

Yes, that's the default I design for. My architectural advisory favours zero-data-retention, self-hosted, private pipelines — typically deployed on a dedicated virtual private server or your own infrastructure — with any third-party model API used only under your own key and data agreement.

Can you help evaluate proposals from other AI vendors or agencies?

Yes. Technical due diligence on third-party vendor bids is one of the more common requests I get — checking whether the proposed architecture is resilient, realistically priced, and free of unnecessary lock-in.

How quickly can we get started?

I begin with a 20-minute discovery audit to align on scope. Once engaged and any NDA is signed, I typically start the Phase 1 diagnostic within 48 to 72 hours.

Free 20-minute audit

Ready to turn speculative AI ideas into solid production systems?

I'll ask about your operational bottlenecks, sanity-check technical feasibility, and tell you which advisory track fits.

Alex Shilov

The 20-minute audit is free, with no obligation, and it's with me, not a sales team. I read every request myself and reply within one business day.

Alex Shilov · AI Systems Builder and Auditor

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