Fractional AI Architecture

Architecture advisory for production AI systems.

Principal-level judgment for engineering teams shipping LLM and agentic systems.

I review your architecture and document the decisions your team needs to make.

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Author of Production-Ready AI Agents Founder of ActiveWizards

Pattern Transfer Through Reviews

Production patterns move through architecture reviews, PR feedback, and decision records.

Operability Before Launch

Review schemas, traces, evals, fallback paths and cost assumptions before launch.

Decision Records, Not Vibes

Architecture choices, trade-offs, owners, and reversal costs, written down.

When This Is the Right Fit

You need to choose between an AI agent, a simpler workflow, or a vendor.
The prototype works, but production requirements are unclear.
Quality, reliability, cost or data access is putting delivery at risk.
Human approval, workflow handoffs or system ownership is unclear.
A build, hire or funding decision needs a clear technical rationale.

For founders, engineering leaders and enterprise AI teams with a decision to make.

Who Owns the Review?

I'm Igor Bobriakov, founder of ActiveWizards, a 10-year AI engineering studio. My advisory work comes from systems my team builds and maintains.

I lead advisory engagements; ActiveWizards provides engineering teams when implementation is needed.

I wrote Production-Ready AI Agents. Its Three Pillars — Observability, Reliability, Security — are the constraints I use when reviewing AI architectures.

As Architect

I review schemas, state, evals, and code paths for silent failures: errors that pass tests but corrupt behavior, cost, or trust.

As Founder

I connect architecture choices to staffing, roadmap pressure, vendor risk, and leadership funding decisions.

Read my approach to agent control flow and validation boundaries.

Engagements

A Focused Review or Ongoing Advice

Resolve one consequential decision, or review recurring trade-offs with your team.

Prices in USD. Scope and start date agreed before work. Implementation is separate.

One decision

Production AI Readiness Review

Before shipping, scaling or funding the next change.

$7,500 fixed

Result: A decision brief and ranked findings your team can act on.

One AI workflow, with scope and evidence agreed upfront
Architecture risks, evidence gaps and prioritized actions
Verification checks, a findings readout and one clarification session

Written findings in five business days after agreed inputs and start date. No follow-on purchase required.

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Recurring decisions

Technical Advisory

Your team is building. Principal review is missing.

$6,000 / month

Result: Decisions reviewed and documented while your lead owns delivery.

Four architecture sessions per month
Prioritized async code and architecture review
Written recommendations, trade-offs and decision records

16 hours per month across sessions, preparation and reviews. No on-call or unlimited support.

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Need broader AI architecture leadership? Discuss your needs.

What a review finding contains

Illustrative format, not a client case or a promised finding.

Decision: What the team needs to decide and the system boundary.
Evidence: Code paths, traces, evaluations or documents inspected.
Finding: The observed issue, consequence and confidence, separate from assumptions.
Unknowns: Missing evidence and what could change the recommendation.
Action: A ranked next step, alternatives and the suggested owner.
Verification: A check the team can run to evaluate the change.

When Advisory Needs Builders

The Squad

2-5 AI engineers in Python, LangGraph, vector databases, and cloud infrastructure, deployed through ActiveWizards.

The Model

Build work is scoped separately with ActiveWizards. Architecture responsibility, delivery ownership and review cadence are agreed for that engagement.

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Frequently Asked Questions

When should we bring you in?

When AI work is moving from prototype to production, when architecture choices feel hard to reverse, or when leadership needs a written technical rationale before funding more work.

What do you actually review?

Architecture, state management, retrieval design, evals, schemas, API and MCP contracts, provider choices, observability, latency, cost, and the trade-offs your team has to operate.

How do you work with our team?

Your technical lead owns delivery. I review architecture, code and trade-offs, then document recommendations. We agree priorities within the engagement's scope. Implementation, ongoing operations and incident response are separate.

What happens after we submit?

I review your submission for fit. If there is a match, we discuss the decision, available evidence and scope before agreeing a fee and start date. This form does not book paid work. A readiness review is not required before every advisory engagement.