How is this different from the Axionic Readiness Review?

The Readiness Review assesses your applications, architecture and delivery. This review looks at one thing: how AI is being deployed and governed across your organization. Many clients do both.

What access do you need?

Read-only admin access to your AI tools and their billing, your identity provider's application list, and interviews with five to eight people. We do not need production data.

Do you recommend specific vendors?

We recommend patterns first, then products that fit them. Axionic does not resell any model vendor or take referral fees from them, so the advice is not tied to one.

Will you try to sell us Axionic Agents?

Only if you have unattended, cross-system or regulated workflows where a governed agent platform is the right pattern. Most findings point to simpler and cheaper fixes.

Fixed fee, fixed scope, quoted after a 30-minute scoping call. The fee depends on the number of business units and tools in scope.

You own the blueprint. Some clients execute it themselves. Others retain us for governance setup, a fractional AI architect, or a governed platform pilot.

AI Deployment Readiness Review

Your people are already deploying AI.
Find out if your business is ready for it.

Desktop assistants, agents inside your SaaS tools, custom builds, and things IT never approved are all running at once. In three weeks, a senior architect shows you what is running, where each workload actually belongs, which controls are missing, and what it should cost. Fixed scope. Vendor neutral.

Book a scoping call Read the research behind it: Deploying AI at Scale

30+ years of enterprise architecture · Vendor neutral: we do not resell any model or platform · Blueprint you can act on, not a slide deck

The Problem

Running AI Without a Plan

Five patterns, zero plan

Most organizations run desktop assistants, embedded SaaS agents, custom agents and self-installed tools side by side. Almost none chose that mix on purpose.

Governance is behind adoption

In Deloitte's 2026 survey, 74% of leaders expect agents in moderate use by 2027. Only 21% report mature governance for them. The gap is where incidents and audit findings come from.

The bill is moving faster than the budget

73% of organizations exceeded their AI cost projections this year, according to the FinOps Foundation. Seat licenses are the small part. Usage is the part nobody owns.

What You Get

Four Deliverables You Can Act On

1. AI inventory

Every assistant, agent, connector, plugin and recurring task we can find, sanctioned or not, each with an owner and a purpose. You can act on this by: shutting down what should not exist and assigning owners to what should.

2. Workload placement

Each use case placed against the five deployment patterns: desktop assistant, published plugin, embedded SaaS agent, governed agent platform, or custom build. You can act on this by: moving work to the cheapest pattern that is safe for it.

3. Control gap assessment

Your current state scored against nine controls, from identity and connector permissions to audit trail and kill switch, with a prioritized remediation list. You can act on this by: fixing the three gaps that carry the most risk first.

4. Budget model

A workflow-level cost model for the next 12 months: seats, usage by user tier, unattended workflows, governance tooling and contingency. You can act on this by: setting caps by group and defending the number to your CFO.

How It Works

Three Weeks, Fixed Scope

Week 1

Discover

Stakeholder interviews, admin console and billing review, connector and endpoint discovery. You give us read access and about six hours of your team's time.

Week 2

Analyze

We place each workload, score the nine controls, and build the cost model on your real usage data.

Week 3

Blueprint

A working session with your leadership team, then the written blueprint: findings, a 90-day action plan, and a 12-month target state.

The Frameworks Behind It

Built on Open, Published Research

Five deployment patterns

Managed desktop assistants. Self-hosted open agent runtimes. Agents embedded in SaaS. Custom agents on model APIs. Governed agent platforms. Each fits a different class of work.

See the five patterns

Nine controls

Inventory. Identity. Least privilege on tools. Enforcement outside the model. Human approval gates. Audit trail. Supply chain review. Kill switch and cost ceiling. Permission-aware retrieval.

See the nine controls

Prefer to self-serve first? Take the nine-control self-assessment, download the AI budget model, or start smaller with an architect-led AI prototype.

Fit

Who It Is For, and Who It Is Not

A good fit if you:

Have 100 to 5,000 employees and AI tools already in use
Are about to standardize on an assistant or sign an enterprise AI agreement
Operate in a regulated or audit-heavy sector
Have a board, investor or customer asking how AI is governed
Have seen an AI invoice you could not explain

Not the right fit if you:

Have not started using AI at all (start with a prototype instead)
Want a vendor selected for you without examining your workloads
Need a penetration test (we partner for that; this is an architecture review)

Proof

Proven in Production

National Tax Firm: AI Document Processing

Architect-designed OCR + LLM system processing hundreds of tax notices daily at 90–95% accuracy. Not a vendor product, but a custom-architected system designed for their specific document types, compliance requirements, and workflow. Expansion underway.

Led by Drew Rutter, founder of Axionic. 30+ years of enterprise architecture across IHG, Ticketmaster, TAG Heuer and Walt Disney World Resorts. MIT AI/ML credentials. About Axionic

FAQ

Frequently Asked Questions

Book a 30-minute scoping call

No pitch. We will ask what you are running today, tell you whether the review is worth doing, and quote a fixed fee if it is.

Your people are already deploying AI.Find out if your business is ready for it.