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MIT & Google Certified Strategy

Build measurable AI operating lift in 90 days or less.

We translate enterprise AI discipline into a phased execution model for owner-led and growth-stage teams. Every engagement is scoped around operational outcomes, security controls, and a delivery rhythm your business can sustain.

Executive proof signals

  • Average implementation cycle: 14 days to first deployment milestone
  • Advisory + execution model aligned to security and governance controls
  • Operating playbooks designed for internal adoption, not vendor lock-in

Operational Proof Ledger

SYS.UPDT: JUL-2026

Automation lift

+35%

Data entry & support workflows

Security posture

Pass

Control & configuration review

Typical delivery

14d

Strategy to implementation

Engagement model

A+B

Executive advisory with technical execution

Capability Architecture

A curated operating model that blends advisory depth with implementation speed.

Level 1 · Strategic Core

Strategic AI Security and Execution

We establish the operating thesis, identify high-value workflow candidates, and sequence implementation around control requirements and adoption risk.

Primary outcome

Prioritized ROI roadmap

Governance baseline

Security-first rollout controls

Level 2 · Build Track

Custom AI Automation

Deploy workflow automation, internal copilots, and applied integration patterns mapped to your existing systems and team capacity.

Level 3 · Operating Advisory

Executive Technical Advisory

Guide tool decisions, vendor selection, capability budgeting, and phased implementation governance for sustainable scale.

Why Allen AI

Enterprise operating discipline translated for growth-stage businesses with practical timelines and measurable outcomes.

Leadership pedigree

25+ years operating at enterprise scale

Experience leading technology programs across high-accountability organizations including Raytheon, Verizon, AT&T, and Hyundai.

Proof of impact

Focusing on high-value potential efficiency outcomes

Engagements are scoped around targeting and unlocking operational value first, then translated into delivery milestones and adoption plans.

Security and strategy

Certified advisory with implementation accountability

MIT and Google-aligned strategy foundations combined with practical controls for security, governance, and scalable rollout.

How I Approach These Problems

Illustrative scenarios based on real methodology - not client work (yet). Here's exactly how I'd tackle four common situations.

Illustrative customer support automation visual

Illustrative scenario

Scenario: A logistics operation drowning in repetitive support tickets

A mid-size operations team is spending 15+ hours a week answering the same 20 questions - order status, scheduling changes, and basic troubleshooting. Here's the build sequence I'd run:

  1. Audit the last 90 days of tickets to identify the highest-volume, lowest-complexity categories.
  2. Deploy a scoped chatbot trained only on those categories, with a hard handoff to a human for anything else.
  3. Instrument from day one so ticket deflection is measurable, not assumed.

What this typically targets:

Meaningful reduction in first-response time and support labor hours on repetitive tickets. The exact number depends on ticket mix, which is why step one is a measurement step, not a guess.

Illustrative voice receptionist automation visual

Illustrative scenario

Scenario: A small medical billing office missing after-hours calls

A lean office team is losing inbound opportunities because calls roll to voicemail after hours. Here's the build sequence I'd run:

  1. Review call logs to map peak miss windows, call intent, and existing booking workflow constraints.
  2. Deploy a scoped voice receptionist for appointment requests, billing FAQs, and routing, with immediate escalation for complex cases.
  3. Integrate with scheduling and CRM systems, then track call capture, booking completion, and handoff quality.

What this typically targets:

Higher answered-call coverage after hours and faster appointment intake without adding overnight staffing overhead.

Illustrative AI security audit visual

Illustrative scenario

Scenario: A growing SaaS company that has never had a third-party review

The product is shipping quickly, but security controls have grown organically. Here's the build sequence I'd run:

  1. Inventory systems, integrations, and AI-assisted workflows to establish scope and threat surfaces.
  2. Review authentication flows, secrets handling, access controls, and deployment configuration for practical vulnerabilities.
  3. Deliver a severity-ranked remediation plan with implementation guidance and retest criteria.

What this typically targets:

Lower exploitable risk exposure, clearer ownership of control gaps, and a prioritized path to stronger security posture.

Illustrative private LLM hosting visual

Illustrative scenario

Scenario: A firm that can't send client data to a public API

The team wants AI-enabled search and drafting, but policy and contract obligations require tighter data boundaries. Here's the build sequence I'd run:

  1. Map data classification requirements, retention policies, and approved hosting boundaries.
  2. Stand up a private model and retrieval architecture with network isolation, role-based access, and audit logging.
  3. Run structured evaluations for response quality, latency, and failure cases before broad rollout.

What this typically targets:

Safe adoption of AI workflows for sensitive documents while keeping control over data handling and compliance boundaries.

Founding Client Program

Transparent early-stage offer for teams that want direct founder involvement.

A note from Brad

Brad Allen portrait
"I built Allen AI Solutions on 25+ years of enterprise operating experience - most recently at Hyundai Capital America - because I kept seeing the same gap: companies either overpay for generic AI consulting, or underbuild it themselves and create new risk. I'm taking on a small number of founding clients right now, and I'm upfront that you'd be among my first engagements under this name. What you get in exchange is my full attention, founder-level pricing, and a methodology built on real enterprise practice - not a junior team learning on your dime."

Founding Client Program - 3 spots this quarter

I'm opening a limited number of founding-client engagements at founding-client pricing. In exchange, I ask for an honest case study and testimonial once we've delivered results - the kind of proof I can't publish yet because it doesn't exist. If you want to work with someone personally invested in getting your first engagement right, this is the moment to do it.

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Routes to the same consultation form.

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