About
An AI-first technology company built for the part most projects skip
Aqlyst exists because the gap between a working demonstration and a governed production system is where most technology investment quietly disappears.
Why does Aqlyst exist?
Because most technology projects fail in the same place. A proof of concept works, everyone is encouraged, and then the production system never arrives — because data ownership, error handling, permissions, deployment safety, and post-launch ownership were never decided. Aqlyst starts slower on purpose so those decisions are made first.
Who you work with
Senior-only delivery
Delivery is done by people who have carried systems into production and then run them. That standard is the reason we take on a limited number of clients at a time: capacity is the constraint we accept in order to hold it.
Behind that is more than fifteen years in enterprise systems — the last eight building AI and business applications that had to pass a security review, survive an audit, and still be running a year later. The work runs in both directions: setting application and AI strategy for an organization, and writing the solution that meets its users.
The leadership half is enterprise architecture, governance, application lifecycle management, DevOps, and security standards — plus portfolio ownership, technical roadmaps, vendor management, and building and mentoring a multidisciplinary development team. The delivery half is the data model, the permission design, the integrations, the automated process, the reporting layer, and — more recently — the agent: what it is allowed to touch, how it is evaluated, and where it hands back to a person.
Before that came a decade in infrastructure and service delivery: directory and identity services, collaboration platforms, networks, configuration management databases, service desk operations, and a cloud migration. It is the reason the questions here start with data ownership, permissions, and who gets paged — the decisions that separate a demonstration from a production system. Sectors served include healthcare and research, government programs, higher education, nonprofits, and eCommerce.
Deepest specialization: the Microsoft stack — Power Platform, Azure, Microsoft 365, Dataverse, and Copilot Studio. How we work with Microsoft.
- Based in
- Memphis, Tennessee
- Service area
- United States, remote-first, with Tennessee as the home market
- Experience
- 15+ years in enterprise technology, 8+ building AI and business applications
- Credential
- Former Microsoft MVPAwarded 2023 and 2024
- Certifications
- PMI Citizen Developer Business ArchitectProject Management Institute · Credential ID 3641857
- PMI Citizen Developer PractitionerProject Management Institute · Credential ID 3462463
We publish only credentials we hold and can evidence. No partner tier, client count, or review score appears anywhere on this site unless it is verifiable.
Where the depth is
- Enterprise application and AI strategy, technical roadmaps, and portfolio ownership
- Application architecture, from the data model through to the deployed interface
- Data modeling, permission design, and integration with line-of-business systems
- Governance, application lifecycle management, DevOps, and security standards
- AI agent design and adoption: evaluation, tool permissions, and human handoff
- Process automation, from the problem definition through to the deployed flow
- Analytics and reporting, from data preparation to agreed measures
- Cloud, identity, and workplace foundations
How we think
Three things we will not trade away
Business-aware
We start with the outcome and the people affected by it. The technology choice is a consequence of that conversation, not the opening of it.
Platform-smart
Deep specialization in Microsoft, and a willingness to say when it is the wrong answer. Neither of those is useful without the other.
Trust by design
Security, permissions, and accessibility are design inputs. Retrofitting any of the three costs more than building with them.
How we work
Verify, rank, decide, prove
The same four steps on every engagement, published so you can hold us to them.
Verify
We check the running systems and the live data ourselves before agreeing what to build, because a status report or a closed ticket is a claim rather than a measurement. One to two weeks.
Rank by reach
We count how many users, records, and configurations each problem actually touches, and that count sets the order of work rather than how large the fix looks. We try to refute our own findings first, and tell you which ones did not survive.
Decide in writing
Before building starts you get a written scope that separates what you asked for, what we chose, what we are assuming but have not proven, and what we are deliberately leaving out. Nothing starts on an assumption nobody agreed to.
Prove it moved
Work arrives in slices, and each one carries a before-and-after measurement of the thing it was meant to change, because a passing test shows only that the test ran. Two to four weeks per slice, closing with a written record of what was verified.
Fit
Who we work best with — and who we do not
The second list is the more useful one. Saying it out loud saves everyone a conversation neither side wanted to have in week three.
A good fit
- Organizations already on Microsoft 365 or Azure who are not getting the value they expected.
- Teams who have outgrown spreadsheets but are not ready for a year-long platform program.
- Stalled AI initiatives that produced a convincing demo and nothing in production.
- Leaders who need one agreed set of numbers before they can make the next decision.
- Firms whose genuine expertise is invisible to search engines and AI assistants.
Probably not a fit
- Small, well-defined chatbot builds. If a no-code tool solves your problem, we will tell you — and show you how.
- Enterprise-scale, multi-vendor RFPs. We are not built to be one supplier among nine, and the coordination overhead is where the value goes.
- Commodity SEO retainers priced by the deliverable. Our marketing work is structural, and it does not survive being bought by the blog post.
- Projects where the outcome is already fixed and only implementation hands are wanted.
- Work that requires claiming a partner tier or certification we do not hold.
Why Aqlyst
Checkable, not adjectival
Production, not pilots
Most AI projects stall between proof-of-concept and production because data ownership, error handling, deployment safety, and post-launch governance were skipped. We decide those first.
Governance from week one
Security, permissions, and accessibility are designed in rather than retrofitted, so the review at the end is a confirmation instead of a rebuild.
Microsoft depth without Microsoft bias
Deep specialization in the platform, plus an honest read on where it is the wrong fit. We will tell you when the answer is something else.
Senior-only delivery
Every engagement is staffed by people who have shipped and operated production systems. Nobody learns the fundamentals on your project, and there is no account layer between you and the people doing the work.
We check the numbers before building on them
Silent data failures do not crash anything, so nothing alerts: a dropped payload, a wrong multiplier, a conversion that never arrives. We have spent years finding that class of defect in production systems, and we look for it before an agent or a dashboard is built on top of it.
One partner across build, operate, and grow
Competitors sell one pillar. An agent needs governed data, a portal needs an identity model, and growth needs a site that performs — we connect the system end to end.
Scoped in the open
Engagement models, durations, and what is included are published rather than gated behind a discovery call. Scope is agreed in writing before work starts, and changes are quoted separately rather than absorbed and invoiced later.
Next step
Start with the problem, not the platform.
Tell us what is not working. We will tell you what we would do first, and whether we are the right people to do it.
We reply to every message, usually within one business day.
info@aqlyst.ai · (901) 232-2944