AI & Intelligent Applications
AI agents, copilots, and custom applications that survive the move from a convincing demo to daily use.
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AI-first technology company
Intelligent technology for operations, customer engagement, and growth.
Aqlyst builds AI agents, custom applications, intelligent automation, data platforms, websites, and intelligent marketing systems. We connect technology, business processes, customer experiences, and analytics to help organizations work smarter and grow faster.
Senior-only delivery. Fixed-scope engagements that finish in production. We reply to every message, usually within one business day.
One company, three outcomes
Build intelligently. Operate intelligently. Grow intelligently.
Competitors sell one of these. We connect the system end to end — an agent needs governed data, a portal needs an identity model, and growth needs a site that performs.
Build intelligently
AI agents, applications, mobile apps, websites, portals, and cloud platforms — designed to reach production, not just demo well.
Operate intelligently
Automation, integrations, workflows, analytics, and decision systems that replace manual handoffs with governed processes.
Grow intelligently
Marketing automation, SEO, AI search visibility, content intelligence, campaigns, and conversion — connected to measurement you can act on.
What we do
Eight capability areas
Most engagements draw on two or three of these. Microsoft is one specialization among them, not the shape of the company.
Intelligent Automation
Manual, disconnected processes become governed automated workflows that reduce effort and accelerate decisions.
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Intelligent Marketing
Content, customer data, automation, search, and analytics connected into one measurable growth system.
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Data & Decision Intelligence
One certified definition of each measure, and dashboards people trust enough to decide from.
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Mobile & SDK Engineering
Native and cross-platform apps, the SDKs inside them, and the production failures nobody else can reproduce.
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Cloud & Integration
Cloud architecture, APIs, and identity designed so failures are loud, retries are safe, and cost is predictable.
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Digital Experiences
Websites and portals that load fast, meet accessibility standards, and turn visits into conversations.
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Microsoft Solutions
Deep specialization across the Microsoft platform, with an honest read on where it is the wrong fit.
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Why does the reporting say one thing and the business another?
Because the measurement underneath was never checked. A payload dropped for exceeding a size limit, a value multiplied wrongly for years, a conversion never delivered to the ad platform — none of these crash anything, so nothing alerts. We find failures that do not announce themselves, then build the automation and agents that act on what is left.
You're likely here because:
- A copilot or chatbot pilot impressed everyone and then never shipped.
- Approvals and requests still run through email threads and spreadsheets.
- Two reports show two different revenue numbers and nobody can say which is right.
- Your team is re-keying the same data between systems every week.
- Your ad platform claims conversions your accounting system has never seen.
- Your app crashes for a small share of users and nobody can reproduce it.
- Buyers cannot find you in Google, and AI assistants do not mention you at all.
- You have a Microsoft 365 or Azure investment you are not getting value from.
What checking the numbers actually looks like
Three runs against three of the failures above. Each one compares what a system actually did against what it reported, because a measurement can only be checked from outside the pipeline that produced it.
$ reconcile orders --window 30d
app events read .......... 1,284,905
warehouse rows read ...... 1,284,905
reported 30-day total .... 41,912.00
verified 30-day total .... 4,191,200.00
reported / verified ...... 0.01
───────────────────────────────────────────
MISMATCH the multiplier is applied
twice, at ingest and again
at aggregation
ALERTS 0; a constant factor
produces no change to watch
SCOPE 7 of 7 configurations
RUNNING since the pipeline was built
NOTE illustrative, not a result
$ A reconciliation compares what the application actually emitted against what the report claims for the same window. A figure wrong by a constant factor still has the right type and sits in a plausible range, so nothing inside the pipeline disagrees with it. Only ground truth from outside does.
$ trace conversions --from browser --to ads
browser fired ............ 10,000
collector accepted ....... 9,981
queued to ad platform .... 7,412
platform acknowledged .... 7,406
unaccounted for .......... 2,594
───────────────────────────────────────────
LOSS 2,594 events, 25.9% of the
run, all of it at the queue
WHY payloads over the size cap
are dropped without a
receipt being written
EFFECT the long tail is removed,
not a random sample
NOTE illustrative, not a result
$ Counting the same conversion at four points shows where events are lost rather than only that they are missing. A record discarded for exceeding a size limit leaves no receipt, so a pipeline losing data looks exactly like a pipeline that is working — and the records most likely to exceed a limit are the largest sessions.
$ config explain ingest.max_payload
env a declared "4.5MB"
parsed 4
reason the parser stops at the
first non-digit character
effect a 4-byte cap; all dropped
env b declared "none"
parsed 0
reason 0 is also the value that
means no limit
effect the cap is disabled
───────────────────────────────────────────
FIX refuse to start on input you
cannot parse; log what you
actually resolved
NOTE illustrative, not a result
$ One operator-editable setting, read by a parser that returns a plausible number for an implausible input. The string 4.5MB becomes 4 and caps every payload; a value the parser cannot read becomes 0, which is also the code for no limit. Both outcomes are severe, and they are opposites.
Selected work
Systems we've built, and what changed as a result
Anonymized and representative of the work we deliver. Named client work is published only with written approval.
Research and healthcare
Enterprise Knowledge Agent
A governed Copilot Studio experience that answers policy and procedure questions from approved sources, cites where each answer came from, and hands routine requests straight into the systems that process them.
- AI Agents
- Microsoft
View case study
Operations and field services
Lifecycle Operations Platform
A connected Power Platform solution replacing spreadsheet and inbox handoffs with a single request, approval, and document lifecycle that operations leaders can see end to end.
- Intelligent Automation
- Microsoft
View case study
Professional services
Executive Intelligence Hub
A certified semantic model and executive reporting layer that ended the reconciliation debate, replacing conflicting spreadsheets with one agreed set of measures.
- Data & Analytics
- Cloud
View case study
How we work
Verify, rank, decide, prove
The same four steps on every engagement, whatever the capability. The first two run before any scope is agreed, so the work is aimed at what we measured rather than at what was reported.
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.
Why Aqlyst
Checkable, not adjectival
Every claim below is something you can verify in a first conversation. Ask us to.
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.
What does an engagement cost?
It depends on scope, and we do not publish figures. A focused starting engagement — one agent, one automated process, or one certified dashboard set — is fixed-scope and fixed-timeline. Larger platform work is quoted after a paid discovery. Ask in a first conversation and you will have a real number early.
Insights
Recent thinking
Data & Analytics
The failures that don't announce themselves
The dangerous defects in data and measurement systems are the ones that never crash. Nothing alerts, nobody files a ticket, and the wrong number is trusted for years.
Aqlyst Technologies6 min read
AI Agents
Claims are not evidence: verifying what an AI agent tells you it fixed
As teams adopt AI coding agents, the reviewer's job moves from reading code to verifying claims. A commit message, a status update, and an agent summary are all claims.
Aqlyst Technologies6 min read
AI Agents
Governing enterprise AI agents
A practical approach to grounding, permissions, evaluation, and human oversight — the four decisions that separate an agent pilot from an agent in production.
Aqlyst Technologies5 min read
Next step
Tell us what needs to change.
A short conversation is usually enough to tell whether we are the right fit. We will be direct about what we would do first, and about anything we think is the wrong place to start.
We reply to every message, usually within one business day.
info@aqlyst.ai · (901) 232-2944