Build intelligently
We build AI agents, copilots, and custom applications that work from your trusted content, act inside your real systems, and survive the move from demo to daily use.
An AI agent is a governed assistant connected to your approved content and business systems. It answers questions with citations, and it can take real actions — creating a request, updating a record, routing an approval — inside the permissions each user already has. It is not a chatbot bolted onto a website.
Problems we solve
What we build
Each of these is a definition rather than a label — if a term below is not what you thought it meant, that is worth a conversation before a proposal.
How we work
The same four steps on every engagement. Nothing is scoped until the current state has been checked against the running system rather than taken from a report.
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.
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.
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.
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.
Proof
Research and healthcare
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.
View case study
Product names are used descriptively. We select against the problem, not against a reseller agreement.
One production-ready outcome with grounding, guardrails, an evaluation set, a way to verify what the agent actually did, and a named owner at handover. Platform work is quoted after a paid discovery. If you already have a pilot that never shipped, the Stalled Pilot Rescue starts from what you built rather than from a blank page.
Questions
A narrow agent grounded in a defined content set, with real permissions and an evaluation harness, takes four to six weeks. What extends that is almost never the build. It is deciding which content is authoritative, who owns it, and what the assistant must refuse to answer — which is why we treat those as the first step rather than a detail.
Three things together. The assistant answers only from a defined, curated content set rather than from general knowledge. Every answer carries a citation the reader can open, so a wrong answer is visible rather than plausible. And an evaluation set of real questions with known-good answers runs against every change, so a regression is caught before a user meets it.
Yes, and this is not optional. An agent built correctly runs inside the identity of the person asking, so it can only surface what that person could already open themselves. An agent that runs under a single privileged service account is the most common way a knowledge assistant becomes a data-leak incident, and we will not build one that way.
It can take actions — creating a request, updating a record, routing an approval — and this is usually where the value is. Answer-only assistants save a search; assistants that act remove a step. What an agent may touch is decided before it runs, and the limits are proven rather than assumed. Actions are scoped, logged, and confirmed by the user where the consequence warrants it. Dry runs get tested too: a call meant to change nothing can succeed and create real data, and that should fail in a test rather than in production.
Someone in your organization, named before we start. An agent is not a finished artifact: the content it is grounded in changes, the questions people ask shift, and the underlying models are updated. We hand over the evaluation set, the documentation, and the deployment pipeline, and we can stay on a support arrangement, but the ownership sits with you.
By checking something the agent does not author. A summary, a status message, and a commit note are all claims, and claims are not evidence. We compare the stored state before and after, which settles whether something changed at all rather than inviting an argument about a diff. Reviewing agent output, we have found batches of completed-work claims where most were simply untrue.
As unfamiliar code from a confident author. The failure mode has changed: the code usually compiles and the tests usually pass, so review shifts from reading style to verifying claims. Worth knowing that everyone now runs similar assistants, so two reviewers often produce the same finding and miss the same gap. We plan for duplicate coverage rather than assuming breadth.
That depends on the architecture, and it is a decision to make deliberately rather than discover. We document exactly which content leaves your tenant, which provider receives it, and what their retention terms are. Running inside your existing Microsoft 365 or Azure tenant is usually the right answer, because identity, permissions, compliance boundaries, and audit are then the ones you already operate rather than a second set to govern. If your constraints require that nothing leaves at all, that is a design input, not a blocker.
Next step
Describe the problem rather than the solution. We will tell you what we would do first, how long it takes, and what it costs.
We reply to every message, usually within one business day.
info@aqlyst.ai · (901) 232-2944
