The platform is not the hard part. Where your data already lives, what your security team has already cleared, and who maintains the thing in eighteen months: that is what decides whether an agent survives contact with real work. We start there, recommend the platform that fits, and build the agents and the applications around them.
The agents we build know your documents, query your business systems, work with your people, and respect the security boundaries you have already drawn. They run inside your own cloud tenant, on the platform that matches where your data sits. No second estate to manage, and nothing leaving your control.
HR fielding the same benefits questions every Monday. IT answering “how do I reset my password” for the 400th time this year. Finance walking people through expense policy. Your domain experts spend their day answering questions someone else could answer if the knowledge were accessible. A grounded agent ends that cycle.
Your website is your busiest channel. A customer-facing agent handles first-touch qualification, surfaces the right product or service, books a meeting on a real calendar, and escalates to a human when judgment is needed, around the clock, in multiple languages, without growing your support roster proportionally.
You have data in an ERP, a CRM, a service desk, or a line-of-business application nobody wants to touch. The work people do today is “read from system A, update system B, tell someone.” That is an agent, and often an application around it. We build it, integrate it, and hand it over documented.
Every platform demo looks the same. The difference that actually shows up in the budget is how far your data has to travel to reach the model. An agent that sits next to your documents is a project. One that has to pull them across a cloud boundary first is a project plus a recurring bill plus a security review. We do not resell any of these.
SharePoint, Teams and Outlook hold the knowledge, and Entra already governs who sees what. The agent belongs in the Microsoft estate, where those permissions are inherited rather than rebuilt. Strongest option if you are also headed for FedRAMP or a defense impact level.
The hard part is the query, not the conversation. When the truth sits in a warehouse, the agent should reason next to it instead of shipping extracts back and forth. Google Cloud and Databricks-shaped environments land here.
If your applications, your storage and your controls are all in one place, the deciding factor is operating agents at production scale under governance you already passed. The ability to put several model vendors behind one procurement is real leverage.
Different economics entirely. Here portability and margin matter more than internal convenience, because consumption that is invisible internally becomes your cost of goods sold. Build for the model layer to stay swappable.
The most common situation and the least discussed. Before any platform question is worth asking, something has to decide what the system of record is. Usually the first phase is not an agent at all.
Healthcare, defense, and anything with an auditor. The binding constraint is a signed agreement and a defensible record of what the model saw. That eliminates most of the field before the first demo, which makes the choice easier, not harder.
Every engagement starts with a paid discovery, because we both need to know what is being built before anyone commits. Development is quoted against an approved scope. Once it is live, an optional operations retainer keeps it tuned and keeps the running cost from drifting.
One to two weeks. Workshop with your stakeholders to map use cases against business value, find where the data actually lives, confirm what your security team has already cleared, and recommend the platform that fits. Output is a written architecture recommendation and a sized proposal.
Two to twelve weeks depending on scope. We design the behaviour and the conversation, connect the knowledge sources, wire up actions and integrations, build any custom connectors, and build the application around it where a chat window is the wrong interface. Tested against the use cases agreed in discovery.
Publish to the channels your people already use. Configure data-loss policies, environment governance, and consumption controls so the bill holds no surprises. Train your team on day-to-day operations and your power users on building their own. Document everything, including what we would do differently.
Ongoing and optional. We administer the environment, watch performance and consumption, tune answers and actions against real usage rather than assumptions, report monthly, and tell you when a new agent is worth building and when it is not.
A conversation is a good interface for a question and a poor one for a job. Approving forty invoices, comparing two quotes side by side, walking a technician through a procedure with their hands full: none of that wants to be typed. We build the application, and put the model behind it where it belongs.
Most organizations run something critical on a spreadsheet, an access database, or software whose vendor stopped answering the phone. We rebuild it as a real application, with AI where AI earns its place and plain software everywhere else.
Where your customers do business with you. Quoting, onboarding, status, document collection. Agents handle the ambiguous parts and hand off cleanly to a screen when the task needs structure and a record.
Mobile and tablet applications for people who are not at a desk. Offline tolerance, large targets, voice where it helps. The single fastest way to capture the knowledge that walks out the door when an operator retires.
The agent is the easy part; the contracts between systems are the project. Connectors, transformation, error handling, and the retry logic that decides whether this thing is trustworthy at 2am. We quote for it because it is where the time goes.
Whichever one fits, and that is decided in discovery rather than beforehand. In practice it follows your data: documents in Microsoft 365 point one way, a warehouse points another, an AWS-native estate points a third. We have no reseller relationship with any vendor, so the recommendation costs us nothing either way.
Often, and that is a good outcome. If your people are already licensed for an agent capability inside software you own, the cheapest first project is usually the one that uses it. The mistake is assuming the bundled version reaches far enough. Discovery is where that gets tested rather than argued about.
Yes. Accreditation narrows the field to two or three platforms before anything else is discussed, which makes the choice simpler. For work touching controlled information we deliver under CMMC Level 2 controls with US-citizen-only personnel. Say so on the discovery form and we will scope accordingly.
Discovery is one to two weeks regardless of scope. A first agent on a single knowledge source is typically two to four weeks. Something grounded across several sources and taking action runs four to eight. Multi-agent or autonomous work runs longer. Real timelines come out of discovery once scope is fixed.
Both, and the honest answer is that a lot of what gets asked for as an agent should be an application with a model inside it. Chat is a good interface for questions and a bad one for repetitive work. We will tell you which one you are describing.
Nobody does on day one, and estimates published before a pilot are close to worthless: the same agent can differ tenfold in running cost depending on how it is built and which model it calls. The honest sequence is to scope in discovery, run a pilot on real traffic, measure what it actually consumes, then set a budget from evidence. We will tell you what to watch and how to keep it in bounds. Anyone quoting you a firm running cost beforehand is guessing.
Yes. Everything lives in your own tenant under your own subscription, and the build is your intellectual property. The handover documentation includes what a future internal team or another partner would need to take over. We retain no exclusivity and we do not lock you into proprietary frameworks.
MyBotHub is SOPIQ’s agent and application practice. It is the AI dimension of a broader method that also covers people, process, and the infrastructure underneath. If the real problem turns out to be a process rather than a missing agent, you are already talking to the people who handle that.
The discovery questionnaire below is comprehensive. Answer what you can, we’ll work through the rest with you on the call. Or just give us your contact info and a few sentences about what you’re after.