AI Integration
AI where it's earned, not where it's trending.
Every business is being told to "add AI." Most of the time, that means a chatbot nobody uses or a feature that costs more to maintain than it saves. We take a different position: AI earns its place by improving something measurable, or it doesn't go in.
Our framework
Three questions before we recommend anything
We evaluate AI opportunities against a simple filter. If a use case can't answer all three, we don't build it — and we'll tell you why.
Is there a real problem here?
Not "could AI do this?" but "is this actually costing time, money, or quality right now?" If the answer isn't concrete, the project won't be either.
Is AI the right tool?
Some problems are better solved by a good workflow, a well-structured database, or a simple rule. We'll tell you when that's the case instead of reaching for a model by default.
Can we measure the outcome?
If we can't define what success looks like before we start, we can't know if what we built is working. Every AI integration we do has a before and an after.
Where it fits
The patterns we actually see value in
Not every business needs AI. But certain patterns — usually involving unstructured data, repetitive human judgment calls, or high-volume classification tasks — are genuine candidates.
Document extraction & classification
Pulling structured data out of contracts, invoices, forms, or reports that currently require someone to read and re-enter them. High volume, repetitive judgment — a strong fit.
Workflow triage & routing
Incoming requests, support tickets, or compliance flags that need to be categorized and routed before a human touches them. AI handles the first pass; humans handle exceptions.
Internal knowledge retrieval
When your team spends real time hunting through documentation, past decisions, or policy documents to answer routine questions, retrieval-augmented generation can meaningfully reduce that overhead.
Anomaly detection & alerting
Operational data — logs, financials, compliance records — that someone reviews periodically for exceptions. AI surfaces the signal; your team acts on it instead of reviewing everything.
Equally important
Where we'd tell you not to bother
This is where most shops stay quiet. We don't think that's useful.
Problems that are really just bad process
If the underlying workflow is broken, AI makes a broken workflow faster. Fix the process first.
Low-volume tasks with simple logic
If it happens twice a day and follows three rules, a deterministic function is faster, cheaper, and more reliable than a model.
Where errors have serious consequences
AI makes mistakes. If a wrong output in this context causes a compliance violation, financial loss, or safety issue — it needs a human in the loop, not just a model.
Data you don't actually have yet
Most AI projects underestimate the data problem. If the data isn't clean, structured, and available, that's what needs to be solved first.
Have a specific use case in mind?
Tell us what you're trying to accomplish. We'll tell you honestly whether AI is the right move — and if it is, how we'd approach it.