Harmont
For building operational systems and managing complex workflows across multinational organizations.
Most multinational organizations believe their pricing intelligence process works — until they calculate what it actually costs. Analyst time, consolidation errors, outdated data, and structural inconsistencies between markets add up to $100,000–$150,000 per year for a mid-sized organization. And that's before counting the decisions made on wrong data.
Competitors don't have a single pricing strategy — they have dozens, one per segment. A competitor charging $99 in the US, $149 in the UK, and $49 in Southeast Asia isn't confused. They're executing three strategies simultaneously. Traditional price collection misses all of this. Segmentation analysis reveals what they're actually doing — and what they'll do next.
Building an AI assistant that books appointments is straightforward. Building one that handles edge cases, follows your SOP, integrates with your vendor software, and behaves consistently under pressure — that's an engineering problem. We break down the three-layer architecture and four-level testing framework we use when deploying voice agents for service businesses.
Every operational system starts as a workaround. Someone builds a spreadsheet that works well enough. A macro gets added. Another team adapts it. By the time the problem is visible, the system is fragile, poorly documented, and understood by one person. We've seen this pattern across industries. Here's why it happens and what the alternative looks like in practice.
Programming tools have evolved into something more powerful than programmers expected: a system for managing complexity at scale. If you're writing a strategy document, maintaining a knowledge base, or running a long-term research project — version control, structured editing, and AI-assisted writing are tools for you too, not just for engineers.
Not every operational problem requires building something from scratch. Pricing Beacon is one example of a governed operational system — structured data collection, standardized categories, real-time visibility across markets — that replaces a workflow most organizations were managing manually. The pattern applies to other operations too. Here's how to identify where it fits.