StrategyOS runs a complete strategic analysis of your business — fifteen established frameworks, executed by coordinated AI agents, then reconciled into one clear set of priorities. The work a consulting firm bills for over months, delivered as software.
Proper strategic analysis — SWOT, PESTEL, Porter's Five Forces, unit economics, market sizing — is what consulting firms sell for tens of thousands of dollars over several months. Small and mid-sized companies cannot buy it at that price, so they skip it and decide on instinct. Meanwhile they are adopting AI faster than they are getting value from it.
The gap isn't model capability. Companies have the tools. What they lack is the structured analysis that tells them where to point those tools — and which of the answers they get back is worth acting on.
Each framework is owned by a specialist agent that receives only the inputs that framework needs. A coordination layer then reads every output and resolves it into a single view.
You connect your business data and answer a guided intake. The system asks precisely what each analysis requires — nothing more, and nothing it can derive on its own.
Specialist agents run fifteen established strategic frameworks concurrently. Each writes its own section, grounded in your data rather than in general knowledge about your industry.
A coordination layer reads all of it and answers the questions that matter: where do the analyses agree, where do they contradict, which risk is genuinely critical, and what should you do first.
Anyone can prompt a language model for a SWOT. The difficulty is producing fifteen analyses that don't contradict each other, and being honest about which conclusions are actually supported by the data.
We have shipped this problem before. Our founding team built and runs BrokerAI, a production AI system that converts raw shipping documents into filed customs declarations for a regulated government portal. Its core is a hierarchical agent classifier over a 13,000-leaf tariff nomenclature, gated by an evaluation harness built on real filed declarations: 95% precision at chapter level and 82% coverage with calibrated abstention, against roughly 23% for the naive single-prompt baseline it replaced. Low-confidence outputs return a specific reason instead of a guess — because in a regulated market, a confident wrong answer is worse than no answer.
Built in-house. No outsourcing. Special prize winners at Hack Armenia 2026, a national LLM hackathon judged on technical implementation and measurable results.
If you run a business and you'd trade an hour of your time for an early look at what the analysis says about it, we want to talk to you.