Signature Decision Wedge
Architecture & AI Readiness
Determine where AI makes sense and what architecture is required to support it before you start building.
Generative AI is a systems engineering problem, not just a model selection problem. Before you commit significant engineering resources to an AI initiative, you must determine if your current architecture, data pipelines, and infrastructure can support it—and whether the unit economics make sense.
We evaluate your systems for AI readiness, architectural scalability, and commercial viability. We don't just tell you which LLM to use; we tell you what needs to be fixed in your data ingestion, retrieval pipelines, and service boundaries to make AI work in production without destroying margins or creating unacceptable latency.
When Decision-Makers Call Us
Engagements are usually triggered by an upcoming capital commitment, an inflection point in scale, or a high-stakes disagreement.
What You Leave Knowing
- Where AI genuinely makes sense and creates commercial value
- What specific architecture is required to support the initiative
- What data limitations or quality issues currently block progress
- What security, latency, and operational risks exist
- Whether the unit economics of the proposed system actually work
- What exact components should be built first
Decision Deliverables
Decision Deliverables
An actionable architecture and AI strategy that defines the minimum viable path to production.
Real-World Engagement Teardown
An illustrative example of the problems we encounter, what our inspection uncovers, and the business outcome delivered.
Who It's For
- Engineering leadership planning a major architectural transition
- Product teams wanting to integrate LLMs or GenAI without destroying margins
- Companies needing to know if their current data infrastructure can support AI