Your organization has decades of institutional knowledge — processes, decisions, expertise. Your AI systems can't access any of it. Context engineering is the discipline that changes that.
The methodology behind our engagements
20
In-depth technical articles on context engineering, agent architecture, and production AI systems
173+
Competitors analyzed across agent frameworks, orchestration platforms, and AI consultancies
5
Production-ready reference architectures published as open-source Python templates
Every number is verifiable. Read the articles. Download the templates.
Traditional AI Consultancy
Context Engineering Diagnostic
Your AI agents give generic answers because they can't access your organization's actual knowledge, processes, and decision history.
You've invested in AI tooling, but leadership is losing confidence because the outputs don't reflect what your organization actually knows.
AI should amplify institutional knowledge, not ignore it.
Not sure where your context layer is breaking? Start with a diagnostic.
Start With a DiagnosticContext engineering is the discipline of structuring organizational knowledge — documents, processes, decision frameworks, expertise — so AI systems can effectively use it.
It's the layer between your data and your AI. Without it, agents hallucinate. With it, they reason with your organization's actual knowledge.
Take our enterprise readiness assessment or book a diagnostic call. We identify where your context layer is broken.
We design and build your context architecture — knowledge structure, retrieval systems, agent context flows — in a focused sprint.
Your AI systems access real organizational knowledge. Agents give answers grounded in your actual data, processes, and expertise.
Every month without proper context engineering, your AI systems compound bad habits — hallucinating answers, ignoring institutional knowledge, eroding team trust in AI. The gap between “AI demo” and “AI that actually works with our data” grows wider.
Every month you delay, your team accumulates context debt.
Agent failures multiply as teams build workarounds on top of workarounds.
Debugging costs compound — each new agent inherits the context gaps of every agent before it.
The longer you wait, the more expensive the fix becomes.
We're building our case study library as we complete engagements. Here's the format you can expect.
Case Study — Coming Soon
Enterprise SaaS
AI agents accessing institutional knowledge
Before
Agent accuracy on internal queries
After
Agent accuracy after context engineering
Case Study — Coming Soon
Financial Services
Production agent failure recovery
Before
Mean time to diagnose agent failures
After
Mean time after structured context layer
Case Study — Coming Soon
Healthcare Tech
Multi-agent coordination at scale
Before
Agent coordination success rate
After
Coordination rate with context architecture
No fabricated results. We publish real metrics from real engagements as they complete.
The diagnostic is a focused engagement, not a free sales call. We deliver a written context architecture assessment.
Take the free enterprise readiness assessment. Get a personalized report on your context engineering maturity across 5 dimensions.
Take the Assessment — FreeTakes 3 minutes because we focus on the 5 dimensions that matter most. No email required to start.
Enterprise AI fails because organizations can't structure their knowledge for AI consumption. We engineer the context layer that makes it work.