Six real-world scenarios. Each maps an organizational problem to a concrete Coherence deployment. Built from the patterns we see across enterprise engagements.
When AI generates 60% of your codebase completely blind.
The problem
Generative AI is accelerating output, but Copilot and Devin have no persistent memory of your organizational architecture. The result: ungrounded, unverified code compounding into structural debt.
The approach
The ontology becomes the persistent architectural context for every external agent. Through the Model Context Protocol (MCP), Copilot and Devin read the knowledge graph before generating a single function. Every output is grounded.
Verified proof points
MCP
persistent architectural context for every agent
Deploying AI agents without governance boundaries is a compliance risk.
The problem
Financial institutions want to deploy AI-driven logic, but LLMs are stochastic. Allowing ungoverned agents to mutate banking pipelines creates compliance risk across KYC, AML, and Basel frameworks.
The approach
The ontology bounds every agent. Compliance constraints are encoded into the knowledge graph. Agents that attempt out-of-scope mutations are blocked before execution. Governed intelligence over stochastic output.
Verified proof points
Governed
every agent bounded by the ontology
When three-year enterprise integration cycles kill institutional velocity.
The problem
Enterprise logic forces engineers to navigate thousands of legacy tables and heavy REST APIs. Building a new process across disparate systems takes years and millions of dollars before a single business outcome is achieved.
The approach
Collapse the middleware. Your engineers write Python logic directly against the ontology, executing complex integrations in 90 days instead of 3-year waterfall cycles.
Verified proof points
90 Days
from blank canvas to production execution
When deleting a single pipeline breaks twelve downstream services.
The problem
You need to know the blast radius of an architectural shift before deployment. Changing a service creates unquantifiable risks because cloud infrastructure lacks a cohesive topological map.
The approach
Ingest your infrastructure into the ontology. Run downstream failure simulations. Compute the full blast radius of cloud migrations before a single server is touched.
Verified proof points
Pre-deploy
full blast radius computed before execution
When executives read Jira, engineers watch GitHub, and DevOps stare at CI/CD.
The problem
The organization exists in disconnected silos. Strategy and engineering are detached. Initiatives fail because there is no unified view bridging issue tracking, source control, and deployment pipelines.
The approach
Pull the entire product and software development lifecycle into the ontology. Generate pull requests, prioritize tickets based on structural risk, and govern rollouts from a single unified surface.
Verified proof points
1
unified command surface
Manual semantic mapping of massive regulatory data lakes is structurally archaic.
The problem
Parsing institutional knowledge, complex legal indentures, and compliance strictures into a graph structure historically requires years of ontology architects manually writing definitions.
The approach
The extraction engine parses unstructured documentation and regulatory matrices into structured Domain Packs under human review. Map the entire operational baseline at machine speed, with expert curation.
Verified proof points
Minutes
from documents to structured ontology
This site uses cookies
We use essential cookies for the site to function and analytics cookies (Google Analytics) to understand how you use it. Analytics cookies are only activated with your consent. We do not track you across other websites. Your data is stored in the EU and processed in accordance with GDPR. Read our Privacy Policy