Every case study maps a specific organizational problem to a concrete, verifiable Coherence capability.
No hypothetical scenarios.
Every proof point links to a live feature in the platform.
When engineers ship 40–60% faster, but nobody checks what they built.
The problem
Generative AI accelerated code output by 40–60%. But the teams responsible for validating that code. Business, risk, compliance. Still operate at traditional speed. The result: unverified technical decisions compound into market debt.
How Coherence solves it
Coherence closes the gap by mapping every code change to its business concept in real time. If a sprint ships code that doesn't map to a business requirement, the platform flags it. Before the next sprint starts.
Verified proof points
40–60%
faster shipping, zero faster validation
When every AI-generated deployment creates compliance gaps nobody can trace.
The problem
Financial institutions cannot prove to auditors how specific data flows through their architecture. Every AI-generated deployment creates regulatory drift. New code that hasn't been traced to KYC, AML, BIAN, or Basel compliance requirements.
How Coherence solves it
Coherence's Lineage module traces any field from the UI button to the specific KYC compliance document and source code function. When a regulator asks "how is this process implemented?". You have the answer in seconds, not weeks.
Verified proof points
6 weeks → seconds
audit evidence generation
When updating "Patient Consent" breaks 11 services nobody knew were connected.
The problem
If a healthcare company needs to update a "Patient Consent" service, they cannot predict which of their thousands of microservices will break. The blast radius of any architectural change is invisible. Until it causes a HIPAA incident.
How Coherence solves it
"Fork the World". Create an isolated copy of the digital twin. Simulate the service removal. See the exact dependency cascade and impacted services highlighted in red before writing a single line of code.
Verified proof points
$0
simulation cost vs. $1.5M–$20M for failed migrations
When what strategy says and what engineering builds are two different things.
The problem
As a SaaS company scales, its product roadmap drifts from what's actually in the codebase. 70–90% of strategic initiatives fail. Not because the strategy was wrong, but because there was no map between strategy and execution.
How Coherence solves it
Coherence uses tree-sitter AST parsing to map actual function-level dependencies and cross-reference them against business strategy. Orphan Chunks. Code that doesn't map to any business requirement. Are surfaced automatically.
Verified proof points
70–90%
of strategic initiatives fail from execution drift
When "Revenue" means 3 different things across 3 teams.
The problem
Every BI tool, every team, every department has its own definition of core business terms. Finance's "Revenue" ≠ Product's "Revenue" ≠ Sales' "Revenue." Dashboards contradict each other. Reports are unreliable. Nobody trusts the numbers.
How Coherence solves it
Coherence enforces a single, human-approved definition for every concept and exposes it as a governed semantic layer. Any BI tool can query it. Drift from the canonical definition is flagged automatically.
Verified proof points
1
definition per concept. Everywhere
When your contracts say X but your systems do Y.
The problem
Written legal contracts exist in a world entirely separate from the technical systems responsible for enforcing them. There is no automated link between "the contract says X" and "the system does X." Non-compliance is invisible until litigation.
How Coherence solves it
Coherence maps legal obligations (CLM, Indenture agreements) directly to the service-level code that executes them. A live, traceable link between contract terms and technical enforcement. If a code change would violate a contractual obligation. It's flagged before deployment.
Verified proof points
27+
legal concepts mapped to enforcement code
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