ENTERPRISE & CLOUD
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IDE-BASED AI ASSISTANTS · IN-HOUSE LLMS · YOUR OWN AGENTS, GOVERNED THE SAME WAY, LIVE VIA MCP.
BUILT FOR BOTH
Carry the system forward as it runs today.
For teams whose mandate is to get off an aging runtime without changing what the system does.
Modernize and improve in the same pass.
For teams who want a better architecture, not a line-for-line copy. New capability is added and tracked as net-new.
WORKED EXAMPLE
A developer’s AI coding assistant proposes extending how long a session token stays valid.
Change proposed.
AuthSessionService.java, lines 82 to 90: extend session token lifetime from 15 to 60 minutes.
Governing rule.
R-SESS-014, session tokens expire at 15 minutes, bound at 4 call sites.
Verdict.
Not behavior-preserving.
Resolution path.
Revise the code, or formally govern the rule change. Flagged until then.
LLMs propose. CodeIntent verifies. This runs live today via MCP, from the tools your developers already use.
AT SCALE
Independent benchmark research shows LLM coding agents lose about 76 points of accuracy as codebases grow. The bigger and more interconnected your systems, the more that gap costs you. CodeIntent doesn’t replace the agent, it checks what the agent proposes, before it reaches production.
WHERE LLMS FIT
LLMS DO
Generate candidate code quickly
Refactor obvious paths
Propose architecture changes
Power agents and copilots
CODEINTENT DOES
Supplies deterministic semantic context
Preserves source-to-target traceability
Verifies preservation and change decisions
Creates an audit trail of what changed and why
FITS INTO YOUR STACK
If your codebase is already in reasonable shape, Ingest, Modernize, and Validate move fast. Govern is where CodeIntent earns its place: every change your developers and AI agents make afterward is checked against the same baseline, before it's accepted into production.
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HOLONIC
The deterministic evidence layer underneath legacy modernization and the agentic enterprise.
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