Context Mesh · Available now

Create enterprise context before AI writes a line of code.

Add your product definition, tickets, artifacts and code into one connected Context Mesh. Our context analyzer scores it and shows you exactly what to fix.

Start 30-day evaluation See a sample report
Sample mesh — your scores appear after analysis

Four kinds of context. One score.

AI writes better code when it can see the whole picture. Each part of the mesh is scored separately, so you know where to improve it.

Discover
What should we build?
The problem, the solution, affected personas and non-functional requirements.
External
Your tickets
Jira and Azure DevOps work items, broken into intent, scope, actors, dependencies and acceptance criteria.
Files
Existing artifacts
Specs, architecture and policy documents from your current or a similar application.
Code
Your repositories
Git projects read structurally — functions, classes, modules — the way a senior architect reads code.
The readiness report

A score you can act on.

Every analysis flags gaps: missing requirements, outdated docs, unlinked ticket dependencies. Each one comes with a specific fix, sorted by how much it will raise your score. A better score ensures increased relevance and higher quality code produced. All of which translates into reduced token usage and cost.

STRONG80–100Ready to ground AI-assisted development.
GOOD55–79Usable, with a few gaps worth closing.
THIN30–54Expect rework. Key context is missing.
WEAK0–29AI will guess. Add context first.
Claims Portal ModernizationSample analysis · 4 recommendations
65GOOD
External
No tickets connectedConnect the Jira project or Azure DevOps board for this work so scope and acceptance criteria can be traced.
+18
Code
3 modules have no linked requirementMap the claims, billing and notifications modules to the stories they implement.
+8
Code
No test suite found for the API layerInclude the existing API tests so generated changes can be validated against them.
+5
Files
Data-retention policy is 2 years oldReplace it with the current policy so generated code follows today’s rules.
+1

How it works

  1. 01Build your meshAdd your product definition, connect tickets and repositories, and upload existing artifacts.
  2. 02AnalyzeOur context analyzer reads code structure, breaks down tickets and parses documents into connected knowledge.
  3. 03ScoreGet a 0–100 score for each kind of context, plus an overall mesh strength.
  4. 04ImproveFollow specific guidance to close gaps before you use AI to build the code.

Value for everyone

  • Software DevelopersComplete, grounded context from the start, so there is less prompt iteration and debugging.
  • Enterprise ArchitectsGenerated code follows NFRs, security guidelines and system design, with full traceability.
  • Product Owners & Engineering ManagersUser stories cross-referenced with the codebase, so there are fewer refinement cycles and less QA friction.
  • CTOs & VPs of EngineeringPredictable returns on AI spend, compliance enforced, no architectural drift across repos.

Enterprise-grade by default

  • Enterprise guidanceRecommendations for improving your context, aligned to your organization’s standards.
  • Tenant-isolatedYour context stays in your own isolated environment.
  • Role-based access controlControl who can view, edit and analyze each part of the mesh.
  • Short-lived device tokensAuthorization without long-lived credentials.
  • Incremental indexingSnapshot-based re-analysis keeps LLM and API costs down.

Score your context for 30 days.

Free enterprise evaluation.

Start evaluation