Govern AI coding across every team, repo, model, and tool.
KriyaCore is a local-first AI engineering platform for organizations that want Cursor-like developer speed with centralized model governance, reusable AI playbooks, approval gates, audit trails, and cost controls.
Uses approved repo files, warehouse metadata, tests, and PR summary rules.
Created a CDC model plan, proposed two file edits, generated a validation command, and prepared a PR summary. Source files remain local until approved.
AI coding at enterprise scale needs more than individual IDE seats.
KriyaCore gives engineering organizations a control plane for safe adoption: source privacy, approved models, repeatable workflows, permissions, integrations, and measurable outcomes.
Local-first execution
The desktop runtime reads files, runs tests, manages git, and connects local MCP servers on the developer machine. Cloud receives metadata and approved summaries.
Customer AI gateway
Route every request through organization policy across OpenAI, Anthropic, Azure OpenAI, Bedrock, Vertex AI, OCI GenAI, Ollama, vLLM, and private endpoints.
Reusable AI playbooks
Turn repeated engineering work into versioned capabilities with instructions, required context, validation commands, permissions, metrics, and ownership.
A Cursor-like local client connected to an enterprise control plane.
Developers work in a downloadable desktop/IDE experience. Admins manage the models, budgets, policies, playbooks, integrations, and audit history from the cloud.
Start with a pilot workspaceSelect repo
KriyaCore indexes repository structure locally without uploading the full codebase.
Choose playbook
Pick a governed workflow such as dbt CDC, API hardening, migration review, or flaky CI repair.
Approve context
Developers approve files, commands, model calls, and tool usage before execution.
Track outcome
The control plane stores run status, validation results, quality signals, token usage, and cost.
Bring your own models and policies.
Customer admins control model availability, budgets, roles, routing rules, and repository-level restrictions. Developers can use auto-selection while the organization keeps policy ownership.
Connect tools without turning them into unmanaged shadow workflows.
MCP servers, API connectors, documentation sources, source control, CI/CD, databases, and internal systems can be governed as first-class integrations with permissions and usage analytics.
Playbooks turn expert workflows into measurable team capability.
Instead of saving prompts in personal notes, teams publish governed AI playbooks with versions, required context, validation commands, integrations, and reuse metrics.
Versioned, reusable, validated, and governed by team policy.
Versioned, reusable, validated, and governed by team policy.
Versioned, reusable, validated, and governed by team policy.
Versioned, reusable, validated, and governed by team policy.
Versioned, reusable, validated, and governed by team policy.
Versioned, reusable, validated, and governed by team policy.
Why KriyaCore instead of unmanaged AI IDE rollout?
Individual AI IDE licenses improve local productivity. KriyaCore adds the governance layer needed to scale AI engineering safely across departments, budgets, compliance needs, and repositories.
Measure whether AI engineering is actually working.
Track utilization, cost, quality, reliability, and adoption across teams so leaders can expand the pilot with evidence instead of guesswork.
Measure your pilotStart with one team, one policy, and reusable playbooks.
Validate source privacy, model governance, cost controls, MCP integrations, and measurable reuse before broad enterprise rollout.