Keva organises support work.
Evidence stays explicit.
Keva provides ticket, knowledge, connector-catalogue, and governed-automation paths. Platform effects and reply delivery are currently suspended, while typed tenant-scoped memory is bounded and its behavioral improvement remains unproven.
Your support team is drowning in
repetitive tickets
80% of support tickets are variations of the same questions. Your team spends hours copy-pasting answers, updating websites manually, and context-switching between tools. Customers wait. Agents burn out. Revenue leaks through delayed responses.
Tickets, source catalogues, memory,
and explicit readiness boundaries.
Governed ticket agent
Keva has source paths for classification, knowledge retrieval, proposals, and reply drafts. Platform effects and reply delivery are suspended, and no full lifecycle is claimed product-proven.
Platform source catalogues
15 platform source catalogues contain 842 risk-classified action definitions. Catalogue depth is not runtime availability: platform effects are currently suspended while provider custody is proven.
See platformsKeva Brain
Persistent memory that gets smarter. Remembers what it did for each customer, understands platform configs, and warns itself about past failures before acting.
Learn moreUniversal Browser Agent
Keva contains browser-automation source paths for selected workflows that are not covered by a provider API. Interactive browser and platform-effect execution are currently suspended, so these paths are not a promise of SSO, 2FA, provider behavior, or universal availability.
WAF rule updates, deployment rollbacks, environment variable changes, webhook configurations, settings not exposed via API
Cloudflare WAF rules, Vercel env vars, Stripe webhook endpoints, AWS IAM policies, Railway scaling
Any Language, Automatically
Detects customer's language during classification — zero extra latency. Spanish ticket? Spanish reply. 20+ languages, no configuration needed.
Smart Triage & Routing
Every ticket is classified by category, sentiment, complexity, and language. Easy tickets use a faster model. Complex tickets get the full agent.
Trust Controls
5-level autonomy slider. Confirm with customer before actions. Daily action limits.
Omnichannel
Email, Slack, Teams, SMS, Telegram, widget, API. One unified inbox.
CRM Sync
Salesforce & HubSpot sync paths exist in source. Deployed bidirectional behavior remains unverified.
Team Collaboration
Collision detection, @mentions, canned responses. Work together seamlessly.
Visual Workflows
Drag-and-drop automation builder. Triggers, conditions, actions.
Customer Profiles
A tenant-wide profile model exists in source. Authority, workspace policy, privacy custody, and product proof remain open.
Knowledge Base
RAG grounding. Auto-extracts new articles from resolved tickets.
RBAC & Security
Role and audit controls exist locally. Formal SOC 2 readiness remains unproven.
Draft with detected language,
then preserve the delivery boundary
Classification source can return a ticket language and drafting can use that value. Reply delivery is currently suspended, so no automatic multilingual send is claimed.
Brand voice, knowledge retrieval, approval, and platform context have separate admission and proof boundaries; platform actions remain suspended.
Hola, tengo un problema con mi pedido #4521. No he recibido la confirmacion de envio.
Hola! Gracias por contactarnos. He revisado tu pedido #4521 y puedo confirmar que fue enviado ayer. Te reenvio la confirmacion ahora mismo.
Define the intended effect.
Prove it before enabling.
A duplicate-charge workflow would need exact Stripe setup, ticket custody, risk and approval admission, durable provider identity, independent effect read-back, and ambiguity-safe recovery. Action-plan and platform execution are currently suspended before provider work.
"The customer was charged twice for order #8923. Can you refund the duplicate?"
1. Process refund $49.99 on Stripe charge ch_3Nq...
2. Reply confirming refund with reference number
An AI that remembers
everything it did
Most AI agents start from scratch on every ticket. Keva Brain maintains persistent knowledge — what actions it took for each customer, what it learned about each platform, and what failed recently.
Before proposing a new action, it checks whether that action type has been failing on your platform, preventing repeated mistakes.
Learn how Keva Brain works## Past Actions for This Customer
- [Mar 14] Updated product price from $29 to $39
- [Mar 12] Processed refund $49.99 for order #4821
- [Mar 10] Created DNS record for api.example.com [FAILED]
WARNING: "create_dns_record" failed 2 times on this connector in the last 7 days. Proceed with caution.
From discovery to resolution.
We set it up. You stay in control.
Discovery call
We review your stack, ticket patterns, and the proof boundaries that matter. Duration depends on scope.
We build your setup
We identify available setup paths, required configuration, and any manual or unavailable connector work before credentials are supplied.
Prove before enabling
Run focused local checks, then require configured, deployed, product-driven, and independently verified evidence before any execution path is enabled.
Ongoing optimization
Direct line to our team. We tune the AI as your business evolves. Monthly reviews included.
Support from your
terminal
The Keva CLI gives developers an AI support agent right where they work. Run keva chat to start a real-time conversation with streaming responses.
Attach files for context, resume previous conversations, and let the agent fix issues on your platforms — all without leaving the terminal. Manage tickets programmatically with --json support.
~/project $ keva chat "my Railway deploy is failing"
SSE connected...
you 2:31 PM
my Railway deploy is failing
Support Agent 2:31 PM
Illustrative target only: a governed Railway workflow would diagnose the missing variable, acquire approval, attempt the change, and independently verify the redeploy. Platform execution is currently suspended.
~/project $ keva tickets list --status open --json
_
Zendesk, Monday.com, Notion, or that internal tool nobody's heard of?
Discuss whether it belongs on the roadmap →No design-partner automatic-resolution rate is claimed yet. A real cohort, fixed denominator, product-driven outcomes, and independent read-back are required first.
Multilingual source paths exist, but no unattended 12-country customer outcome or zero-work claim is presented as behavioral proof.
Stop answering the same questions.
Review Keva's ticket, knowledge, memory, and connector source paths—together with the proof still required for your stack.
Availability varies; no unattended outcome is implied.