The kelv agent gathers information scattered across your tools and gets work done within your company's rules. Use it from Slack, Teams, or its own workspace.
Claude
OpenAI
Gemini
DeepSeek
Qwen
Llama
ClaudeOpenAIGeminiDeepSeekQwenLlama
Mention it in Slack or Teams to hand off work like organizing documents or creating tasks. It checks your company's rules before acting, and every action goes into the audit log.
@kelvCan you pull the key points from last month's call notes for next week's proposal?
kelv is checking sourcesI reviewed 4 call notes and the proposal template, narrowed things down to 3 key points, and wrote a memo.
Screens, conversations, and company names are fictional design examples.
The information work depends on is spread across tools: conversations in Slack and Teams, documents in Drive and Notion, customer data in Salesforce. As the entry point in the work layer, the kelv agent takes requests, gathers what's needed, and passes it to the control layer.
Gathered information comes together in one place: the control layer. Each request is checked against company and team rules set in advance. The kelv agent can act only within the requester's own access and action permissions. It won't read files that person can't see, or take actions that person can't take. Every run passes through here and is logged in the same format.
Only work that has cleared the control layer runs on AI models. Use Claude, OpenAI, Gemini, or your in-house LLM, depending on the task. We can also help you build a local LLM on your own infrastructure.
ClaudeOpenAIGeminiLlamaQwen
In-house LLMkelv agentWrite requests the way you'd write any chat message. The kelv agent works within each department's rules and permissions.
The key points memo is ready. I'll send it to the client after approval.
Added as an automation. Results will arrive in your inbox.
Found 3 and drafted follow-up requests to the submitters.
Drafted the incident report and created 4 tasks.
Drafted 12 answers and requested review before publishing.
Created an evaluation sheet and shared it with the hiring team only.
Requests and results are illustrative examples.
Some integrations are planned. We'll confirm availability when you talk to us.
The kelv agent handles assigned work on its own. Irreversible actions, such as sending outside the company or deleting data, don't run without approval.
@kelvSend the proposal to Takahashi at the client
Sending outside the company needs approval. I've asked the Head of Sales to approve it.
Nothing is sent until approved.
@kelvDelete all the old customer data
Deleting customer data is blocked by the execution policy. Should I list the records and ask an admin to review them?
Information policies set which documents and storage locations each department or job can access.
Actions you specify, such as external sends or deletions, wait until the owner approves.
kelv records who asked, which model ran, and what was done. Auditors can trace the full history.
kelv has its own workspace for working with the agent. Work requested in Slack or Teams lands here too, as docs, tasks, and automations.
The kelv agent works with your team, building on earlier conversations.
Chat, docs, tasks, and automations live in the same project. The kelv agent draws on all of them.
AI works alongside your team within its permissions, taking the owner's decisions and past history into account.
Reply “Create 1, 2, and 3” and they go on the board as tasks with owners.
kelv turns calls and conversations into a doc of decisions, action items, and open questions. People just review and correct it.
Lists what was decided, including conditions and deadlines.
A table of who does what by when. Anything undecided is marked “TBD.”
Inaudible or unclear parts are flagged, not guessed.
Revision history is kept. Switch between view, editor, and Markdown to make changes.
Ask in chat, for example “Review stalled tasks every Monday at 9:00,” and it becomes an automation. Results go to the owner's inbox.
Say when, what, and how, and it's added to the list. No detailed setup screens.
Run on a set day and time, or when something happens, such as a monitoring alert.
Each run sends results to the owner's inbox, where they stay until marked done.
Logs who set it up and when, and when it last ran.
Tasks created from chat or meeting minutes appear on the board with parent tasks, owners, and status. People and AI track progress on the same board, from spec to done.
Decisions in chat or minutes become tasks with owners automatically.
People and AI share the same meaning for each status, such as spec, ready, in progress, and in review.
Group tasks under a parent and see overall progress.
Switch between list and board, and track by owner or ID. AI can be an owner too.
We'll confirm scope and timing when you talk to us.
THE HUMAN SIDE OF AIA HUMAN KIND OF PROGRESSBUILT FOR CLEAR INTENTSPACE FOR WHAT COMES NEXTHUMAN VALUE. MADE VISIBLE.CLEAR INTENT. QUIET PRECISION.
People decide what AI is trusted to do.
The kelv agent works within that decision.
More work goes to AI every day: documents, code, customer support. Yet the company hasn't decided what AI may be trusted with, and no one can see who uses it or how. Managers and approvers keep receiving work without knowing how much AI was involved.
Rules you set by department, team, or job apply to the kelv agent as they are. People and the agent work under the same rules.
kelv collects decision criteria, procedures, and prompts that worked. They carry into the kelv agent's work across departments and models.
Chats, docs, good prompts, edits
Ways of using AI that worked
The person and the rules decide what's shared
Only judgment and procedures are shared
Decision criteria, procedures, phrasing
Memory of your data and procedures
Across departments and models,
work follows the same approach
Expert judgment and the prompts and edits that worked on the job are kept instead of staying with individuals.
New team members and AI alike start working your company's way from day one.
The more it's used, the better it fits your work, with fewer fixes and send-backs from managers.
Manage every LLM the kelv agent uses under the same rules and metrics. Switch models or adopt new ones without rebuilding rules or logs.
ClaudeSet data scope, permissions, and approvals across models.
Keep requests, runs, decisions, and results in one format.
Track tokens, cost, and results by model.
Choose by performance, cost, and data requirements.
Beyond the platform, we help you choose, build, and run a local LLM on your own infrastructure. With a local LLM, even work involving data that can't leave the company can use AI.
With typical SaaS, your business data is stored in the vendor's environment. With kelv, you choose where the work layer and control layer data stores live: your own cloud, on-premises, or a dedicated environment hosted by kelv. Decide based on your security needs and internal policies.
Work layer and control layer data and local LLMs run in your own cloud.
Place work layer and control layer data stores to fit your requirements: your own cloud, on-premises, or a dedicated environment.
If you'd rather not run it yourself, kelv can host it.
The model layer can be built in your own environment as well. Work with data that can't leave the company stays inside it.
A screen that calls several AIs, or ROI analytics, can be built in-house. The hard part is keeping rules, permissions, logs, and a shared harness consistent across models. You also need a way to keep improving across the whole company.
Add kelv to Slack or Teams, or start in the workspace.
In your first department, decide what it can access, what it does, and who approves.
Try it with one team and widen its scope as you review the audit log.
We're designing for the requirements below. Tell us what you need and in what order.
Separate AI work from human-approved work, and assign owners
Log requests, runs, decisions, and results in one format across models
Set access, storage, and external sharing scope with per-job information policies
Use Claude, OpenAI, Gemini, or in-house and local LLMs by task
See tokens, cost, and results by user or agent
Connect authentication through SAML or OIDC and sync permission groups
Export logs to a SIEM or similar tools
Choose SaaS, a dedicated environment, or on-premises
Set the storage region and rules for retention and deletion
Support for certifications such as SOC 2 and ISMS
They're designed to. The display adapts to each tool, but rules, memory, and the audit log are shared. We'll confirm scope and timing when you talk to us.
Actions you mark as needing approval don't run until the owner approves. You choose which actions need approval, by department or job.
Yes. Work layer and control layer data stores can live in your own cloud, on-premises, or a dedicated environment hosted by kelv.
Claude, OpenAI, Gemini, DeepSeek, Qwen, and Llama. Use them alongside in-house or local LLMs, depending on the task.
Integrations with Google Drive, Notion, Salesforce, and others are planned. We'll confirm availability when you talk to us.
Pick a first department and job, and pilot with one team. Expand as you review the rules and logs.
Tell us about your work and requirements, and we'll suggest a first team and how to proceed.