Answers the lead that arrived at 2 a.m.
It reads the enquiry, checks what you already know about the company, writes a reply in your voice, and sends it — within five minutes, whether or not anyone is awake.
The system of action
The follow-up happens.The record updates.Nobody typed a thing.
Felesh puts AI agents to work inside your operation — they research, draft, follow up, and keep the record true. Agents do the legwork; your team makes the calls. Delivered as a managed cloud service, isolated to your organization.
Your agents. Your data. Your operation.
New enquiry — Nordwind Logistics 02:14
- Read the enquiry and matched it to an existing account
- Drafted a reply citing the pricing they asked about
- Sent it, and set a follow-up for Thursday
receipt answered in 3m 41s · autonomy: acts with approval
The ceiling
Your output is capped by headcount.
Every lead answered, every promise kept, every record updated — all of it costs someone's hours. To handle 50% more, you hire 50% more. More software to type into does not move that ceiling. It just moves the typing.
In plain terms
What an agent actually does
Not a chatbot, and not a suggestion box. An agent is given a job and does the job — end to end, on your data, inside your rules.
Chases the promise nobody wrote down
A price was promised on a call three days ago and never sent. The agent notices the silence, drafts the follow-up with the right number in it, and closes the loop.
Keeps the record true
The record updates from the work itself, as the work happens — not from whoever remembers to log it on Friday afternoon.
You set the limit
Start where you are comfortable. Move the line when you are ready.
Autonomy is a dial, not a switch. Every agent starts by suggesting and earns more room to act as it proves itself on the record — and you can move the line back at any time.
Start where you are comfortable. Move the line when you are ready.
Autonomy is a dial, not a switch. Every agent starts by suggesting and earns more room to act as it proves itself on the record — and you can move the line back at any time.
Felesh proposes an action and shows its reasoning. Your team decides and acts. This is the safest starting point, and where v1 operates today.
Felesh prepares the reply or the update. An operator reviews and sends. The system does the work; the operator retains the judgment.
Felesh executes the action once approved — auto-with-review. Routine operations run at machine speed. An operator retains the final say.
Within defined boundaries, Felesh handles routine work end-to-end. A receipt backs every action. You control the autonomy dial and can turn it back at any time.
Most teams start at the first two rungs and stay there for months. That is a reasonable way to run this.
The glass box
Every action leaves a receipt.
What the agent saw, why it acted, what it did, and under which autonomy. Nothing happens off the record — so you can check any single action rather than having to trust the whole system at once.
receipt · action #4821
action›Sent follow-up to a stalled opportunity
why›No reply in 3 days; a price was promised on the call
drew on›last conversation, the current pricing, the open commitment
autonomy›auto-with-review · approved by the operator
outcome›reply received in 2h; opportunity advanced a stage
Felesh CRM
Every lead answered. Every promise kept.
The system of action for revenue teams. Every inbound lead gets a qualified, personalized response in under five minutes, and nothing your customers are promised is forgotten. Felesh CRM is the first product built on Felesh.
Ask about Felesh CRMWhat Felesh CRM gets right
Every lead answered in minutes
Each inbound lead is captured, qualified, and answered with a personalized reply in under five minutes — at 2 a.m. and at quarter-end, not just when an operator is at their desk.
Nothing falls through
Every commitment made to a customer is tracked to completion. The follow-up that used to live in an operator's memory now lives on the record, and the work to close it gets done.
The record keeps itself current
As work happens, the customer record updates from the work itself. What you see reflects what was actually said and done, not what someone remembered to log later.
You hold the controls
Every action carries a receipt: what the agent saw, why it acted, and what it did. Each agent starts in suggest-mode and earns more autonomy only as it proves itself — controlled by the autonomy dial you set.
The platform beneath
Built on Felesh
Felesh CRM is the first product on a general platform for agent-run work. The same foundation already carries live operations in financial services and government. It rests on three commitments that do not move.
Control & isolation
Each organization runs in its own isolated environment — separate identities, separate data stores, and separate secrets. No organization sees another's data, by design. Your data stays inside that boundary, never shared or used to train outside models. Data-residency options are available with Felesh for enterprise.
Adaptability
Felesh is shaped to your domain rather than forcing your work into a fixed template. It runs today in financial services and government. The Builder designs new agentic systems through conversation — you describe it, and we deliver the deployed system.
Production-grade by default
Your data is replicated and stays available through failures. Every change is auditable and reversible, blueprints are versioned with immutable history, and you get full operational visibility. We run our own operation on the exact foundation we hand you.
In production
Already running where the stakes are highest
A financial-services institution
Agents turn inbound customer signals into qualified, traceable actions inside the institution's own isolated environment. Every step is recorded, every decision is reversible, and nothing leaves the boundary it is meant to stay in.
A government services agency
Citizen-facing requests are captured, routed, and acted on under human review. Every action carries a receipt, and the record keeps itself current as cases move through the system.
We do not name customers on a marketing page. In a working session, we can walk you through the system itself — the same discretion we extend to you.
Why Felesh is different
Incumbent CRMs sell seats, and they store records meant to be typed into by people. Agents that actually do the work threaten the first and do not fit the second. The incumbent answer is an AI panel bolted onto the old record. Felesh is built the other way around: the record is a shared substrate where human operators and AI agents work as peers. Your system of record says what happened; Felesh is where the work gets done. This is a different foundation, not a different feature — and a panel cannot reach it.