Natural-language search
Instead of building a filter field by field, the manager simply asks: which engineer customers are frustrated? The search reads the conversation base and returns the people who fit.
Operations · Smart CRM
The CRM your own agent fills in
Contact details, history and a read on every customer are born inside the conversation — on WhatsApp, on webchat and on the phone. Nobody has to reopen the system at the end of the day to type up what was already said.
Part of the Hal-AI Agentic platform. The same agent, the same memory, across every channel.
Customer and conversation hub
Everything that happened with that customer, on a single screen
The smart CRM brings the contact base and the service history together, channel by channel. It is where the human team follows conversations, takes them over and hands them back to the agent.
Customer record
Contact details, the channel they came in through and the full message history. The record travels with the conversation: opening the chat opens the record.
AI-generated mood and tags
From the most recent messages, the agent tags customer traits and reads the mood of the conversation. The instruction that guides that reading is written by the manager, in plain text.
Take over and hand back
Pause the bot, answer as a human, transfer to another operator and close the conversation — which returns the chat to the agent without dropping the thread.
Quick replies and templates
A library of ready-made replies for the operator, plus official WhatsApp template sends for when the 24-hour window has already closed.
Pinned conversations and export
Pin the conversations you are watching and export a full thread when it has to leave the screen — audit, legal, handover.
WhatsApp
Webchat
Agent test chat
Voice calls
The CRM separates conversations by origin and keeps the customer history unified.
The difference
The agent fills the pipeline while it works
A traditional CRM only knows what somebody typed into it. Here the record is a by-product of the conversation: the agent is the one who talked to the customer, and the agent is the one who writes it down.
A CRM that depends on typing
- The conversation happens on WhatsApp and the record is left for later — when it happens at all.
- Every operator takes notes their own way, so data quality changes from person to person.
- The conversation lives in one app and the record in another system; nobody sees both at once.
- Segmenting the base means trusting fields that were filled in months ago.
- When the customer comes back, the history is in the head of whoever handled it last time.
A CRM the agent fills in
- The contact enters the CRM on the very first message, with origin channel and history.
- Tags, traits and mood are generated from what was actually said, on the same criteria for everyone.
- Conversation and record are one screen: the operator replies with the profile right beside it.
- Segmentation uses the traits the conversation produced, and it feeds your campaigns.
- The agent's long-term memory holds the context between conversations weeks apart.
It is not a form waiting for someone to fill it in. It is the conversation becoming a record the moment it happens.
The customer reaches out
A message on official WhatsApp, a chat on your site or a phone call. The agent answers with the personality and the tools you configured.
The agent resolves and records
It queries your system's APIs, answers the customer and leaves the contact, the conversation history and the read on that customer in the CRM.
A human steps in when it matters
Taking the card pauses the bot in that chat. Once the conversation is closed, the agent picks up exactly where it left off.
The base turns into action
The traits you accumulate segment your campaigns, and the whole operation shows up in reports by period, by queue and by operator.
Customer Intelligence
Advanced Agentic CRM
Start Conversation
9 customers listed in this selection
Aurora Store
First Message: 06/12/2026 · Last Message: today 9:41 am
Channel Used: WhatsApp · +1 555 0xx-xxxx
Profession: engineering
Desire: second location
Objection: lead time
Moodsatisfied
12 tags →
Vitoria Clinic
First Message: 03/03/2026 · Last Message: yesterday 5:22 pm
Channel Used: Webchat · Customer IP: 198.51.100.24
Profession: clinical engineering
Objection: rescheduling
Desire: not recorded
Moodfrustrated
7 tags →
Route 12 Logistics
First Message: 08/21/2026 · Last Message: today 8:05 am
Channel Used: Webchat · Unknown name on first contact
Profession: operations engineering
Objection: pickup window
Desire: fixed route
Moodimpatient
4 tags →
What you’re looking at
In the top menu, the open tab is the CRM. Where a panel of filters would normally sit, there is a plain-English question — which engineering customers are frustrated? — and, just below it, the channel strip counting how many records came from each origin. The selection returns nine profiles: each one with the date of the first and the last message, the channel used, the trait chips the agent wrote while reading the conversation (profession, want, objection) and, at the foot of the card, that customer’s mood meter.
The advantage
Nobody built a filter field by field, and nobody had to sweep the base beforehand to fill in what was missing: the traits were recorded during each conversation, not typed in after it. A coordinator reaches the frustrated cases without depending on someone having ticked a box months ago, and takes that same selection straight into a campaign — no spreadsheet export, no ticket for the data team.
Only on Hal-AI
The plain-English question does not become SQL written on the spot by a model. It runs over curated, parameterized queries, inside a closed list of what the agent is allowed to do with the base — the same security design the platform applies everywhere an autonomous agent touches customer data. A workflow automation can filter a list that is already there; it does not read the conversation to conclude that this customer is an engineer and is frustrated.
Notice what is not there: no columns to drag a card between. What organizes the base are the traits the conversations themselves produced, and it is that selection — not a stage filled in by hand — that becomes a campaign audience. Names, counts, phone numbers and addresses shown are fictional.
One screen
Conversation and record in the same place
The operator never switches between a messaging app and the customer file. They read the history, see the traits and reply on the same screen — and everything they do is logged on the card.
- Full history per channel, from first contact to the latest turn
- Customer record beside the conversation, with traits and mood
- Pause and resume the agent without a parallel thread
- Transfers that change the card owner and preserve who said what
- Attachments, audio and images handled by the same agent
The phone comes in too
Calls answered by AI get their own screen: the calls active right now, a history of who called and which agent answered, and a turn-by-turn transcript of every voice conversation. Phone-number filtering included.
Voice and telephonyMemory, not a dead archive
The history is vectorized and the agent extracts stable facts and customer traits, with a sense of time. This is the technology behind the patent filed with the USPTO — the reason a customer never starts over from scratch.
The memory patent
Wired into the rest of the operation
The CRM does not stand alone
The base your conversations build is the same one that launches campaigns, organizes the human service queue and turns into a report at the end of the period.
Campaigns
Segment the base by query and by customer traits, follow the send on a kanban board and let the guardian protect your official number.
Call center
Queues, operators, scoped supervisors and real-time monitoring of open chats — with the takeover that pauses the agent automatically.
Reports
Chats per operator, human chats per period and customers per period, filtered by queue and by channel, with PDFs generated on the server and scheduled delivery.
And the systems you already run
The agent queries the REST APIs of your ERP, medical records or order system as tools during the conversation — so what it writes into the CRM has already been checked against your source of truth. Hal-AI's versioned public API runs the other way: read customers and messages, send a message, transfer or close a chat, and switch the bot on and off from inside your own system.
Frequently asked questions
What people usually ask about the CRM
Does someone have to type the record in after the conversation ends?
No. The customer record, the history and the tags come out of the conversation itself, generated by the agent from the most recent messages. The manager writes the instruction that guides that reading and edits anything they want on screen.
Can the human team take over a conversation in the middle?
Yes. When a person takes the card, the platform pauses the agent in that chat; once the conversation is closed, the agent starts answering again. A transfer changes the card owner and keeps the record of who said what.
Do WhatsApp, webchat and the phone live in the same place?
The CRM separates WhatsApp conversations, webchat conversations and agent test chats, while the customer history stays unified. Calls handled by voice have their own screen, with the calls active right now and a turn-by-turn transcript of every voice conversation.
Watch your service turn into a customer base
We will walk you through the smart CRM using your channels, your kind of conversation and the systems you already run.