What you are building
An intelligent customer support layer that operates across your frontline touchpoints: web chat widgets, ticketing queues (Zendesk, Freshdesk, Intercom), customer success dashboards, and internal Slack triage channels. It handles incoming requests with full context:
- "I see you reached out last Tuesday regarding the webhook timeout on endpoint /v2/orders; let's check if the patch resolved that."
- "Since your account upgraded to Enterprise tier yesterday, your rate limit has increased to 10,000 requests per minute."
- "We previously noted your team uses Azure AD for SSO; here are the SAML 2.0 provisioning instructions specific to your setup."
When human representatives step in for high-tier escalations, the assistant presents a crisp, verified timeline of customer facts, preferences, and open issues, eliminating 10 minutes of manual ticket triage.
Why memory decides whether it works
Support bots fail when every ticket starts from zero or pulls stale vector matches:
- Repeating context: Asking customers to restate their OS, plan tier, or past steps kills trust immediately.
- Stale plan data: After an account upgrades to Enterprise, vector search still pulls old Free-tier notes and quotes the wrong limits.
- Siloed channels: Live chat, email tickets, and Stripe billing events never connect into one timeline.
- Policy hallucinations: Without grounded facts, bots guess on SLAs and refund rules.
What / ml provides
One Timeline Per Customer
Combine Zendesk tickets, Intercom chats, Stripe plan changes, and CRM notes under a single customer ID.
Always Up-to-Date Plan Info
When a customer upgrades their plan or changes their setup, / ml updates their profile so your bot never quotes old limits.
Fast ~10 ms Lookup
Pull a customer's account details and open issues in about 10 ms so live chat replies feel instant.
Ticket Links for Human Agents
Every fact links back to the ticket number and date, giving human agents a quick summary when a conversation escalates.
Cost and token economics
Support teams handle thousands of tickets a day. Pasting a customer's entire ticket history into every reply drives up model costs and slows down chat.
Tokens Used Per Support Reply
Pasting full ticket history: ~8,200 tokens ($0.041 per turn)
/ ml relevant customer facts: ~1,150 tokens ($0.0057 per turn)
Result: 86% lower token cost and 3.8× faster replies.
For a team handling 100,000 tickets a month (3 turns per ticket), / ml saves over $10,500 a month in model costs.
Getting there
Save ticket notes when issues close
Send resolved ticket summaries and setup details to / ml under the customer's ID.
Sync plan upgrades from billing
Forward plan changes from Stripe or your billing system to / ml so account tier and limits stay current.
Look up customer history when a chat opens
When a customer starts a chat or ticket, search / ml with their ID to pull their plan details and past fixes right away.
Pass the facts into your support prompt
Include those dated customer facts and ticket numbers in your prompt so the bot or human agent can reply with full context.
Code example
Ingesting customer tier entitlements and recalling context during live support with the Python SDK:
import os
from slashml import Memory
mem = Memory(api_key=os.environ["SLASHML_API_KEY"])
# 1. Ingest an entitlement upgrade event from billing
mem.add(
"Account upgraded from Growth to Enterprise plan on 2026-10-20. SLA response time: 1 hour. Dedicated SAML SSO active.",
user_id="customer-corp-912",
metadata={"source": "stripe-webhook", "plan": "enterprise", "updated_at": "2026-10-20"}
)
# 2. Ingest a resolved support ticket note
mem.add(
"Fixed webhook timeout on /v2/orders by increasing gateway read deadline to 15 seconds.",
user_id="customer-corp-912",
metadata={"ticket_id": "ZD-84920", "agent": "sarah-support", "updated_at": "2026-10-22"}
)
# 3. Retrieve verified customer memory when user opens new chat
context = mem.search(
"Customer reports slow webhook responses on API endpoints",
user_id="customer-corp-912"
)
for item in context["results"]:
meta = item.get("metadata") or {}
print(f"Memory: {item['memory']}")
print(f"Source: {meta.get('source') or meta.get('ticket_id')} ({meta.get('updated_at')})")Frequently asked questions
Q How does / ml handle customer privacy and GDPR deletion requests?
When a customer asks to delete their data, a single API call permanently removes all of their memories and records in 6.41 ms.
Q Can human agents see which facts the AI used?
Yes. Every memory comes back with the original ticket ID, date, and source so human agents can verify the context at a glance.
Q What if a customer asks about two different issues in one ticket?
/ ml separates conversation notes into individual facts with tags, so billing questions and technical bugs stay organized and easy to search on their own.
Q Does / ml work with our existing helpdesk and CRM?
Yes. You can send events from Zendesk, Intercom, Salesforce, or Stripe straight to / ml using our Python SDK, TypeScript SDK, or REST API.