Lead follow-up · SaaStr
Increase revenue.
Find more opportunities in every customer conversation.
The business
A media and events business for software founders, run by three staff.
The problem
SaaStr had about 1,000 warm leads: founders asking about sponsorships, tickets and partnerships. With a team of three focused on large sponsorship deals, nobody had time to follow up. The leads sat untouched for months, and the sales they could have brought in were going cold.
How it worked
An AI agent took on the follow-up the team had no time for. It contacted every lead, kept the conversation going, and handed back to the team only the leads that showed real interest.
Existing tools
Use what the CRM already knows
Each lead’s history with SaaStr.
AI
Write personalised outreach
Tailored to each lead.
AI
Follow up and spot interest
Within limits the team set.
People
Take over warm leads
Alerted when a valuable lead replies.
Reported results
$2.7M
extra revenue closed
72%
of emails opened
3 weeks
from idea to live
Source: Salesforce customer story (2026). Results as reported.
Document processing · Rocket Close
Improve efficiency.
Move requests forward with less manual checking and back-and-forth.
The business
A US mortgage services company that reviews loan documents.
The problem
About 2,000 document packages arrive every day, averaging 75 pages each. Staff had to read every page, work out what each document was and type the key details in by hand: around 1,000 hours of work a day. The process was slow, costly to scale and open to human error.
How it worked
AI now reads each package, recognises the document types and pulls out the key details automatically, cutting processing from 30 minutes to under 2 minutes per package.
Existing tools
Receive the document package
Scanned, as today.
AI
Read every page
Including tables and forms.
AI
Sort and extract
60+ document types, one format.
People
Check the accuracy
Against verified answers.
Reported results
15×
faster: 30 minutes to under 2
~90%
accuracy on 44,000+ fields
Source: AWS Machine Learning Blog (April 2026). Proof of concept; results as reported.
Invoice checking · Air India
Save time and reduce costs.
Reduce repetitive data entry, document preparation and rework.
The business
An airline paying suppliers for hotels, airport services and catering.
The problem
Invoices arrive from many suppliers: hotels, airport charges, ground handling, transport and catering. Each one had to be checked by hand and reconciled against vendor and bank records. The work was slow, errors slipped through, and the finance team was stretched.
How it worked
AI now checks each invoice against contracts and business rules and reconciles the accounts in real time, so the team only deals with the exceptions.
Existing tools
Receive supplier invoices
Hotels, airport charges, catering.
AI
Check each invoice
Against contracts and rules.
AI
Reconcile accounts
Vendor and bank records matched.
People
Team handles exceptions
Anything the AI can’t match.
Reported results
60%
lower operating costs
70%
less time checking invoices
3×
team productivity
Source: Supervity case study. Results as reported.
Disclaimer: These are published case studies from other companies, not KKhai projects. Results are as reported by each company and its technology partner; sources are linked under each example. Your results will depend on your starting point. Before any build, we agree the measures with you and record where you are today.
Let’s talk.
The first conversation is free.
Recognise one of these problems?
Tell us about it. We’ll talk it through and tell you plainly whether AI is the right fix.
hello@k-khai.com