Leads go cold before anyone replies
Enquiries arrive across web forms, email and phone, and get worked in the order somebody happens to notice them.
Solutions · Sales
Most sales teams do not lose deals because the pitch was wrong. They lose them because the enquiry sat in an inbox for two days. We build the intake, qualification and follow-up layer that closes that gap.
The problem
Enquiries arrive across web forms, email and phone, and get worked in the order somebody happens to notice them.
Records are updated from memory on Friday afternoon, so pipeline reporting describes a business that no longer exists.
Research, note-taking, proposal assembly and data entry consume the hours that were meant for conversations.
What we build
Every one of these is described as a business workflow, not an AI feature. Each is scoped, measured and deployed the same way.
Lead intake
Every enquiry captured, structured and logged the moment it arrives — regardless of which channel it came through.
Lead qualification
Enquiries scored against your criteria so the sales team spends its hours on the ones worth pursuing.
Personalized follow-up
Follow-up drafted from real account context and sent on schedule, so nothing goes cold because someone was busy.
CRM updates
Records updated from calls, email and meetings rather than from memory at the end of the week.
Proposal preparation
First-draft proposals assembled from your pricing, past documents and the specifics of the opportunity.
Sales research
Account and contact briefing prepared before the call instead of during it.
Meeting summaries
Decisions, objections and commitments captured and routed to the right record and the right person.
Pipeline reporting
Pipeline movement and risk surfaced on a schedule, without a manager rebuilding the spreadsheet.
Customer reactivation
Dormant accounts identified and re-approached with a reason that is specific to them.
Worked example
A described workflow pattern, not a published client deployment. Scope, systems and measurement are confirmed for your business during the AI Opportunity Roadmap.
Questions
AI can qualify leads accurately when the qualifying criteria are written down and the information needed to judge them is available. It works well for firmographic fit, stated budget, timeline, service match and completeness of enquiry. It works poorly for judgement calls that depend on relationship history a system cannot see. The practical pattern is that AI scores and routes every lead, and a person reviews anything it marks low-confidence.
Not unless you decide it should. In every build we ship, outbound customer messages sit behind a human approval step by default. The AI drafts from real account context and a person approves or edits before anything sends. Teams sometimes remove that gate later for a narrow, well-tested message type such as an acknowledgement, but that is a deliberate decision made after seeing the system's accuracy, not the starting position.
The realistic change is from hours or days down to under a minute, because the automated path does not wait for someone to open an inbox. The size of the gain depends entirely on your current baseline, which is why we measure it before building. A team already replying within ten minutes during business hours will see a much smaller improvement than one where evening and weekend enquiries wait until the next working day.
No. We integrate with the CRM you already use rather than replacing it. The common problem is not that the CRM is wrong, but that it is fed inconsistently by people doing data entry between calls. AI implementation for sales is usually about making the existing CRM accurate and current, not migrating to a new one.
Related
Inquiry classification · Response assistance · Request routing
OperationsWorkflow automation · Document processing · Task routing
Finance & administrationInvoice processing · Expense organization · Document classification
MarketingResearch · Content workflows · Customer segmentation
Get started
Show us where your business is losing time, revenue, capacity or visibility. We will help determine whether AI can solve it — and what the first practical step should be.
hello@askgeeks.ai · Vancouver, BC · remote across Canada & the US