Loading
Loading
A pipeline that answers every inbound inquiry quickly, screens for fit, books discovery calls without email ping-pong, keeps proposal follow-up on schedule, and turns each finished engagement into searchable knowledge the next proposal can draw on.
Solution blueprint: a system Callimachus Forge can architect for your operation. Not a case study, and not a claim of past deployment.
At a glance
Stack candidates
n8n · GoHighLevel · Calendly · Google Calendar · Gmail · Slack · Airtable · Vector database
System run
A simulated run of the exact steps documented on this page, from first trigger to final handoff. Nothing here is live client activity.
Simulation · Loops
The problem
Consulting firms sell expertise, but the selling itself gets squeezed between billable work. An inquiry comes in through the website while the partners are with clients, and it waits days for a reply. Scheduling a discovery call takes days of back-and-forth email. A proposal goes out, and follow-up depends on whoever remembers. Meanwhile the firm's real asset, what it learned on past engagements, sits in old decks and individual memories, so every new proposal starts from a blank page. None of this work is hard. It is constant, it competes with paid work, and so it slips.
This blueprint assumes a small to mid-sized consulting firm: a few partners who both sell and deliver, perhaps an operations manager, no dedicated sales team. Inquiries arrive through a website form, referral emails, and LinkedIn messages forwarded to a shared inbox. The pipeline lives in a spreadsheet or a lightly used CRM. Proposals are written in Google Docs from whichever past proposal someone can find. Finished engagement materials sit on a shared drive organized by client name, which means they are findable only if you already know what you are looking for.
The manual process
The automation opportunity
The system watches every channel where inquiries arrive and makes sure each one gets a prompt reply. It reads the inquiry, compares it to the work the firm wants, and sorts it: good fits get a warm response with a booking link, unclear ones go to a partner with a suggested reply, and poor fits get a courteous decline that a partner approves first. Before each discovery call it hands the partner a short brief, including similar projects the firm has done. After a proposal goes out it runs polite follow-ups on schedule, stopping the moment the prospect replies. When a project closes it collects a short debrief from the delivery lead and files it where the next proposal can find it.
Architecture
Every step is typed: triggers, AI decision points, business logic, actions, data, and the human checkpoints that keep judgment where it belongs.
AI components
Human checkpoints
A website form, messages to an intake email alias, and forwarded referrals all land in one n8n workflow through webhooks and mailbox polling, so every inquiry enters the same pipeline regardless of channel.
n8n checks for an existing contact, then creates or updates the contact and opportunity in GoHighLevel with the raw inquiry text, source channel, and timestamp attached.
A language model reads the inquiry, extracts service line, company context, urgency, and budget signals, scores fit against the firm's written ideal-client profile, and drafts a summary plus a suggested reply.
Rules branch on fit score and model confidence: strong fits move to scheduling, borderline or low-confidence cases queue for partner review, and clear mismatches get a drafted decline held for approval.
Borderline inquiries, declines, and any reply the model marks uncertain appear in a Slack channel with the summary and draft. A partner approves, edits, or reroutes; nothing uncertain sends on its own.
Approved fits get an email with a Calendly link. GoHighLevel sends call reminders by email and SMS, the booking lands on the partner's Google Calendar, and the opportunity stage advances in the pipeline.
Before each discovery call, the model queries a vector database of past engagement summaries and assembles a brief: prospect context, similar past work, and suggested questions, delivered to the partner in Slack.
When a proposal is marked sent in the CRM, n8n schedules a spaced follow-up sequence. Each message is drafted by the model and staged for one-click approval, and the sequence halts when the prospect replies.
When an engagement closes, the delivery lead answers a short debrief form. The model condenses the answers and deliverable summaries into an engagement profile, embedded in the vector database and logged in Airtable.
A recurring digest goes to Slack and email: new inquiries by status, calls booked, proposals awaiting follow-up, deals gone quiet, and new engagement profiles added to the knowledge base.
Engineering notes
Benefits are qualitative by design. We do not publish numbers we cannot verify.
The free audit maps your actual tools and process against this architecture, and tells you honestly what is worth building.