AI Sales Automation — Lead Scoring, Follow-up and Quote Drafting on Autopilot
AI sales automation is a layer on top of your CRM that reads every new lead, scores it across 12-18 signals with GPT-4 or Claude, drafts the first three follow-up emails in your tone of voice, and generates a quote draft from your product catalog in under 90 seconds. The mechanics sit on LLM models running on our own backend, a vector store of your win/loss history, and a pipeline that surfaces at-risk deals before the rep notices. Typical results in B2B SaaS and services: 60-70% fewer junk demo calls, 3x faster quote turnaround and 28% higher quote-to-close conversion.
What the AI layer actually replaces
This is not a pop-up chatbot and not a SaaS subscription. The AI sales automation we build takes on three concrete roles — junior SDR for first-pass qualification, account exec for repetitive follow-up and pre-sales for fast quote drafting. It runs on top of your CRM system or a new one we build for you.
- ✓AI lead scoring across 12-18 signals — industry, company size, email and site activity, ICP fit, prior purchase history — the model returns a 0-100 score plus a 2-3 sentence reason, never a black box.
- ✓Automated follow-up in any language — the LLM writes first, second and third touches that respond to the lead's previous reply, no copy-paste templates.
- ✓AI quote drafts in under 90 seconds — pulls the product catalog, discount matrix and the needs from the call notes and returns a ready PDF for the rep to edit, not write from scratch.
- ✓At-risk deal signals — the AI watches days without activity, tone shifts in email and missed meetings, then surfaces stalled deals to the manager before they slip.
- ✓Conversation summaries and next-step extraction — after every meeting or email thread the model pulls decisions, objections and next action straight into the CRM deal record.
- ✓RAG over your own win/loss data — a vector store of past closed deals helps the model suggest an argument or discount that actually worked, not generic best-practice phrases.
- ✓Full control over model and data — backend, prompts and embeddings are yours; swap GPT-4 for Claude or an open-source model without migration or vendor lock-in.
Who it is for
An SDR team drowning in 200-400 new leads a month and missing the hot accounts. AI lead scoring drops the bottom 40-60% as cold and runs personalized follow-up on the rest. Result: 60-70% fewer junk demo calls and a pipeline forecast the sales director can actually trust.
Manufacturing, engineering services, enterprise software — where one quote takes 2-4 hours to write and often slips by days. AI drafting pulls specs, prices and discounts into a ready document in under 90 seconds. Turnaround drops from 3 days to 4-6 hours.
Stores serving both retail and wholesale buyers. The AI spots B2B inquiries by email or form, prices on the volume tier, generates a pro-forma invoice and queues it for sending. Wholesale buyers get an answer in minutes, not next business day.
How we ship AI sales automation
We do not start with the model. We start with the pipeline — exact stages, exact fields, exact spots where reps lose time. The AI layer attaches where the value is measurable, not where it is fashionable. The whole project rides on business process automation as the foundation.
1. Pipeline and win/loss audit
We review the last 6-12 months of deals, label wins and losses and identify the signals that actually separate hot from cold leads in your industry. The audit usually shows 70% of rep effort going to 30% of leads that never close — the sweet spot for AI lead scoring.
2. Lead scoring model with explainability
We build a hybrid scorer — deterministic rules for hard criteria (geo, size, industry) and an LLM layer for soft signals (email tone, intent specificity, buying readiness). Every score ships with a 2-3 sentence explanation so reps can challenge a 78 versus 42 call when the model is wrong.
3. Follow-up emails and quotes via RAG
The vector store indexes the product catalog, price lists, discount matrix and past won deals. On a new inquiry the model retrieves relevant passages and drafts an email or quote that reads in your voice, not generic ChatGPT output. Every outbound stays in human-approval mode until the team opts into auto-send for specific segments.
4. Integrations and legacy systems
We connect the layer to HubSpot, Pipedrive, Salesforce, Microsoft Dynamics or your custom CRM via official APIs. Email runs over Gmail/Microsoft 365, telephony over Aircall or Twilio, calendar over Google Calendar or Outlook. No copy-paste handoff between AI and CRM.
5. Monitoring, A/B tests and retraining
Post-launch we track conversion per segment, AI-versus-human reply rates, scoring accuracy against actual wins and losses. Monthly we tune prompts and signal weights, add fresh win/loss cases to the vector store and run A/B tests on quote tone and structure. The system improves every quarter without a full retrain.
Why Saitami
Prices are fixed in EUR, no per-seat monthly fee. For wider scope see AI agent for business processes and AI email automation — the two closest extensions of the sales AI layer.
Frequently Asked Questions
How is AI sales automation different from regular sales automation software?
Classic sales automation executes rules — if lead is from industry X, fire sequence Y. The AI layer reads the actual context of the lead, the tone of their reply and the history of similar won deals, then decides dynamically which email to send, when to stop and when to escalate to a human. That is why it works on leads that do not fit any preset segment — which is most serious B2B inquiries.
How accurate is AI lead scoring in the first three months?
With at least 12 months of clean win/loss data, the model usually reaches 78-85% precision on the top 20% of the score from month one. Without history we start rule-based with an LLM explainability layer, then move to a trained model after the first 50-100 closed deals. Reps always see why a lead is scored as it is and can challenge it.
Do AI-written quotes go out without a human in the loop?
By default — no. The AI generates a PDF draft in under 90 seconds; the rep reviews and edits before sending. After 3-6 months, when specific segments show stable quality (typically small standard orders), we enable auto-send with a value cap and discount matrix, so no quote above a threshold ever leaves without human eyes on it.
How much does AI sales automation cost and what are the monthly fees?
From €4,200 for an AI layer over an existing CRM — lead scoring, follow-up generator and AI quote drafting, one language, integration with up to 2 email/phone channels. Full package with multi-language, A/B tests and a telemetry dashboard starts at €6,800. Recurring cost is €80-€350 per month for API tokens and hosting, depending on lead volume.
Can the AI layer plug into an existing HubSpot or Salesforce setup?
Yes. AI sales automation is built as a separate service that talks to the CRM via official APIs. We do not replace HubSpot, Pipedrive or Salesforce — we enrich them. Scores, summaries and quote drafts are written into the standard deal fields, so the team keeps working in the interface they already know.
Ready for an AI sales layer that filters the noise and writes the quotes?
Send a short description of your pipeline, current CRM and monthly lead volume. Within five business days we deliver an AI lead-scoring demo run against your last 100 closed deals so you can see the precision before the project starts.
Request an AI scoring demo →Related services: CRM system for sales · AI agent for business processes · AI chatbot