AI CRM: A System That Qualifies Leads and Personalizes Communication

A Problem Everyone Recognizes

A service company with 11 sales specialists was paying €539 per month for a popular CRM platform. The problem? 80% of features went unused. But the 20% they needed weren’t adapted to their specific business processes.

Meanwhile, the team was losing 65% of new clients — simply because they couldn’t respond fast enough. Average first response time was 4–24 hours. Clients didn’t wait.

The biggest irony — the company already had an AI sales engine (7,000 lines of code), but it lived in a separate project and couldn’t properly integrate with CRM data.

The Solution: AI at the Core, Not Bolted On

We didn’t build “yet another CRM with an AI chatbot.” We built an AI-first CRM — a system where artificial intelligence is the architectural foundation, not an add-on.

The Autonomous Sales Sequence

The system’s heart is Sales AI V2 — an autonomous conversation state machine with 13 steps. Each new inquiry is automatically classified:

HV (High Value, ≥80 points) — AI runs the full sales cycle: qualifying → proposal → feedback → closing
AS (Auto-Sequence, 20–79) — automated follow-up sequence with personalized messages
NF (No Follow, <20) — minimal engagement, resource conservation

7-Factor Scoring Model

Each inquiry receives a score from 0 to 100 based on:

Client type (repeat vs. new) — 25 points
Inquiry signal (intent) — 20 points
Traffic source — 20 points
Deal value — 10 points
Seasonality — 10 points
Purchase history (LTV) — 15 points

5 Communication Personalities

Each of the 5 sales specialists receives AI-generated text in their own tone and style. The system knows that one agent writes formally and in detail, while another keeps it short and direct. AI adapts to each.

Canvas/Chat Interface

For email improvement, we built a three-panel interface inspired by Claude Desktop. Left panel — the original text. Center — AI’s suggested version. Right — conversation history with the ability to iterate.

Migration: 11,557 Deals, 10 Pipelines, 38 Stages

One of the most complex steps — migrating data from the old CRM. 4 years of data with GDPR retention rules:

11,557 deals
10 pipelines with 38 individual stages
Contacts, organizations, notes, activities

During development, we nearly exceeded the API limit (660,000 requests per day) — this created a safety protocol where any large-scale operation requires separate approval.

Email Integration with Security in Mind

Full email system: IMAP sync for incoming, SMTP for sending, automatic linking to deals and contacts. Passwords encrypted with AES-256-GCM — the same standard used in banking systems.

Email HTML preview requires DOMPurify sanitization or iframe sandboxing — otherwise XSS attacks through malicious emails are a real risk.

Results

Metric	Before	With AI CRM
CRM costs	€539/mo (off-the-shelf)	~€40/mo (custom system)
First response	4–24h	Target: minutes
Clients without response	Significant portion	Target: much less
Feature fit	~20% actually used	Everything tailored to needs

What We Learned

TypeScript significantly improves code structure. When porting the Sales AI engine from JavaScript to TypeScript, code structure improved dramatically. Type safety isn’t just about preventing bugs — it forces clearer thinking.

AI-first ≠ AI-only. Humans still make the final decision. AI prepares, recommends, automates routine. But “send the proposal” and “close the deal” — that’s human responsibility.

Migration is underestimated. 60% of project time went to data migration and integration, not new functionality. That’s normal — and it needs to be planned for.

The system is currently in UAT — running in parallel with the old CRM to validate results in a real environment.

Is your CRM doing what you need? Tell us about your situation — we’ll help you understand the possibilities.