“Anna” — the AI manager that became part of the call-center team
TermoLand stopped losing after-hours enquiries and reduced pressure on operators. Anna now handles 17,000 messages a month inside the CRM, follows leads through the funnel, and escalates only when human judgment is needed.
Discuss a similar systemThe enquiry volume had outgrown a fully manual call center
Every peak and shift change created another opportunity to lose a high-intent customer.
We embedded an AI colleague, not a website widget
Anna works in TermoLand’s CRM on the same conversation timeline as the human team. That single design decision makes hand-offs seamless and keeps AI accountable to the operating process.
CRM-native operation
AI and human messages live in one system. When Anna escalates, the operator sees the full customer history.
Multi-model architecture
Smaller models classify and route requests; larger models handle customer-facing answers, balancing speed, cost, and quality.
Funnel follow-up
Anna does more than answer questions: she reminds, motivates, and moves the lead toward a purchase decision.
Honest escalation
When confidence is low, AI brings in a manager instead of inventing an answer and putting the brand at risk.
Quality-control loop
Operators intercept questionable answers using explicit rules, and real cases continuously improve the system.
Natural-language service
Customers write naturally. There are no rigid menus and no “press 1 to continue” interaction model.
What changed for the operating team
“We used to drown in call-center turnover and missed enquiries — and lose customers. Now Anna, our AI bot, handles it quickly, accurately, and without fatigue.”
Alexander Polkovnikov · CPO, TermoLandSix months of production operation
Figures taken from TermoLand’s CRM after the AI manager went live.
AI-led follow-up and purchase motivation in the live workflow.
Response comparison during peak call-center hours.
What makes customer-facing AI reliable in production
Design for errors, not perfection
At volume, every model will miss something. Reliability comes from interception rules and a human quality loop, not a mythical perfect prompt.
Use different models for different jobs
Classification and routing do not need the same model as customer-facing reasoning. Matching model cost to task protects both economics and quality.
Make uncertainty visible
A clean hand-off is cheaper than a confident but incorrect answer. Escalation is a feature, not a failure.
Put AI inside the team’s system of record
When the agent works inside the CRM, governance, context, and ownership stay intact.
Anna 2.0 — from service agent to revenue operator
The next stage gives Anna the ability to initiate payment processes directly in the CRM. That moves the agent from answering and follow-up to completing the commercial workflow, while the call center retains control over non-standard and sensitive cases.
Could your call center benefit from a 24/7 AI colleague?
We will map the workflow, identify safe automation boundaries, and design the agent around your CRM and operating rules.
Discuss your use case