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AI Assistant

A GPT agent that answers customers and employees in Slack, Teams, on your website or in your CRM. It learns from your data, cites sources, and hands complex cases over to a human operator.

Request a demo → View case studies
3–4 wksto pilot launch
−42%support workload
24/7operation without staff
assistant architecture
inbound
Website · Telegram · WhatsApp · Email · CRM
↓ router
classification · intent extraction
↓ context
RAG over the knowledge base + dialogue history
↓ tools
CRM API · 1C · Email · calendar
↓ response
reply to the customer / escalation to an operator
the problem

Why ordinary chatbots fall short

Scripted if-else bots frustrate customers and never close the task. Staffing a second contact center is expensive. Plain ChatGPT doesn't know your knowledge base, can't work with your CRM, and hallucinates on specifics.

our answer

An agent, not a chatbot

The Aisol AI Assistant is built as an agent: it holds context, reaches into your systems through tools, cites sources from the knowledge base, and escalates complex cases to a human. Support code is kept to a minimum, and it learns from your documents.

how we deploy

Three stages — from first call to production

01 · WEEK 1

Audit

We analyze 100 typical requests, label intents, and identify the ones the agent will handle.

02 · WEEKS 2–3

Prototype

We build the agent, connect your knowledge base and one system (CRM or helpdesk), and measure accuracy.

03 · WEEK 4+

Pilot and production

We route 10% of traffic to the agent, watch the metrics, then scale up to 100%.

scenarios

Where our clients use it

support

24/7 first-line support

Handles routine questions and passes complex ones to an operator. Removes 40–60% of the workload.

−42%requests reaching an operator
sales

Lead qualification

Clarifies the need, budget, and timeline, then hands a warm lead to a manager.

+28%conversion to a call
HR

Internal helper

Answers questions about policies, leave, and company life. Takes routine work off the HR team.

−70%tickets to HR
e-commerce

Catalog and delivery

Recommends products, answers delivery questions, and processes returns. Understands the dialogue context.

+18%average order value
b2b

Technical consultations

Answers questions on specifications, compatibility, and reorders. Cites the documentation.

−55%response time
field service

Technician's assistant

Prompts through instructions by voice and generates field-visit reports. Works from a phone.

−30%time spent on reporting
technology

A stack for your infrastructure

Cloud or on-premise — your choice. We support operation without data leaving your perimeter: self-hosted models on your servers, local vector databases.

GPT-4o Claude 3.5 Sonnet LLaMA 3 · on-prem DeepSeek R1 YandexGPT Qdrant pgvector LangChain LangGraph FastAPI 1C · OData Bitrix24 amoCRM Slack / Teams API
relevant case studies

Who has already deployed an AI Assistant

All case studies →
CH

Construction holding

construction · in progress

An internal assistant for project documentation serving site managers and engineers: access to contracts and drawings with source citations.

RB

Retail brand

retail · in progress

An AI assistant in e-commerce: product selection, delivery status, return processing. Telegram + website + WhatsApp.

FAQ

Questions about the AI Assistant

Off-the-shelf platforms mean generic settings and vendor lock-in. We build a custom agent around your processes and systems, with the option of on-premise deployment. Over the long run the cost is lower because there are no per-seat licenses.
Every answer cites its source, so the operator and the customer can see what it's based on. During the pilot, all answers are mirrored to a review channel for quality control. We measure accuracy on a labeled sample.
Yes. We use self-hosted LLaMA 3 / Qwen / DeepSeek inside your perimeter, with a vector database on your servers. No external APIs are used.
A knowledge base (PDF, DOCX, Confluence, website) and 100–200 examples of real conversations are enough. We connect additional systems (CRM, 1C) as needed.
A platform subscription starts at 12 million ₸ per year. The first deployment opens up an AI layer across the company and, by our estimates, pays for itself within 6–9 months; each additional process is configured on the existing layer — cheaper and faster. We calculate the exact configuration and plan during a free audit.
request a project

Get a prototype AI Assistant in 3 weeks

Tell us about your task — we'll send a working assistant example built on your data, along with an estimate.

  • A 1-hour call with an architect and a product manager
  • A free process audit (5 business days)
  • A brief: metrics, cost, timeline, risks
  • NDA — signed on the first call

We'll reply within one business day. Anything under NDA — we'll discuss on the call.