01
Sales and inbound leads
Qualifies leads by budget, task and timeline. Logs everything into the CRM itself. Tags the manager.
I need an agent like this →AI & LLM Development · 2026We implement AI agents end-to-end
We implement AI agents end-to-end
for B2B processes
We review the process, build a pilot, and connect the agent to CRM, messengers, and your knowledge base. We start where routine work already creates visible losses.
0
Process
0
Launch steps
0
FAQ answers
0/7
Support
Generating response
lead qualifiedBitrix CRM
content draftready for review
support ticketrouted
document parseddata extracted
reminder sentTelegram
RAG · context
3 sources found
Vector DB
1.2M · 38ms
AI agents at work · 24/7
lead qualified· Bitrix CRMcontent draft· ready for reviewsupport ticket· routeddocument parsed· data extractedreminder sent· Telegramreport generated· Sheetstranslation done· editor reviewhandoff created· manager notifiedlead qualified· Bitrix CRMcontent draft· ready for reviewsupport ticket· routeddocument parsed· data extractedreminder sent· Telegramreport generated· Sheetstranslation done· editor reviewhandoff created· manager notified
01 / What we automate
Processes an AI agent can take over
AI agents work best in repeatable scenarios: leads, chats, CRM updates, documents, knowledge bases, campaigns, and content workflows.
02
CRM and daily operations
Fills in records, updates deal stages, collects contacts. Requests stop getting lost outside business hours.
I need an agent like this →
03
Content, SEO and social
Gathers material → writes in your brand tone of voice → edits → plans a month of publications.
I need an agent like this →
04
Assistant for the team
Joins calls and takes notes, finds answers in the knowledge base, parses documents and answers questions.
I need an agent like this →02 / Who it fits
Who benefits from an AI agent
- There is a repeatable process: leads, CRM, documents, content or support.
- The result is measurable: response time, errors, hours, deals in CRM.
- You are ready to start with one pilot instead of rebuilding the whole company.
- There is no concrete process — only a wish to “add AI”.
- There is no data, no guidelines and no examples of real tasks.
- You expect AI to fix your product, your sales or your business model.
Not sure? That is normal — the process review is exactly what answers this question.
03 / Process
How we work
A transparent path from the first call to launch and support
Typical agent pipeline
- INInbound requestWebhook / Input
- AIQualification / SearchLLM / DeepSearch
- CRMProcessing / SaveCRM / RAG
- OUTAuto-reply / OutputAgent / LLM
- OKReady resultManager / Publish
- I
We review the process
We look at what happens today: where the manual work is, which step loses time, who owns the result.
- II
We pick a scenario for the pilot
We take one narrow process with a clear result that can be validated in a couple of weeks.
- III
We build the prototype
We create the agent and define its logic, dialogue scenarios, knowledge base and the integrations it needs.
- IV
We put it into production
CRM, messengers, spreadsheets, CMS — wherever the team already works, with no migration to new systems.
- V
We test on real data
We check the agent's answers, look for mistakes and tune the handover of complex cases to a human.
- VI
We launch and support
We track metrics after launch, refine scenarios and expand to new processes.
04 / Timing and budget
Estimate routine workload
Drag the sliders for a rough estimate. The final calculation comes after reviewing the real process and data.
Employees on routine6
Routine hours per week18
Cost per hour, $1,000 ₽
Hours freed per month
351h
Monthly savings
351,000 ₽
Yearly savings
4,212,000 ₽
* A rough estimate from your inputs. Final calculation comes after process review.
Calculation after review
How much an AI-agent implementation costs
Cost depends on integrations, data quality, and the number of scenarios. We give a realistic range only after reviewing the process.
After the review, the project usually falls into one of three levels: a simple pilot, a pilot with integrations, or a full implementation.
ROI is checked on the pilot: we measure current workload, errors, and response time, then compare them after launch.
Message us — we will review the process, check the inputs, and estimate the range for your scenario: integrations, data, owner, and pilot metrics.
Ask for free05 / Cases
Real projects, practical scenarios
Before → what we built → result: leads, content, and operational routine.
More detailed cases are in the Telegram channel
We publish longer breakdowns with process, constraints, numbers, and business takeaways.
Telegram channel
06 / Stack & Security
Integrations, data, and access control
CRM and systems
- Bitrix24 and amoCRM
- Google Sheets and Forms
- Internal systems via API
Communication channels
- Telegram, WhatsApp, VK
- Website forms and chats
- Handover of complex cases to a human
Knowledge bases
- Notion, Google Docs, Confluence
- Guidelines and knowledge base
- Knowledge updates after the pilot
Security
- NDA before the review
- Least-privilege access
- Logs of the agent's actions
- Role-based permissions
07 / Team
A team of experts
Professionals with years of experience in AI and machine learning
Artem
CEO & AI Architect
Yulia
Lead ML Engineer
Nastia
AI Product Manager
07 / Reviews
Success stories
“Working with Velmi.ai is a pleasure. They understood the task fast, proposed the best solution and shipped in 3 weeks. Modern stack, clean code.”
Igor Volkov
CTO, StartupHub
“The AI Sales Bot fixed our sales bottleneck. Leads are handled instantly, managers focus only on hot deals.”
Elena Krylova
Head of Sales, RetailPro
“Content automation gave us multiplied output without growing the team. It paid off in the first month.”
Dmitry Morozov
Founder, MediaFlow
08 / FAQ
Frequently asked questions
With a review of one process. We look at where time or leads are lost and propose a scenario for a pilot.
A short description of the process, examples of requests, conversations, documents or CRM fields, and a person who can quickly review the agent's answers during the pilot.
A chatbot follows a predefined script and handles deviations poorly. An agent qualifies the enquiry, decides based on context, updates the CRM and hands complex cases to a human on its own.
It depends on the number of integrations and the quality of the source data. We give an exact timeline after the process review — before it, any number would be inaccurate.
We work under NDA. Access, conversations and documents are used only to configure your scenario. During the process review we explicitly agree which data may be passed to the agent and which must stay with a human.
We measure it on the pilot: how much time an operation takes now and how much is freed up after the agent goes live. A concrete number you can verify after the pilot.
Ready to automate your business with AI?
Tell us about your project and we’ll propose a solution that scales processes without losing quality