AI for sales

An AI assistant for your sales team

An assistant that searches the catalogue, drafts quotes, suggests arguments to the sales rep and works inside the channels they already use: CRM, messengers, the internal portal.

Discuss a project Common questions

Stack and integrations

Python FastAPI Qdrant PostgreSQL vLLM local LLMs

In short

What we build: An AI assistant for a B2B sales team: catalogue search, draft quotes, CRM integration.
Who for: Companies with 500 SKUs or more, where reps lose hours to searching and quote preparation.
Stack: Python, FastAPI, Qdrant, a hosted API or a local LLM (Mistral, Qwen).
Timeline: MVP in four to eight weeks, production in three to four months.

A good fit when

→ The catalogue is large and reps spend a long time finding items and alternatives
→ Quotes are assembled by hand from spreadsheets, PDFs, the accounting system and old emails
→ You need answers that cite sources, not confident text
→ Commercial data must not go to an external AI service

What you get

→ RAG search across the catalogue, knowledge base, past quotes and technical documentation
→ Draft quotes and emails with sources and verifiable links
→ Integration with your CRM, accounting system, messengers or internal portal
→ Answer logs, access roles, human fallback and quality evaluation

Process

01 Discovery: we go through the catalogue, the typical questions and the data sources
02 Prototype: retrieval plus the first quote scenario on real documents
03 Production: roles, monitoring, security, training for the sales team

Why not an off-the-shelf product

Custom development earns its place where your processes matter.

Ready-made SaaS is good for standard scenarios. But once the business logic depends on specific roles, documents, integrations, security or data, the cost of the workarounds quickly exceeds the cost of a proper architecture.

We start with discovery, separating what genuinely has to be built from what is cheaper to cover with an existing service. That is why the project ends up smaller, clearer and easier to run.

Related cases

Common questions

Can we avoid sending data to hosted LLMs?

Yes. For sensitive data we deploy the model and the vector database inside your perimeter.

How long does an MVP take?

Usually four to eight weeks, given access to the catalogue, sample quotes and twenty to fifty typical questions from the reps.

How do we control AI mistakes?

Answers are built with source citations, every query is logged, and critical fields are checked before anything goes to a customer.

What does a full project cost?

We quote in roubles and give a figure after the discovery call. The range depends on catalogue size, the number of integrations and the security requirements.

Is there a real deployment behind this?

Yes: a 12,000-SKU catalogue on a local LLM, where quote preparation went from two hours to twelve minutes. The write-up is on the cases page.

Is there an off-the-shelf product instead of building?

Not for a B2B catalogue with a local LLM and your own role model. Ready-made wrappers run on public LLMs and do not suit sensitive data.

How do we measure the effect?

Three metrics: time spent on search and quotes (down 60–90%), lead-to-deal conversion, revenue per rep. We calculate the return in the first three months after launch.

Who owns the source code after release?

You do, when the contract says so, which is our standard arrangement. Full rights to code and models transfer after final payment.

Written up in more detail

Next step

Let us go through your problem.

We will show you a possible architecture, the risks, the order of the budget and what an MVP could prove.

Discuss a project