Our cases

Initial situation

The institute held an exclusive knowledge base locked away in printed editions. Manually searching a 30,000-page corpus cost experts hundreds of working hours every month. Access for external researchers and students was limited by complex terminology and a high entry barrier. Linear scaling of the consulting model was impossible.

What we did

We deployed an AI infrastructure to digitise and semantically analyse the entire corpus, building a vector knowledge base on a RAG architecture. We fine-tuned an LLM on the source’s specific terminology and semantic links. For data confidentiality the solution was deployed in the client’s local loop (on-premise) and then scaled to the cloud in a hybrid model. We launched a web platform, personal cabinets for experts and an interactive discussion panel that automatically enriches the vector base from verified discussions.

Result and economic effect

• Over 30,000 pages processed and available through instant semantic search; more than 50,000 pages of answers generated with precise references to the original sources.
• Thousands of scholar-hours saved, and a community of more than 300 active researchers formed around previously closed expertise.
• At the intersection of AI infrastructure and an academic knowledge base, a new scientific discipline took shape.

Initial situation

The company used third-party software with a built-in AI module that had a number of critical limitations: slow query processing, per-module pricing, hard vendor lock-in and a high cost of ownership (up to 10,000 ₽/month per workstation). As the team scaled, infrastructure costs grew linearly, blocking the launch of free social services.

What we did

We built an isolated vector knowledge base of Russian legislation, integrating the current statutory acts (Labour Code, Civil Code, Criminal Code) into a single RAG system with answer verification down to articles and clauses of the codes. We created a specialised Telegram AI assistant that generates answers twice as fast as market analogues. We launched an intelligent first-consultation service: the system handles typical requests, qualifies leads and routes complex cases to specialist lawyers.

Result and economic effect

• Technological independence and OpEx reduction of up to 80–85%: a full move to a sovereign technology stack and an in-house IT loop. Independence from the external vendor removed the need to buy add-on modules.
• Optimised cost of ownership: replacing third-party licences removed the barrier of linearly growing software costs as the team expands. Example model: for a team of 20 specialists, savings on external software can reach up to 2.4M ₽ per year.
• Analytical processes accelerated about 2× on average: instant semantic search across legislation saves up to 30–40 hours per month per specialist.
• Sales-funnel automation: round-the-clock AI qualification of inbound traffic (24/7) with seamless routing of target clients to a deal.

Initial situation

A specialised university in no-code and AI development is building a large ecosystem of specialists. The regional unit’s task: launch a sustainable offline community for students and alumni, raise retention and LTV, automate mentor matching and strengthen brand loyalty without direct advertising.

What we did

We built a single digital hub platform to coordinate offline activities (schedule, registration, format analytics) as the core of social marketing. We integrated a Telegram AI agent operating on a vector base of member profiles (stack, grade, career goals): automatic selection of learning tracks, mentor matching and targeted invitations of residents to relevant events. We organised a series of expert masterclasses and career sessions.

Result and economic effect

• LTV growth of up to 34%: engaging the audience in a long-term community ecosystem increased the frequency of buying advanced and adjacent education programs (repeat sales).
• CAC reduction of up to 28%: achieved through the viral effect within the community and organic brand integrations into adjacent niche communities.
• Conversion to sale up to 14.5%: on average, every seventh user who registered in the AI agent’s vector base became a paying student.
• Brand effect: a steady organic inflow of audience and the university’s integration into expert communities across digital, mass media, telecom and AI development.

Initial situation

The centre was scaling a scientific methodology of systemic bioregulation of the body. It needed to automate the creation of individual prescriptions (nutrients, phyto-extracts, diet) based on genetic maps, ensure full data confidentiality per regulatory requirements and offload curating physicians, while turning the IT product into a standalone high-margin segment.

What we did

We built a personal AI agent on a vector model with verified medical protocols and a base of biophysical parameters of nutrients and nutraceuticals. We implemented an algorithm that automatically computes personalised recommendations: the AI analyses genetic polymorphisms and metabolic pathways, builds a map of bioregulatory sensitivity and determines precise dosages. The architecture was deployed on a local server inside the centre’s protected IT loop.

Result and economic effect

• Partner-network sales growth of up to 24%: driven by instant multilingual support from AI assistants and aligned consultation-quality standards across 20+ countries of presence.
• New-partner onboarding accelerated up to 2.5×: an automated AI school shortened the training cycle and brought distributors to their first deals faster.
• International-support costs reduced by up to 35%: thanks to automatic handling of up to 80% of routine and first-line requests by tier-1 AI agents.
• Capitalisation of expertise: the average check grew up to 2×, a move into the premium B2C segment and the launch of a separate digital business on a subscription model.

Initial situation

A large industrial holding selling high-tech equipment faced decentralised training standards, uneven consulting quality across its partner network and the absence of a unified digital support ecosystem. It needed a comprehensive “AI-first” strategy, the architecture of a corporate online school and a solution for local AI compute (edge computing) at physical sites.

What we did

We ran a series of strategic sessions on integrating AI into the company’s loop. We designed a hybrid IT architecture: local compute for regional distribution centres and cloud synchronisation for global analytics. We designed the technical documentation and roadmaps for the AI learning platform (web, mobile apps, CRM integration, predictive reporting). Based on systems cybernetics we ran a model-security audit and load testing (pentest). We planned automated content factories.

Result and economic effect

• Direct IT-integration budget optimisation: cost savings at the design and architectural-planning stage exceeded 10M ₽.
• Content Production costs reduced by up to 42%: through automated generation of technical manuals and multilingual training modules on an AI content factory.
• Partner satisfaction index (CSI) up to +18%: by cutting support response time from several hours to several seconds.
• Time-to-Market for new products cut by up to 30%: a unified knowledge-distribution system rolled out equipment-service rules across the entire international network at once.

Initial situation

The client faced significant infrastructure barriers: the high cost of renting cloud GPU compute for AI, the lack of flexible financial instruments (instalments) to acquire enterprise-grade hardware, and a shortage of qualified technical support — creating a risk of missing digitalisation deadlines.

What we did

We audited the IT infrastructure and built an AI-transformation roadmap tailored to the construction sector. We arranged direct delivery of specialised server hardware from our warehouse in China, offering flexible financial terms (targeted instalments against collateral) and comprehensive technical support. We installed the system software and deployed an MLOps platform that lets the client’s team autonomously train, test and scale local models.

Result and economic effect

• GPU-infrastructure costs reduced by up to 56%: replacing constant cloud rental with an in-house on-premise loop optimised the CAPEX/OpEx balance, with hardware payback within 14 months.
• Time-to-Deploy for new models accelerated up to 4×: the local MLOps loop let the team deploy and test predictive scenarios (estimate analysis, photo-based defect control, special-equipment logistics optimisation) in days instead of weeks.
• Up to 20% budget savings on management and design: automating routine operations of the technical and project departments reduced payroll costs and removed human error in document verification.