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We design AI systems for teams that work with voice, text and data at scale.
We help companies design, build and evolve AI systems — whether that means creating something from scratch, integrating proven solutions or strengthening an existing product.
What we do
BUILD AI SYSTEMS TAILORED FOR YOUR BUSINESS
We build systems that automate routine work, surface the right information faster and support better decision-making across teams.
If you’re dealing with manual processes, large volumes of unstructured data or a growing operational load, custom AI can help you scale without increasing headcount.
We design and develop each system end-to-end, making sure it fits naturally into your workflow and delivers measurable operational impact.
We design and develop each system end-to-end, making sure it fits naturally into your workflow and delivers measurable operational impact.
INTEGRATE EXISTING AI SOLUTIONS
We integrate AI solutions that fit your needs. If there’s already a tool that fits your need — whether it’s a partner product or a proven off-the-shelf solution — we help you choose it.
We help you choose the right tools and adapt them to your data, infrastructure and workflows, reducing time to value without locking you into a single approach. We take care of integration, customisation and deployment, so the system works as a natural part of your operations.
STEP IN AS A TECHNICAL AND GO-TO-MARKET PARTNER
We refine the product, add missing AI capabilities and help shape it into something that solves real business problems.
We also support the commercial side. Our team joins demos, talks directly to potential clients, helps position the product and works with companies to make sure the solution lands well inside their operations.
Voice sentiment analysis for call quality
2025
Real-estate price forecasting
2025
NDA
Let’s talk about your case
2025
2024
If you’re exploring an idea or trying to understand whether AI makes sense for your product or operations, we’re always open to a conversation.
Not all projects are presented here, most of them are under NDA. Too bad, but that's part of the job.
Robust speech sentiment analysis systems, leveraging audio preprocessing, spectrogram analysis, and custom models to interpret nuanced emotional tones.
Business intelligence platform for smarter growth in real estate, combining location intelligence and AI to enable better decisions.
Product discovery and recommendation system
2024
An advanced recommendation system for product personalisation which leverages data from custom web scraping pipelines and APIs to enrich recommendations.
Examples of what we’ve built
AI VoIP Platform
2025
A scalable, cloud-based voice platform with coverage in 190+ countries and local DIDs in 100+. Proven to lift call-answer rates to as high as 96% in customer deployments.
Smartificial
Smartificial
Smartificial
Smartificial
Give the agent a lead list — it will call, qualify, and hand off leads with next steps. It can also handle inbound calls and stay consistently on-message.
Smart Voice Agents
Legal assistant trained on local legislation
AI assistant that searches through laws and regulations, answers complex questions and simplifies legal research.
Our strength comes from deep technical knowledge and years of building real, production-ready products.
We understand how systems behave under load, how data moves inside an organisation and how to turn that into reliable AI that supports daily operations.
Our expertise
Our Stack
We work across the full AI engineering stack — from large language models to audio processing and enterprise-grade data pipelines.
Voice AI: Audio preprocessing, VAD, spectrograms, emotion and sentiment models, and real-time evaluation flows.
NLP & LLMs: LLMs (OpenAI, LLaMA, Mistral), RAG architectures, fine-tuning, embeddings, vector search (Pinecone, Qdrant, Weaviate), and domain-specific assistants.
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NLP & LLMs: LLMs (OpenAI, LLaMA, Mistral), RAG architectures, fine-tuning, embeddings, vector search (Pinecone, Qdrant, Weaviate), and domain-specific assistants.
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Research-Level Engineering
We are a team of experienced engineers building NLP, voice and data systems for years. Our work is shaped by a strong engineering culture and guided by a scientific approach to AI — from choosing the right architecture to careful testing and evaluation. This combination allows us to deliver systems that work reliably under real-world conditions, not just in controlled environments.
- Daniil KrasnoproshinData Scientist & AI/ML EngineerDaniil’s research mindset shapes how we build AI systems — from careful data preparation and clear hypotheses to structured evaluation and a deep understanding of how models behave in real-world conditions.
He has extensive experience building NLP and voice systems, RAG-based assistants and full agentic architectures, with a strong focus on reliability, low latency and cost efficiency in production. - Ilya KislenkaFounder & CTOIlya is the founder of the company with a strong technical background.
For the past 5 years, he has been working as a CTO, focusing on delivery and technology management — from early pre-sale involvement to overseeing engineering teams, technical quality and the company’s internal IT infrastructure. With over a decade of hands-on development and leadership experience, he brings a structured, detail-oriented approach to building reliable, production-ready systems.
From Prototype to Production
Our background spans more than 10 years of full-cycle software development: backend, frontend, mobile, architecture, and distributed systems.
This foundation allows us to take AI ideas all the way to production — integrate them into existing products, build the surrounding infrastructure and ensure they perform reliably at scale.
This foundation allows us to take AI ideas all the way to production — integrate them into existing products, build the surrounding infrastructure and ensure they perform reliably at scale.
We’ve deployed AI into environments with high call volume, dense legal documents, regional market data and operational workflows that can’t afford downtime.
Our team is used to building systems that need to be stable, maintainable and ready for real traffic — not just prototypes.
Our team is used to building systems that need to be stable, maintainable and ready for real traffic — not just prototypes.
Our projects move forward step by step, with clear decisions and no surprises.
We approach every project as an engineering task: understanding constraints, decomposing the problem and building AI systems that can be deployed, supported and scaled in production.
How we work
01 —
Initial Call
Initial Call
05 —
Deployment & Support
Deployment & Support
We map the problem, understand the goal and check technical feasibility. If the task is clear, we prepare a proposal within a few days.
Once the system is live, we monitor performance, adjust where needed and support the team. When your product evolve, we help the AI evolve with it.
02 —
Pre-Cycle (for complex projects)
Pre-Cycle (for complex projects)
We run a series of calls with a business analyst. The goal is to fully decompose the product: features, data, workflows, constraints and the role AI needs to play. The analyst then translates everything into a detailed brief for the AI and engineering team.
04 —
Development & Integration
Development & Integration
We build the solution end-to-end. The development team integrates the system into the client’s product with minimal disruption.
03 —
Proposal & Planning
Proposal & Planning
We outline the approach, timeline and team setup. If the client is aligned, we move straight into execution.
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