Practical AI for products, teams, and workflows
We help teams turn AI into useful products, internal tools, and multilingual experiences.
What we focus on
How we work
Language and speech AI
We build multilingual and voice systems for products where off-the-shelf tools underperform.
Scoped pilots
Every engagement starts with a focused pilot to test fit before scaling.
Model selection over defaults
We match models to the task, balancing accuracy, latency, and cost.
How we think
Research-driven, product-focused
We build AI features that ship into real workflows — not demos or proof-of-concepts that stall.
Every project balances technical depth with practical constraints: timelines, budgets, and team readiness.
Evidence over hype
We benchmark models on your data before recommending them. No defaults, no hand-waving.
Multilingual depth
We work with languages and domains where general-purpose models struggle — low-resource languages, domain-specific terminology, mixed-script inputs.
Maintainable systems
We hand off documented, testable code — not black-box notebooks your team can't modify.
What we believe
AI should fit the workflow, not replace it
Most AI projects fail at integration, not at model quality. We focus on the part that actually matters — making it work inside your team's existing tools and processes.
See our delivery processMap before building
We audit the actual workflow first — where time is lost, what decisions repeat, what data already exists.
Pick the right model, not the biggest
A fine-tuned small model often beats a general-purpose large one on cost, speed, and accuracy for specific tasks.
Measure after shipping
We define success metrics before writing code and track them after launch. If the numbers don't move, we adjust.
Services
What we help teams build
We work across the stack — from model selection to production deployment — for teams that need AI to actually work.
AI-powered products
End-to-end product development where AI handles core functionality: search, classification, generation, or voice.
Architecture, prototyping, deployment
Workflow integration
Connecting AI models to your existing tools — CRMs, internal dashboards, support systems, document pipelines.
API design, data pipelines, system audits
Language and speech systems
Multilingual NLP, speech-to-text, and text-to-speech for languages and domains where general-purpose tools fall short.
Low-resource languages, domain-specific NLP
Team training
Workshops on prompt engineering, model evaluation, and building AI features into existing development workflows.
Hands-on sessions, not slide decks
How we engage
We start with a scoped pilot. If it works, we scale. If it doesn't, you know early.
Process
Four steps. No surprises
Every project follows the same structure so you always know where things stand.
Scope
Define the problem, agree on success criteria, and choose the right approach.
Prototype
Build a working proof on real data. Test with your team, not in isolation.
Integrate
Connect to your systems, handle edge cases, and set up monitoring.
Handoff
Document everything, train your team, and make sure it runs without us.
Team
Real people, AI workforce behind them
We're a small team on purpose. We use AI tooling across research, coding, testing, and content — so we move fast without scaling headcount.
Founders
Systems & Engineering
Behzod Ortiqov
Handles architecture, implementation, and integration. Background in machine learning, data science, and MLOps across MedTech and FinTech.
LinkedIn
Product & Delivery
Muhammad Abdugafarov
Runs product direction, client communication, and project delivery. Background in enterprise systems, engineering leadership, and applied AI for finance.
LinkedInAI tooling
We use AI tools across the entire workflow — research, code generation, language QA, testing, and documentation. This lets two people deliver what typically requires a larger team.
Contact
Have a problem AI might solve?
Tell us what you're working on. We respond within 24 hours.