AI-native product engineering for businesses that need more than a landing page, a chatbot or a disconnected automation. I turn an ambiguous task into a controlled system: product boundaries, interface, backend, data, AI, integrations and production operation.
These are not isolated “services”. Each direction can become a complete product, a focused pilot or a clearly scoped part of an existing system. The work begins with the business constraint and ends with a usable operating result.
I take on a task when a ready-made SaaS, a website builder or a one-off automation cannot carry the actual business process.
First I turn an unclear request into a product boundary: users, data, decisions, risks and the nearest useful milestone.
Then I connect interface, backend, data, AI, integrations and production into one system with one technical logic.
A production-ready product rather than a set of disconnected contractor tasks.
I design agent systems for consultation, qualification, knowledge work and controlled operations — not a decorative chat window.
The contour includes business knowledge, tools, permissions, context, model policy, logs and a path for human takeover.
Fine-tuning is used only when it is technically justified; many tasks are solved more reliably through data, RAG, tools and evaluation.
An AI contour whose answer, cost and responsibility can be managed.
This direction is useful where an independent transaction record, verifiable state or programmable agreement matters more than a conventional database.
I design smart-contract logic together with the product interface, backend integrations, data model and security boundaries.
On-chain is not added for fashion: the architecture starts with the business rule that must be made verifiable or tamper-resistant.
A Web3 component embedded in a product system, not an isolated contract.
I build data paths when important signals are scattered across sources, inconsistent or invisible to the people making decisions.
The work includes ingestion, normalization, quality checks, a data model, analytics and, where it adds value, classification, scoring or prediction.
A model is not an end in itself: its output must be explainable, connected to a workflow and measured against a business decision.
A data product that makes a specific operating decision more reliable.
I connect CRM, 1C, payments, messengers, external APIs and an existing product into one traceable business process.
Before automation, I map data ownership, failure points, permissions and the action a person must still approve.
The implementation includes API contracts, queues or webhooks when needed, monitoring, retries and a safe recovery path.
A controlled operating flow rather than a fragile chain of manual transfers.
A prompt is only one layer of an AI system, not a substitute for product logic, trustworthy data or permissions.
I define roles, instructions, tool calls, response formats, refusal boundaries and the source from which a model may take facts.
Then I add evaluation scenarios, logs and human approval where an inaccurate answer can affect a customer, money or an operational decision.
More predictable AI behaviour with visible boundaries and testable quality.
Describe the task — a form or the chat.
Free: scope, timeline, cost.
A working core that proves the architecture on a real business scenario.
In iterations, with demos and metrics.
Production, monitoring, growth.
The whole project — from architecture to launch and support.
AI and automation built into your existing system.
I run, monitor and evolve the platform after launch.
Send the context and desired outcome. I will identify the architecture, risks and the first useful milestone.
