Research
Validate the problem, audience, and success criteria before expensive development.
AI PRODUCT STUDIO · WORLDWIDE / REMOTE
We implement AI workflows and build digital products
Select a measurable business workflow, validate data and risk, build a bounded pilot, and prepare proven scope for operations.
01 · What we do
We build the complete product, not an isolated AI layer
We work from problem definition through launch and the next growth cycle.
UX and prototype
Build a flow and interface that can be tested with real users.
AI / ML
Apply models, RAG, recommendations, and automation where they create value.
Web and backend
Develop interfaces, APIs, roles, data flows, and production integrations.
Integrations
Connect the product to internal systems, external services, and ETL pipelines.
Launch
Test, deploy, and hand over a clear foundation for continued growth.
02 · Own products
Products and research
Products and research initiatives at different stages, with explicit boundaries between concepts, MVPs, and live public systems.
Alpha 0.1.1 · first production integration
PathWeaver
A typed and auditable Python framework for analyzing states, alternative paths, and scenarios while keeping the structural core separate from the LLM layer.
PathWeaver 0.1.1 is packaged as a Python wheel, has passed strict quality gates, and runs in the Gadalnya production backend. It does not yet have a public PyPI release or production-ready library status.
In production
AI Digital Insider
An ecosystem of technology content and practical AI tools.
The public platform is live at aidigitalinsider.ru with technology content and practical AI tools.
Public technical beta
PLC WEB
A browser-based FBD workspace for designing, validating, and simulating PLC logic.
The public beta is live at plc-web.ru with an FBD editor, diagram validation, scan-cycle simulation, and five controller profiles. Industrial deployment is not claimed.
MVP / staging
GPU Market
An MVP/staging P2P platform for GPU rental with web workflows, an API, and a protected local provider runtime.
The team staging environment has a working website, API, accounts, and test roles. A PyTorch/CUDA container run is verified on one Windows 11 / RTX 2070 setup; the full remote workflow, marketplace, and production remain unverified.
CVM-function MCP MVP
Marketing Optimisation
An AI-CVM platform whose target outcome is replacing the routine work of a dedicated CVM department, from CRM analysis to incremental-margin measurement.
The MCP MVP implements one end-to-end reactivation workflow through eight tools and has been tested on synthetic data. Its architecture targets software replacement of routine CVM work, but a real-data pilot must validate its ability to replace a full department and deliver commercial impact.
Public prototype and voice test
Iizhik.RF
A public content prototype with short stories, a mini-game, and a Sleep Stories section intended for future AI narration.
The public site includes stories, night and play modes, sleep content, and two surveys. Eight 30-second voice candidates are under blind evaluation; the final soothing AI voice and full audio and video podcasts have not yet been selected or released.
MVP concept
Legal Support MCP
An MCP-layer concept for reviewing technical specifications and contracts: risks, questions, sources, and draft wording remain within a human-controlled workflow.
The MVP scope is documented: an MCP server for rules, sources, templates, and versions, plus a thin AI-client skill. The intended output is a structured package of prioritised remarks, questions, source links, and draft wording, with material or ambiguous terms escalated to a lawyer.
Public MVP · Reading v2
Gadalnya
A public personalised reading: profile, baseline situation, three cards, interpretation, and an interactive meaning map.
Sites v12 publishes Reading v2: users set a profile and baseline situation, draw three cards, and explore connections in a meaning map. Voting for 74 cards is complete and four pilots are approved; real payments and measured product impact remain unverified.
03 · How we work
Short, testable stages
Each stage informs the next, moving from a problem to a working product and continued growth.
- 01
Problem
Define the context, constraints, and success criteria.
- 02
Research
Validate the problem, audience, and available data.
- 03
Prototype
Build the logic and interface for a fast test.
- 04
MVP
Turn the core scenario into a working product.
- 05
Launch and growth
Launch, measure, and continue the product cycle.
04 · Client work
Client work delivered
Real business problems, constraints, design decisions, and confirmed outcomes, without exposing client data or inventing KPIs.
05 · INSIGHTS
Practical guides to AI products
Decision criteria, validation process, measurement, and limitations, grounded in primary sources without invented outcomes.
AI product development roadmap: from business problem and data to launch
This roadmap avoids starting with a stack or model. It links a user problem to an observable business outcome, tests the data and highest AI risk, builds one end-to-end path, and only then invests in production operations.
Read the guideAI MVP validation plan: what to prove before production
An AI MVP is not a demonstration of every future feature. It should retire the most expensive uncertainties by separating user value, data fitness, AI quality, end-to-end workflow behaviour, and operational readiness.
Read the guideRAG assistant architecture, evaluation, and limitations
RAG is more than a vector database and a model. A dependable assistant needs source and permission governance, reproducible document processing, retrieval, evidence-backed generation, a versioned evaluation set, and operational monitoring.
Read the guideAI business automation: how to choose a workflow and measure impact
The best first workflow is not necessarily the most visible. It recurs, has understandable inputs and an owner, allows its output to be checked, and has a manageable error cost. Impact is measured against a recorded baseline process.
Read the guideAI agent development cost and timeline: what determines the estimate
An agent estimate depends less on the model than on systems, permissions, error cost, evaluation, and operating requirements. A defensible estimate starts with one bounded workflow and a tool map.
Read the guideHow to choose an AI development partner
A strong proposal explains how value, data, errors, integrations, operations, and responsibility will be tested—not only the model and stack.
Read the guideAI agent architecture, security, and evaluation
A safer agent is built around bounded tools, server-side enforcement, untrusted inputs, human decisions, and reproducible evaluation.
Read the guide
06 · New project
Start with your problem
Tell us what needs to be researched, built, or launched. You do not need to know the exact stack or scope in advance.
- We define the problem and success criteria first
- We do not add unsupported promises or metrics
- We agree the format and stages after discussion
07 · Product updates
Follow early-stage product development
For Legal Support MCP and Gadalnya, you can leave a contact and receive one message about the next public iteration.
Next-stage notification
Choose a product and a preferred contact. This form is only for the requested update.
Your contact stays under your control
You can withdraw consent and request deletion at hello@aisaassolution.com.
08 · Team and trust
Full-cycle product work without unsupported promises
One product workflow
Research, UX, AI / ML, web / backend, integrations, launch, and growth are treated as one connected effort.
Evidence when it is ready
Public cases and results appear only after verification and publication approval.
09 · FAQ
Working with the studio
At what stage can I bring a project?
A business problem, hypothesis, research direction, prototype, or a request for an MVP and launch are all valid starting points.
Can I order only the AI / ML part?
Yes, when a separate AI / ML scope makes sense. We define its boundaries and integrations after discussing the problem.
How can I view current products?
Live products open from the portfolio. Prototype and research calls to action reflect their actual stage.
How can I follow early-stage products?
For products with notification access, leave your contact on the relevant page. We use it only to tell you about the next public stage.
10 · Next step
Have an AI product challenge?
Share your contact details and context in the form, or write to us directly.
Contact the founder directly: oscar@aisaassolution.com