AI Implementation with an ROI Plan

• AI readiness audit in 1–2 weeks. We assess process maturity, inventory your data, and identify 2–3 scenarios with real potential — not ones that are "interesting from a hype standpoint."
• Roadmap with ROI. We prioritize use cases by metrics in money and timelines. You know which scenario will pay off first and how long it will take.
• PoC on your data in 4–6 weeks. We test the hypothesis on real processes with a measurable result, not on a test sample.
• Stack and vendor selection with no lock-in. We compare Russian and international solutions and design the data contour for your task.
• Strategy and systemic implementation. We move the pilot into production and manage change so the solution works rather than sitting as a demo module.
• AI + information security expertise. Secure AI adoption with consideration for 152-FZ, corporate policies, and restrictions on transferring data to external APIs.

Typical benchmarks — the final estimate and timelines depend on data maturity and the number of processes; we fix the scope before the start:

Expected result: in 1–2 months — a strategy with a roadmap and ROI assessment; in 3–6 months — launched pilots with a measurable effect; in 12 months — solutions in key processes.

Entry rule: first we analyze the situation, then we propose a format — without pushing a large project. Often the right answer is to start with an audit, not with implementation.

• We start with an audit, not with selling a model. In 1–2 weeks we'll show what's ready for AI, where the 152-FZ risk lies, and which scenario will pay off first — with numbers, not promises.
• Vendor-independent advice. We recommend the solution for your task, not the one that's more profitable for us. We advise both Russian and international stacks where it's legal and reasonable.
• Business focus, not technology focus. KPIs in money, not in model metrics. Proof: AI/ML certifications (Yandex Practicum, Skoltech, AWS ML).
• Fast PoCs. We validate the hypothesis in 4–6 weeks, not six months. Proof: publications and talks at AI conferences.
• Working with your data. We build on the client's real processes and data, not on hypothetical examples.
• Russian stack. Expertise in GigaChat, YandexGPT, and domestic MLOps tools — for a sovereign contour and the public sector.
• We don't push heavy implementation where automation is enough. Where a process can be described by rules, we first do classic automation — it's faster and cheaper.

Who this solves the problem for:

• CDO / CTO of a large company — understands the need for AI, but pilots "died on the shelf"; needs a systemic approach that turns them into working products.
• IT director of a government agency — received a top-down task to "implement AI"; needs to understand what AI actually solves, what data is suitable, and how to choose a Russian solution without misdirected spending.
• CEO of an SME — hears that competitors "already use AI"; needs an honest answer: is there value for the business or is it just a trend.

• There's no data and you're not ready to prepare it. If data is fragmented and not systematized, you first need a data audit and preparation of an AI-ready contour — we'll honestly discuss this at the start.
• You don't need AI yet. Then we'll say it directly: don't spend the budget. Sometimes the right answer is rules-based automation without AI.
• You need a "radical innovation project" for hype. We don't promise magic and work toward a measurable result, not a presentation.
• You're not ready to provide access to at least a data sample. To assess ROI, real data is needed — at least 100–1,000 rows or 20–50 documents.

The main step is a free AI assessment. In a short consultation, we clarify priorities, data maturity, and personal data risk before you pay. You get a plan: what will actually pay off first and which path is shorter.

The secondary step is an AI audit over 1–2 weeks, which delivers a readiness map and 2–3 priority cases with an ROI assessment on your data.

Then, down the chain — if the data is ready, we move to dataset preparation and data cleaning, so AI is built not on a "raw" contour but on a clean base.

[Request an AI assessment] · [AI consulting: how we calculate ROI] · [Dataset preparation — the next step]

• National AI Development Strategy through 2030 — targets for AI adoption across industries and subsidized programs.
• GOST R 59276-2020 — requirements for developing AI systems, including model explainability.
• 152-FZ "On Personal Data" — restrictions on personal data processing and requirements for cross-border transfer.
• Experimental Legal Regimes (ELR) — regulatory "sandboxes" for testing AI in Moscow and other regions.
• Federal Project "Artificial Intelligence" — subsidies for companies implementing domestic AI.