Enterprise Agentic AI Studio

We build AI agents that run inside a real business process

From process diagnostics and architecture through to production rollout, integrations and a governance model. We do not stop at a chat interface — we build a governed loop: goal → decision → action → verified outcome.

Start with Phase 0 See the approach →

25+ yearsof project and technology experience

₽3bn+in delivered project budgets

800people in the teams we have led

4 weeksto a working prototype

Human-in-the-loopAutonomy only within defined limits

Experience with large enterprises

Magnit

Rosneft

Svyaznoy

Euroset

Surgutneftegas

Shell

General Motors

Apple

For commercial, operations and technology leaders who need a measurable result rather than one more AI demonstration.

1 processthe focus of the first sprint

Live dataprototype on production data

Board-readybusiness case and rollout plan

Solutions

Agentic systems built into the company's operating model

The agent receives a goal, plans its actions, uses corporate tools, checks constraints and escalates to a human only the decisions that genuinely need attention.

01 / DISCOVERY

Process map and potential

We identify repeatable decisions, lost time, control points and the scenarios where an agent delivers a measurable effect.

Process miningKPI baselineROI hypothesis

02 / BUILD

Agents and the agentic platform

We design agent roles, tool APIs, memory, orchestration, interfaces and integrations with ERP, CRM, DWH and corporate services.

Multi-agentMCP / APIRAG

03 / CONTROL

Governance and operations

We build in policies, approvals, observability, quality evaluation, audit and safe scaling.

GuardrailsEvaluationsAudit trail

AI assistants

Personal AI assistants for executives: tasks, mail, calendar and projects through a single interface.

Analytics

Real-time dashboards and reports: key metrics visualised, trends forecast, insights surfaced automatically.

Integrations

We connect systems and services under full control through AI agents: CRM, ERP, messengers and external APIs in one flow.

A start without the big bang

Phase 0: prove the value in four weeks

We take one painful workflow, build a prototype on production data and show exactly what can be automated, what control it requires and where the economic effect comes from.

✓process map and target model

✓a working agent prototype

✓effect and risk assessment

✓production architecture and roadmap

Choose a process for Phase 0

Four weeks

How the sprint runs

Week 1
DiagnosticsWe take one workflow apart: steps, data, where time is lost, and who decides what on what grounds.
Week 2
PrototypeWe build the agent on the client's own data and test the hypothesis on live cases rather than a demo set.
Week 3
Limits and controlWe define where the agent acts alone, where it asks for confirmation, and how a wrong action is rolled back.
Week 4
DecisionWe quantify effect and risk, and fix the production architecture and rollout plan in board-ready form.

Applied scenarios

Not a general-purpose bot, but a specialised digital role

Every agent has a clear responsibility, access only to permitted tools, and a measurable process outcome.

Commercial decision agent

Gathers context, runs models and scenarios, explains its choice and drafts a decision for approval.

Exception monitoring agent

Watches KPIs and events, separates meaningful deviations from noise and routes the issue to its owner.

Operational execution agent

Manages the action queue, respects windows and SLAs, controls publication and confirms the work was actually done.

Analytics and documents agent

Runs factor analysis, builds charts and produces the report, deck or package a management decision needs.

Real implementations

Six B2B solutions ready to deploy

The scenarios above describe the types of digital role. Below are the specific processes where the work is already done.

Documents and knowledge

A single information space for the project

Problem20+ participants across 5 workstreams; the information lives in mail, chats and people's heads

Decisionmail and chat ingestion, meeting transcription, minutes, action items, search across the whole history

Effectan answer about the project takes minutes instead of days of collecting it

SAP and IT

Agents in the release cycle

Problemundocumented legacy ABAP, manual regression, false positives from BPCA

Decisioncode description, change review, test generation, defect triage, SAP access over MCP

Effectfewer manual tests; knowledge of the code stops being one person's property

Testing

Maestro — test automation

ProblemERP and web regression is manual, tests are brittle and depend on developers

Decisionmodel-based scenarios without code, test generation and self-healing, scheduled runs

Effectregression in hours; a business analyst can read the test cases

Requirements

Business requirements analysis

Problemdifferent analysts write the specs, quality varies, errors surface during development

Decisionchecks against criteria — completeness, unambiguity, testability, consistency — with problems highlighted

Effectrequirements reach development clean, with less rework

Corporate data

Analytical assistant for executives

Problembetween the question and the number sits a several-day queue to an analyst

Decisionplan-versus-actual calculation, root-cause analysis of deviations, alerts on threshold breaches

Effectthe decision is made on the numbers the same day the question is asked

Procurement

Commercial proposal analysis agent

Problemdozens of proposals in different formats are consolidated into a table by hand

Decisionline items and terms extracted, mapped to one nomenclature and checked against requirements

Effectcomparison in hours, with every deviation referenced to a clause

×10faster task execution

−80%less routine in the team's work

+35%business process efficiency

Working together

The agent strengthens the team rather than creating a black box

We design the points where people and AI meet: when the agent acts on its own, when it asks for confirmation, how it shows the grounds for a decision, and how a user can stop or roll back an action.

“People keep strategy, rules and control. Agents take context gathering, calculation, routine and the discipline of execution.”

Enterprise readiness

Control is built into the architecture

Governed autonomy is a precondition for any production agentic system. For every decision we retain the data, model versions, tools invoked, checks performed and who initiated it.

Request an architecture session

Human-in-the-loopapproval levels by risk

Guardrails & policy enginehard constraints and rules

Trace & auditthe full decision history

Evaluation & monitoringquality, cost, latency

Access & isolationRBAC and data separation

Rollback & kill switchstop and safe rollback

Technology

Technology that drives real results

LLMs, orchestration, integrations, ERP, infrastructure and test automation — without locking the project to a single model vendor.

OpenAI GPTAnthropic ClaudeGoogle GLMMCPLangGraph / LangChainLlamaIndexAnthropic SDKLangflowSAP S/4HANAABAP1CPythonNode.jsReact / Next.jsTypeScriptDocker / KubernetesPostgreSQLREST API / GraphQLAWS / Yandex CloudCI/CDPlaywright / Tosca

Start with one process

Ready to get started?

Tell us where your team loses hours. We will propose what to examine in Phase 0, what prototype to build, and which metrics will prove the case for a production rollout.

Available daily, 09:00–21:00 MSK. Reach us directly, whichever way suits you.

Telegram@Evgeny_2020

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WhatsApp+7 964 537-77-20

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Emailalex@a-help.me

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Phone+7 958 638-41-35

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