Enterprise search and RAG
Source-grounded answers from internal documents. Retrieval takes knowledge structure, content freshness and employee permissions into account.
Focus: retrieval quality, grounded answers and data access.
For businesses and technology teams
Ruslan GROMOV
From a business challenge
to a working AI system.
I design solutions using LLMs, RAG and AI agents. I connect models to enterprise data, tools and processes — with clear quality criteria, safeguards and ownership of outcomes.
Architecture · prototyping · implementation support
AI solution architect,
mentor in applied AI
20+years in business automation
7+years of hands-on AI and ML experience
10+years of training and developing teams
Business + ITaligning goals, data and architecture
Expertise
AI is part of a system,
not a standalone chat
I work on the entire solution architecture: where the data comes from, how to verify model outputs, what an agent is allowed to do and how the solution fits into business processes.
Agents and controlled workflows
Systems that do more than answer questions: they call tools. Choosing between a predictable workflow, a single agent and coordinated specialist agents.
Focus: task state, autonomy boundaries and approval of actions.
Documents and complex context
Extracting, comparing and validating information from contracts, policies, spreadsheets and long documents. Structured outputs ready for further processing.
Focus: data schemas, validation and traceability to the source.
AI in the enterprise
Connecting models and agents to 1C, CRM platforms, knowledge bases and internal services through APIs and MCP. Permissions, contracts and error handling matter more than the number of integrations.
Focus: controlled access and reproducible operations.
Quality and security
Testing the system against agreed scenarios, from retrieval accuracy to agent actions. Separating trusted instructions from external data and protecting sensitive information.
Focus: evaluation datasets, audit trails and human oversight.
Reliability and economics
Observability, request costs, latency and failure handling. Models and infrastructure selected to meet requirements, not to add complexity.
Focus: limits, fallbacks and cost per useful outcome.
Technical details: approaches and tools
Potential architecture components. The stack, implementation scope and responsibilities are defined after reviewing the client's needs and infrastructure.
- Retrieval and context
- Hybrid search, BM25, reranking, GraphRAG, memory and context management; Qdrant, PostgreSQL, Neo4j.
- Agents and tools
- Tool calling, MCP, LangGraph, supervisor / worker, handoff, checkpoint / resume; deterministic workflows where autonomy is not needed.
- Services and data
- Python, FastAPI, Pydantic, Docling; API contracts, queues, events, streaming responses and enterprise integrations.
- Security
- RBAC, least privilege, permission checks before retrieval, sensitive data filtering, prompt injection defenses and human approval for critical operations.
- Quality and observability
- Golden datasets, retrieval metrics, citation checks, agent trajectory evaluation and regression tests; pytest, OpenTelemetry, request and cost tracing.
- Delivery and operations
- AI gateways, local or cloud models, caching, rate limits, retry / fallback; Docker, CI/CD, IaC, prompt versioning and recovery planning.
Experience and trust
Teaching, speaking
and expert contributions
Work with business leaders, education projects and the public sector. The gallery shows teaching, team sessions, speaking and professional meetings — different types of participation, not a uniform list of training clients.
Applied solutions
From model capabilities
to departmental needs
Example scenarios for AI solution design, not completed client projects. The system scope and expected impact must be validated using the company's own data.
Legal · procurement
Contract and tender assistant
Compares terms against internal policies, highlights discrepancies and builds a requirements matrix with references to source documents.
What we validate
Extraction completeness, citation accuracy and missed risks. The final decision remains with the domain specialist.
Operations · sales · finance
Process agent for 1C and CRM
Brings together order, payment and obligation data, explains discrepancies and prepares the next workflow step.
What we validate
Data and action correctness, user permissions and repeated calls. Changes follow defined rules and approval procedures.
Development · IT
Engineering assistant for your team
Analyzes a task and repository context, proposes changes, generates tests and prepares review materials.
What we validate
Passing tests, access restrictions and reproducibility. The team controls code merges and releases.
Design principleMore than an answer.
More than an answer.
An outcome under control.
Every step serves a business goal. Data, actions and outputs follow agreed rules.
Input
Task and data
The goal, enterprise knowledge and user context.
Documents · 1C · CRM · APIs
AI core
Solution logic
Orchestration selects the next step and preserves task state.
- Retrieval / RAG
- Models / LLMs
- Tools / APIs
Output
Validated result
A source-grounded answer or an action permitted by workflow rules.
Validation · approval · executionShared control layer
- Access permissions
- Quality and audit
- Cost and latency
- Human approval
Working process
Architecture you can
discuss, test and implement
First, we agree on the desired outcome and constraints. Then we test the hypothesis, document decisions and prepare the system for real-world use.
Define the problem
Processes, users, data sources, risks, IT and security constraints. Identifying where AI adds value and where conventional automation is sufficient.
Deliverable: project boundaries and success criteria.
Choose the architecture
Components, integrations, deployment options, model selection and resource estimates. Trade-offs between quality, speed and cost.
Deliverable: C4 / HLD diagrams, key architecture decision records and an implementation plan.
Test with a prototype
A proof of concept using representative data, failure scenarios, output evaluation and agent behavior testing. Evidence determines the next step.
Deliverable: a prototype, evaluation report and updated risk assessment.
Prepare for production
Low-level design, team support, integration acceptance, monitoring, limits and recovery planning. Knowledge transfer to the people responsible for operations.
Deliverable: an agreed launch plan and operating procedures.
Complexity must earn its place
Not every problem needs multiple agents, a custom model or Kubernetes. An architecture should explain which problem each component solves and what it costs to maintain.
Professional experience
Engineering depth.
The language of business.
I combine hands-on AI architecture with experience in adult learning. My career spans software development, team leadership, data engineering and solution architecture. I have worked with neural networks and machine learning for over seven years; my current focus is language models, AI agents and process automation.
That background helps me explain not just what a tool can do, but what makes it useful: data quality, process constraints and evaluation criteria. In a workshop, we distinguish an impressive demonstration from a solution worth developing.
For over ten years, I have taught internal and external teams. I work with owners, managers and specialists with different technical backgrounds, turning a broad ambition to “use AI” into a specific task, a learning activity and a reasoned next step.
From development to architecture
Applications, data, integrations and processes as a connected system, rather than a collection of isolated tools.
From a task to an independent skill
Program design, practical work, feedback and assessment criteria for adult learners.
Higher education in mathematics
Specialisation: mathematician and systems programmer.
Professional background: what I bring to the classroom
Software developer
I break a task down into inputs, actions and expected outputs. Participants learn to specify requirements for an AI agent, test individual steps and understand why a convincing answer does not necessarily mean a tool works correctly.
Team lead
Technical leadership and knowledge sharing inform how I organise group practice. Participants discuss requirements, distribute roles and explain decisions to colleagues; I help identify difficulties and provide feedback as they work.
Data specialist
I show how input quality affects conclusions. Using practice datasets, we examine missing values, duplicates, inconsistent metrics and calculation checks. This helps managers work critically with analytics instead of trusting a polished chart.
Solutions architect
I consider applications alongside data, integrations and business processes. In teaching, this helps distinguish a prototype from a production solution: define conditions of use, access requirements and questions to resolve with IT and information security.
AI specialist and educator
I turn experience with neural networks, language models and AI agents into accessible learning scenarios. I explain when knowledge-base search, RAG or multi-step automation is useful, and when a simple tool with human verification is sufficient.
Engagement options
Expert support where
architecture needs clarity
A focused expert review, a new solution design or ongoing team support. We agree on scope and deliverables for each stage before starting.
AI initiative review
A review of the business problem, current prototype or architecture. Deliverables include constraints, risks, alternatives and a prioritized action plan.
Solution design and prototype
A technical concept, validation of key assumptions, architecture documentation and acceptance criteria for the implementation team.
Architecture support
Design reviews, technical risk management, integration planning and production readiness alongside the client's IT team.
Technical team mentoring
Architecture and engineering guidance using the team's own tasks. Knowledge transfer on RAG, agents, quality evaluation and controlled implementation.
Fees and timelines are determined after an initial review, based on data readiness, integration availability, security requirements and the scope of responsibility.
Get in touch
Let's discuss your challenge
and the next step
Tell me which process you want to improve, which systems you already use and where the project stands. We will start with your goal, constraints and success criteria.