AI engineering for regulated, data-heavy businesses

Senior engineers building production AI systems

We design, build, and operate AI agents and assistants, RAG systems, workflow automation, and enterprise integrations — from discovery to production, with people in control of the decisions that matter.

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Reference architecture

Automated / AI-assistedHuman checkpoint

Your systemsDocuments, databases, APIs, and tickets
Agent or assistantClaude where it fits — retrieval, reasoning, and tools where the work needs them
GuardrailsPolicies, evaluations, and an audit log
Human checkpointA person approves high-risk and customer-facing actions
ProductionMonitored, measured, and improved

Selected work

Production systems, shown working

Systems we have designed, built, and run. Each case shows what the system does, where AI does the work, and where a person decides.

Featured case · Oceni.by

From client intake to a signed valuation report, automated end to end

A multi-tenant SaaS platform for regulated asset appraisal in Belarus — vehicles, real estate, and equipment. AI handles the repetitive work; an appraiser signs off every report, and a person approves every investigated support reply.

Read the case oceni.by

3: valuation engines built: comparative, cost, and income methods
8: document types generated from one appraisal
600+: automated tests on every push
100+: recorded architecture decisions

The appraisal pipeline

Automated / AI-assistedHuman checkpoint

E-commerce data · hecnu.comLiveHecnuAutomated pricing intelligenceA pricing scraper service that collects, normalizes, and delivers product and market pricing data on a schedule.Read the case More under NDAAdditional client references are available on request, under NDA.Ask for references

Services

What we build

From a first use-case assessment to systems we operate in production. Senior engineers work directly on each engagement — no handoff to junior teams.

AI strategy & discovery

Assess use cases, data, and systems. Define target outcomes, constraints, and a production roadmap.

Claude implementation

Design Claude-based workflows where appropriate, with evaluations, approvals, and enterprise controls.

AI agents, assistants, and workflow automation

Build assistants that draft for a person to act on, agents that plan and use tools toward a goal, and multi-step workflows with approvals and exception handling.

RAG and knowledge systems

Build retrieval-augmented systems that connect LLMs to trusted enterprise knowledge.

Code modernization with AI

Use AI-assisted engineering workflows to improve legacy code, tests, documentation, and delivery velocity.

Enterprise integrations

Integrate AI capabilities with enterprise tools, data platforms, and processes.

Managed AI operations

Operate, monitor, and continuously improve AI systems in production.

Where this work applies

Fintech

Support operations, risk workflows, onboarding, analyst copilots, and controlled process automation.

Healthcare

Administrative automation, knowledge workflows, non-diagnostic review support, and compliance-aware tooling.

Legal

Document review, matter knowledge bases, contract analysis workflows, and approval-driven assistants.

SaaS

Internal knowledge assistants, customer support augmentation, product ops, and engineering enablement.

E-commerce

Catalog operations, support automation, merchandising workflows, and back-office process automation.

Enterprise IT

ITSM automation, employee service desks, knowledge search, integration layers, and managed AI operations.

Approach

A delivery model built for production

Pilot-to-production delivery with readiness gates, measurable acceptance criteria, and managed operations.

01
Discover
Understand goals, constraints, and data landscape. Select high-impact use cases and define success metrics.
- Use case prioritization
- Data & system assessment
- Risk & feasibility review
02
Prototype
Build a focused prototype, connect to real data, and validate with end users.
- Evaluation harnesses
- User testing & feedback
- Security & guardrail design
03
Productionize
Harden for production with controls, performance, and operational readiness.
- Guardrails & policies
- Access control & approvals
- Audit logs & traceability
- Performance & load testing
04
Operate
Monitor, optimize, and evolve with continuous feedback and governance.
- Monitoring & alerting
- Model & prompt updates
- Change management
- Incident response

Delivery team

Senior engineers across solution architecture, backend, data, and security work directly on each engagement.

Architecture approach

Reference patterns cover assistants, agents, RAG, evaluation, guardrails, audit logs, monitoring, and enterprise integrations.

Training & enablement

We document architectures, hand over runbooks, and train your team so systems stay maintainable after delivery.

Across every phase

Security & responsible AI

AI does the work. People stay in control.

We build systems that protect data, respect privacy, and keep a person in charge of every high-risk or irreversible action.

Privacy by design

Minimize data exposure. Use encryption in transit and at rest.

Human approval

High-risk or irreversible actions require human review.

Evaluate before production

Run evaluations, red teaming, and bias checks before release.

Auditability

Complete audit logs, lineage, and change history.

Sensitive data handling

Data classification, retention controls, and access governance.

Open source

Engineering in the open

Public projects under github.com/sensaiworks.

horizon-mcpOpen-source MCP (Model Context Protocol) server that gives Claude a controlled Windows desktop surface — tools for file, process, and window interactions with explicit user approval gates.View on GitHub

Team

The people who build it

Senior engineers and partners who work directly on client engagements across architecture, engineering, data, and AI delivery.

Dmitry Goubar

Director of AI Engineering

Innovation strategy, client engagements, and delivery direction.

Claude Certified Architect – FoundationsAnthropic · Issued Aug 2026

Konstantin Rogozhkin

AI & Automation Engineer

Frontend and full-stack product engineering, AI automation, and developer tooling.

LinkedIn Website GitHub

Boris Rozhnov

Solutions Architect

Solution architecture for AI systems and enterprise integrations.

Andrew Stakhov

Senior Software Architect

Cloud-native architecture, .NET, and enterprise platform engineering.

LinkedIn Website GitHub

Igor Kolodkin

Data Integration Specialist

Data integration, pipelines, and connecting enterprise systems and sources.

Nodirbek Rajabov

Data Recovery Specialist

Physical and logical data recovery across HDD, SSD, and RAID from a dedicated lab.

Website

Mike Lezinsky

Founder, Software Developer

Software development and product delivery.

Company

About SensAI

SensAI LLC is a US-based software and AI studio. The facts below are kept deliberately verifiable for enterprise and procurement review.

Legal entity: SensAI LLC
Country / jurisdiction: United States — South Carolina
Address: 2764 Pleasant Road Suite A939, Fort Mill, SC, 29708
Clients: Public reference: Oceni.by. Additional references available under NDA.
Certifications: Claude Certified Architect – Foundations (Anthropic) — issued Aug 2026, held by one team member and individually verifiable.
Partnerships: No vendor partnership status is claimed; any partnerships will be stated only once formally granted.

Contact

Tell us what you are building

Start with a focused production AI assessment — scoping the system, the data, and the constraints before any build begins.

GitHubgithub.com/sensaiworks Address2764 Pleasant Road Suite A939, Fort Mill, SC, 29708 Support and securitysupport@sensaiworks.com

Start herehello@sensaiworks.com

Email us directly. A short, specific message gets a faster and more useful answer than a form ever would.

Worth including

The system or workflow you want to build, fix, or take to production
Where the data lives, and any regulatory or residency constraints
Whether you are scoping an assessment or ready to start delivery