AlphaCorp AI is an AI agent development company dedicated to building custom AI agents and automation systems that help businesses run faster, work smarter, and scale without adding headcount. According to its description, the team has over 10 years of experience delivering enterprise and AI products, having built ERP systems that support business operations, as well as the AI agents that run on top of them. The company positions itself as taking over where pilots fail, building the parts that can run unattended every day in real data, real systems, and real money environments, including agent workflows, retrieval based on proprietary documents, and the evaluation and monitoring around them.
[Core Capabilities and Services]
- Custom AI agents: able to plan tasks, call tools, and complete tasks end-to-end, with human approval gates for any action that leaves the organization.
- Retrieval-augmented generation (RAG): retrieval based on proprietary documents, with answers accompanied by sources and auditable.
- Intelligent automation: handling high-repetition, low-attention processes such as invoice matching, order entry, and document processing, and integrating with existing ERP systems.
- Evaluation and monitoring: delivering test suites, tracing, and dashboards to address silent drift in production environments.
- Generative AI development: production-grade agents, retrieval pipelines, and fine-tuned models.
- Custom AI development: assembling AI around proprietary data, with an honest score for build or buy.
- LLM fine-tuning: LoRA, QLoRA, DPO, and RAFT, moving format, tone, and task behavior from prompts into model weights, and recommending retrieval when applicable.
- Custom software development: integrating AI into production-grade systems, following NIST and OWASP standards.
- RAG development: connecting LLMs with real data and designing retrieval pipelines.
- Prompt engineering: structured prompts, evaluation frameworks, and optimization loops.
- Software engineering: full-stack development for AI products, including APIs, dashboards, integrations, and more.
- MLOps: CI/CD pipelines, model monitoring, and infrastructure automation.
- AI integration audit: reviewing existing workflows, identifying where AI can save time and cost, and providing a roadmap.
- AI consulting: helping businesses determine their AI strategy and formulate an executable plan.
[How to Use] The company offers free call bookings to discuss AI's role in the business. Every AI agent project follows the same path from discovery to production: discovery and AI integration audit (mapping workflows, data, and systems, and defining success criteria); architecture and agent design (designing agents, tools, and guardrails);Build, evaluate, and fine-tune (automated evaluation frameworks, fallback logic, and human-in-the-loop checks); deployment and MLOps and continuous support (go live, monitor performance, and continuously improve as the business evolves). Integration is done through the Model Context Protocol (MCP), an open standard for connecting models with tools and data, now managed by the Agentic AI Foundation under the Linux Foundation. As of December 2025, MCP has more than 10,000 active public servers.
[Use cases] Teams across industries including healthcare, financial services, SaaS, and logistics deliver production-grade AI agents. Project examples include: building agentic software and RAG pipelines for Versar Global Solutions to enable intelligent document processing and autonomous decision-making; providing full-stack AI agent engineering for the hospitality industry, based on an Azure-based Next.js/TypeScript frontend and FastAPI/Python backend, including LLM prompt engineering and MLOps; autonomous AI agent consulting, implementing Anthropic Claude Code and OpenAI Codex in production engineering workflows; providing full-stack engineering for CampusReel, including pathfinding algorithms; providing full-stack AI agent engineering for Luniq, based on an Azure-based Python/FastAPI backend and RAG pipelines, prompt engineering, and MLOps/DevOps. The company has also built an open-source RAG stack, which it says is the fastest source-available RAG stack, with end-to-end document ingestion in under 200 milliseconds, using Milvus vector search, local Jina embeddings, and streaming LLM responses, all in a single asynchronous Rust binary, powered by Cerebras, Groq, and others.
[Limitations] No clear information is provided in the materials about fees, pricing, and specific quotes; it only states that cost is determined by scope, priced per work session, with drivers including the number of systems the agent touches, the required degree of autonomy, and the amount of reasoning, and that the scoping phase provides a fixed estimate before building begins. Timelines depend on integration scope and the required autonomy, and the scoping phase produces a dated plan.
[Team and operations] The founder is Ignas Vaitukaitis, who understands how enterprises operate and what they need, and founded AlphaCorp AI with the belief that the companies leading the future are the ones building with AI today. Engineeringis done in Brazil, with a remote-first AI engineering team aligned with U.S. Eastern working hours, and the team is trilingual and supports asynchronous collaboration. After launch, AlphaCorp AI operates the agents together with clients: tracking monitoring, evaluation regression runs, token spend tracking, and adjusting autonomy as trust is established. A 2026 Deloitte survey found that only 21% of enterprises have mature governance for autonomous agents, and the operations phase is intended to keep clients from becoming part of that statistic.
