AmpliIA helps companies identify opportunities, design the architecture and build AI applications that work in production. We work from consulting to implementation: agents, automations, internal platforms and integrations with existing systems, always considering confidential data, business rules and operational risk.
We do not deliver only strategic recommendations or isolated prototypes. Ampliia connects business vision, software engineering and governance to make AI a reliable part of the operation.
We map processes, data, risks, systems and opportunities where AI can reduce cost, increase speed, improve revenue or raise decision quality.
We define flows, integrations, knowledge bases, permissions, models, tools, quality criteria and the right level of human supervision.
We build agents, automations, backends, dashboards, APIs and integrations with CRMs/ERPs, WhatsApp, documents and internal systems.
We design for access control, traceability, logs, answer evaluation, protection of sensitive data and monitoring routines for corporate use.
Check some implementation fronts already delivered. The focus is always the same: turning knowledge, data and processes into AI applications that are useful to the operation.
Agents that qualify leads, answer questions, collect information, query systems, register requests and transfer history to the human team when needed.
Flows integrated with clinic systems to book, confirm, reschedule and cancel appointments while respecting operational rules, availability and patient context.
OCR, RAG, classification, summarization, semantic search and information extraction for regulatory, legal, administrative or technical documents.
Internal environments where teams query manuals, policies, contracts, notes and sensitive knowledge bases with access control and clear usage limits.
Systems that connect AI to spreadsheets, databases, APIs, ERPs and administrative routines to reduce repetitive work and operational errors.
Platforms with AI at the center of the experience: assisted creation, analysis, planning, content generation, human review and continuous improvement.
Beyond company projects, we maintain technical publications and open-source code related to responsible use of agents and language models.
Accepted and presented at ECML PKDD 2025. Introduces the MAPS framework, which enhances multi-step mathematical reasoning in LLMs through multi-layered self-reflection and iterative auto-prompting.
Open-source work related to reasoning-effort control and more transparent use of models in real applications.
A direct process to move from business opportunity to deployed application, without losing technical control, security or scope clarity.
We map the objective, users, available data, systems, legal constraints, risks and what needs to improve in the operation.
We design the architecture and implement the system with integrations, permissions, evaluation, UX and human fallback where required.
We ship with monitoring, logs, documentation and improvement cycles to increase quality, coverage and reliability over time.
We can evaluate where AI makes sense, which data and integrations are necessary, which risks must be controlled and what the implementation path would look like.