AI & automation
We design copilots, agents and automations that work inside real operations: documents, tickets, knowledge bases, administration, customer care, operations and software delivery.
AI value does not come from the newest model alone. It comes from reliable data, clear scope, human ownership, correct integrations and quality metrics. That is why we combine software engineering, data governance, security and change management.
We bring AI where it can reduce repetitive work and improve decisions without removing team control.
Assistants for reading documents, summarising information, drafting content, searching knowledge bases and supporting operations.
Automations that perform multi-step tasks with logs, limits, approvals and integrations with business tools.
RAG, semantic search and internal chatbots based on verified and maintainable sources.
Policies, data classification, PII filters, access control, quality monitoring and escalation procedures.
Analysis of processes, data, tools, risks, skills and priority use cases.
An MVP on a specific workflow, with sources, prompts, evaluation criteria and acceptance rules.
Connection with business software, repositories, CRM, ticketing, document management, dashboards and APIs.
Usage policy, roles, metrics, user enablement and maintenance rules.
We choose processes with measurable value, manageable risk and available data.
We define access, sources, limits, approvals, logging and responsibilities.
We test with real users, measuring output quality, time saved, errors and trust.
We move the system into production with monitoring, training and an evolutionary backlog.
Summaries, classification, data extraction, completeness checks and operational drafts.
Knowledge assistants, response suggestions, ticket triage and operator support.
Support for code review, documentation, onboarding, internal search and DevOps automation.
Controls on policies, logs, evidence, checklists and audit preparation.
A request comes from Team Operations with a short internal memo attached. The task is to summarise it, with normal priority. The AI reads the memo and prepares a draft that includes case ID T-017, the referent, a concise summary, and a proposed priority. A person reviews the draft before any save or forward action happens.
If the same request arrives without a referent, the output is STOP: referent missing. The system does not guess: it asks for the missing detail and waits for human confirmation. The same stop rule applies if the attachment is unreadable, the data conflicts with itself, or the action affects customers, money, compliance, or permissions. In all those cases, a human must verify the facts and authorise the next step.
Each review should log the source, the reviewer, and the outcome so the team can trace what was automated and what remained under human control. If you want to discuss a real workflow, send a short brief through Contacts and compare the decision rules with this guide on AI or traditional automation.
We start with a short assessment: workflows, data, risks, use cases and adoption roadmap.
Assess an AI use case