We implement real AI automations in your business. From process analysis to a system running in production. We deliver the system working and you decide whether to continue with ongoing operations or not.
Schedule a 30-min callWe identify repetitive processes in your operation and automate them with AI: ticket classification, document data extraction, report generation. Before implementing we measure the current process to get a baseline of time and errors, then compare against that baseline so the impact is measurable.
Autonomous agents that combine reasoning, tools and access to your systems to execute complete workflows. Unlike a chatbot, an agent can query APIs, read/write databases and trigger actions inside your systems. Typical cases: automated commercial research, proposal generation, ticket triage with contextual escalation.
Systems that search and reason over your documents, databases and internal sources with real context understanding. Your team asks in natural language and gets answers with citations to the sources. Useful when knowledge is fragmented across Notion, Drive, Confluence, tickets or PDFs and nobody has time to consolidate it manually.
Assistants that understand your business, remember prior conversations and scale customer or internal support accurately. They are trained on your documentation, have guardrails around what they can answer, and hand off to a human on ambiguity or sensitive cases.
Models that analyse patterns in your historical data to predict behaviour: demand by zone or period, customer churn, credit risk, equipment maintenance. The output is actionable predictions wired into the workflows your team already uses.
We connect the AI models to your existing systems — Salesforce, HubSpot, SAP, Odoo, Notion, whatever you run — securely and at scale. The model’s decisions land in the systems your team already works in. We handle authentication, rate limits and two-way sync.
The cases where an AI implementation delivers clear return share three common patterns: repetitive processes that consume hours per day from your team (ticket classification, document data extraction, report generation), unstructured data accumulated in logs, emails or PDFs that nobody has time to systematically review, and recurring decisions that require costly human analysis and could be solved by an assistant model.
The practical rule we apply during discovery: if the process consumes several hours per week from your team or is a recurring bottleneck in your operation, it's usually worth solving. If the impact is marginal, we'll tell you in the first call and won't move forward.
When we say no: if the case can be solved with a script, a macro or a traditional integration without a model, we recommend that route. AI adds value when there is variability, ambiguity or need for contextual understanding. If your process is deterministic and predictable, classical automation is simpler, faster to implement and easier to maintain.
Repetitive process with volume
Your team dedicates at least 5 hours weekly to the same manual workflow and the volume keeps growing.
Untapped unstructured data
You have logs, tickets, documents or emails with valuable information nobody has time to process systematically.
Decisions that require analysis
Your teams make recurring decisions based on reading or analyzing text, where an assistant model accelerates classification, ranking or filtering.
Cases solvable without AI
If a macro, a script or a traditional integration solves the problem, that's the right path. We don't add AI without justification.
Volume too low
Very low-volume processes that take little time per month rarely justify an implementation and its maintenance.
No data to validate
If the case requires a custom model and there's no history to evaluate results, we recommend instrumenting first before implementing.
The best tools from the artificial intelligence ecosystem.
Or email us at hello@berit.ar