What applied AI consulting is — and is not
It is not buying a platform or training a model from scratch. It is identifying repeated decisions inside the company, measuring what they cost today in time and error, and replacing the mechanical part with automation under human supervision. The model is the means; the result is cost per transaction, response time and decision quality.
We start with a process inventory expressed in numbers: documents received per week, hours spent reconciling, customers lost to slow replies, days needed to close the month. Without those numbers there is no business case — only enthusiasm.
The diagnostic produces a short list ranked by return, each item carrying an implementation cost, a monthly running cost and its risk profile.
Five use cases with fast payback
Document automation: extracting data from invoices, delivery notes, policies and contracts, with human validation on exceptions. Assisted service: assistants that answer in English and Portuguese, qualify requests and escalate what needs a person.
Collections and credit: internal scoring that ranks the book by probability of payment and prioritises outreach instead of treating every customer alike. Demand and inventory forecasting: purchasing and replenishment, critical wherever import lead times are long.
Financial and management reporting: automatic consolidation of data scattered across spreadsheets and disconnected systems, with dashboards that close the month in days rather than weeks.
Data, infrastructure and real-world connectivity
The constraint is rarely the model — it is data quality and connectivity. Before automating anything, decide where data lives, who may read it, what runs in the cloud and what must keep working on an intermittent link, including offline operation with later synchronisation.
We also define integration with what already exists: ERP, billing, banking, payroll and the spreadsheets the business actually runs on. Replacing whole systems is usually the slowest path; connecting existing ones with well-designed automation delivers inside the same quarter.
On model consumption, the decision that matters is cost per call versus the value of the decision, and which data may leave the company. Sensitive processes get anonymisation, limited retention and an audit trail.
Risk, governance and people
Automation without governance creates new risk: unexplained decisions, personal data processed without a lawful basis, single-vendor dependency. We assign process owners, set automatic-decision limits, require human review wherever money moves, and keep an incident log from day one.
Adoption is the most underestimated part. A tool without training changes nothing, so we always include hands-on training for the teams who will use the system, plus indicators that show real usage rather than installation.
Companies that want in-house capability use our Business Analytics training, capped at five participants per cohort.
How we work: 90 days, three phases
Days 1–20, diagnostic: process map, baseline numbers, data and system inventory, use cases ranked by return and risk. Days 21–60, pilot: one or two use cases deployed in the real environment at limited volume, with before-and-after metrics.
Days 61–90, scale and handover: extend what worked, document it, train the teams and leave a maintenance plan with explicit monthly costs. If the pilot misses the agreed metric it is closed — cheaper than institutionalising a project that does not pay.
Companies with cross-border operations usually combine this with our trade and market-entry work, because the same data drives pricing, stock and credit decisions.
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QFLab provides market intelligence and strategic consulting services; it does not provide regulated investment advice.
