From a business problem to a production AI system
We don't start with code. We start with the process and the numbers: what it costs today, what it would cost to automate and what return to expect. Then we build, test and run it, nearshore and in your time zone.
Stages of the work
- 01
Diagnostic
We analyze the problem or process you want to improve and assess whether it makes sense to automate it or build a system.
On your side: You receive a concrete assessment of viability and approach.
- 02
Discovery
We document how the process works today: who is involved, which systems are used, and where the bottlenecks are.
On your side: We need access to the people who run the process.
- 03
Prioritization
We define which part of the problem should be solved first so visible results arrive as soon as possible.
On your side: You validate the order of priorities with us.
- 04
Functional design
We specify what the system will do: screens, flows, rules, and use cases, in a document you can understand.
On your side: You approve the scope before development starts.
- 05
Architecture
We design the technical components, the data, the integrations, and the security of the solution.
On your side: You receive a description of the technical solution and its operating costs.
- 06
Development
We build the system iteratively, showing working progress in each delivery.
On your side: You see real progress, not percentage reports.
- 07
Testing
We validate features, integrations, and performance with real process data.
On your side: You take part in testing with your team.
- 08
Implementation
We deploy the system to production and connect it to day-to-day operations.
On your side: The system is running in your operation.
- 09
Training
We train the people who will use and administer the system, with documentation included.
On your side: Your team is able to operate the solution.
- 10
Evolution
We measure results, fix issues, and add features according to real usage.
On your side: We define the improvements of each cycle together.
Scope and result
How scope is validated
Scope is written down before development: screens, flows, rules, and success criteria. You approve it; later changes are agreed, they do not appear on the invoice.
How the result is measured
We define the metrics before we implement: hours saved, errors avoided, response times. The system evolves with those data, not with impressions.
How we decide where to apply AI
We don't start from the technology: we start from the expected impact on capacity, cost, productivity, and profitability. If the analysis doesn't show a real improvement, we don't recommend it. Once implemented, we keep testing with the same logic: lower the cost per result, not add technology for its own sake.
We go deeper on this in Green AI →Security & data practices
How we protect your data and your systems
These are the practices we apply on every engagement. We don't claim certifications we don't hold: when a project requires SOC 2 or HIPAA controls, we implement them on your infrastructure and document them for your auditors.
- Confidentiality first
- Mutual NDA before the discovery sprint, and a data processing agreement when personal data is involved.
- Your cloud, your data
- Systems are deployed in your cloud account whenever possible. Code, data and trained models stay in your name.
- Least-privilege access
- Named accounts, role-based permissions, MFA, and access revoked at the end of the engagement.
- Encryption
- Data encrypted in transit (TLS) and at rest, with secrets in a managed vault, never in code.
- No training on your data
- Client data is never used to train third-party models. We use enterprise API terms that exclude training.
- Audit trail
- Every agent action and every automated change is logged, with traceability back to its source.
Certifications today: team-level AWS Certified AI Practitioner. No company-level certification yet (no SOC 2). We'll update this page when that changes.
Tell us what you need to solve
In a 30-minute discovery call we review the process, the systems and the numbers, and tell you what's worth building, what isn't, and what return to expect. No cost, no commitment.