Our engineers bring judgment; AI extends their reach
Our team decides how your requirements are built and to what quality standard, and works with AI tools to review code, prepare tests, analyze security, and keep documentation up to date.
Our criteria
When we use AI, and how we decide
Keeping up with artificial intelligence is part of our job; so is using it with discipline. These are the rules we work by on every project.
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Authorized tools
We only use the AI tools authorized for each project, configured as agreed during planning.
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Verifiable tasks
We use AI on tasks whose output an engineer can check: with tests, through code review, or against the system's actual behavior.
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Decisions we don't delegate
The interpretation of your requirements, the architecture, security decisions, and the approval of every change always rest with our engineers.
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Your information, your rules
You decide which AI tools may process your code and information, and under which data policies.
Supervised work
In every task, the engineer leads and AI assists
The engineer sets the objective and is accountable for the result; AI contributes analysis, suggestions, and drafts that the engineer validates before using them.
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Development
The engineer designs the solution from the requirement and writes the code with AI support, following the project's conventions.
- Explores possible implementations and refactorings with AI
- Decides what is merged, how, and why
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Code review
Another engineer reviews every change alongside the results of the static analysis and AI-assisted review that run as part of the automated checks.
- Examines potential defects, inconsistencies, and deviations from conventions in greater detail
- Evaluates every observation and approves or returns the change
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Testing
The engineer defines which behavior must be guaranteed and, with AI support, prepares additional test cases based on the code and the requirements.
- Expands coverage with unit, integration, and edge cases
- Verifies that each case tests the right behavior before automating it
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Security
The team analyzes security with specialized tools and extends the search for risks with AI support.
- Traces risky patterns, credential handling, and vulnerable dependencies
- Confirms every finding and defines the fix
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Documentation
The engineer keeps technical documentation in step with the system.
- Starts from AI-prepared drafts of documentation, release notes, and change descriptions
- Corrects and verifies each draft before it is published
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Existing systems
Before modifying a legacy system, the team studies and documents it.
- Uses AI to walk through the code, map dependencies, and describe how it works
- Validates the analysis and defines the change plan
Workflow
The path of every change
Every change, written with or without AI assistance, follows the same path before it is merged.
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Step 1: Proposed change
The engineer develops the change from a requirement and, within what has been agreed with you, decides whether to use AI, and for what.
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Step 2: Automated checks
Static and dependency analysis, AI-assisted review, and automated tests run on the change.
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Step 3: Review and approval
Another engineer reviews the change and the findings from the automated checks, applying their own judgment as in any peer review, and approves it or returns it with comments.
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Step 4: Traceable merge
The change is merged with its requirement, tests, and approval on record, ready for a controlled deployment.
No AI-assisted change reaches production without an engineer's recorded approval. Technical responsibility for every delivery lies with our team, not with a tool.
Standards
Standards AI does not replace
We use AI to review more thoroughly, but none of these controls depends on it: each practice keeps its owner and its evidence.
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Code review and quality
Every change undergoes peer review and static analysis, and adheres to agreed coding conventions. This catches defects early and keeps the codebase consistent. AI-assisted review runs on every change as an additional check.
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Automated testing
Unit, integration, and regression tests run automatically with every change to catch errors before they reach your users.
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Security by design
Threat modeling, credential management, role-based access control, encryption of sensitive data, and dependency review are built into the architecture, not added as an afterthought.
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Continuous integration and deployment
Every release follows an automated process, with separate development, testing, and production environments, defined approvals, and the ability to roll back changes in a controlled manner.
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Documentation and traceability
Every requirement is linked to its code, tests, and deployment, and technical decisions are recorded. Your organization retains knowledge of the system, regardless of who operates it.
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Project and AI-use governance
Regular progress reports, risk management, and formal change control: every scope adjustment is evaluated before it is approved. AI use on the project follows what was agreed during planning and, when required, the confidentiality agreements.
Methodology
A predictable process from start to finish
We work in phases, with defined deliverables and validation checkpoints involving your team. You know the project's status, risks, and next steps without having to ask.
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Step 1: Assessment and discovery
We get to know your business objectives, processes, users, and technical constraints. We identify risks and dependencies before agreeing on scope, timeline, and budget. If a system already exists, we analyze its code and documentation with AI support.
Deliverable: Scope and risk document -
Step 2: Architecture and planning
We define the architecture, technologies, delivery plan, and acceptance criteria. Together we agree on the progress metrics, the communication plan, and the AI usage criteria: which tasks AI will support, with which tools, and under which data policies.
Deliverable: Plan, architecture, and AI usage guidelines -
Step 3: Iterative development
We build in short cycles, with incremental delivery and regular demos. We use artificial intelligence tools where they add value, and every increment goes through code review and automated testing before it is integrated.
Deliverable: Working increments and progress reports -
Step 4: Validation and quality
We run functional, regression, performance, and security testing, including additional cases our team prepares with AI support, and we document the evidence. Your team validates each delivery against the agreed acceptance criteria.
Deliverable: Test and acceptance report -
Step 5: Controlled deployment
We release to production through an automated, documented process, with a rollback plan, active monitoring, and knowledge transfer to your team.
Deliverable: Documented system in production -
Step 6: Support and continuous improvement
We monitor operations, resolve incidents in line with the agreed service levels, and evolve the system according to your business priorities, with the same discipline as the initial build.
Deliverable: Regular operations reports
Frequently asked questions
Questions we often hear before getting started
Who is accountable for an AI-assisted change?
Our team. The engineer who develops the change is accountable for its content, even if AI was used, and another engineer reviews and approves it; we record which requirement it addresses, who approved it, and which tests validated it. Technical responsibility for the delivery is ours, under the terms set out in the contract.
Which AI tools do you use, and which ones process our information?
On your project, only the ones you authorize. Before we start, we agree on which tools will be used, with what configuration, and under which data policies; when required, the confidentiality agreements also cover their use. If your policies do not allow your code to be processed by external services, we define the scope of AI use with you, including the option of not using it. If AI use is limited or excluded, review, testing, and the approval of every change remain in place.
Does working with AI shorten the timeline or lower the cost of a project?
With AI support, repetitive coding, testing, and documentation tasks take less time, and engineers can give more attention to design decisions and review. That efficiency is factored into timeline and cost estimates from the assessment onward; quality controls are not reduced.
Can you review the quality of a system we already have?
Yes. We perform a technical assessment of the code, tests, security, and architecture, supported by the AI tools you authorize, and our engineers validate every finding. You receive a report with prioritized risks and a gradual improvement plan designed to protect the continuity of your operations, and you decide whether our team or yours carries it out.
What does it mean to work to the standards of international companies?
It means every project follows defined protocols: code review, automated testing, security by design, change control, documentation, and regular progress reports. These are the practices our team applied for years at large-scale organizations, adapted to the size and objectives of your company. We apply the same practices when we use artificial intelligence: the tool changes, the standard does not.
How do you track project progress?
From the outset, we agree on a communication plan with regular meetings, demos of each delivery, and progress reports covering status, risks, decisions, and next steps. You have a designated point of contact and access to the project management tools.
Can you work with our in-house team?
Yes. We can join your team and adopt your tools, approval workflows, and policies, including your rules on AI use, or bring our own processes. In both cases, code review, automated testing, and progress reporting remain in place.
Want to know the state of an existing system?
Request a quality assessment: we review your code, tests, and security with AI support and deliver findings validated by our engineers, along with an improvement plan.