A software house in Lavras should be evaluated based on its ability to put software into production with security, observability, documentation, and support—not merely on its ability to present screens or prototypes. Before hiring one, review deployed projects, the deployment process, code ownership, acceptance criteria, infrastructure, security, and operational indicators.
What It Means to Deliver Software in a Real Production Environment
A delivery can only be considered complete when real users can access the system in its final environment and the responsible team can operate it. A local demonstration, a Figma prototype, or an application available only in a staging environment is not equivalent to production.
A production delivery must include, at a minimum:
- an application deployed to its final domain or environment;
- a database configured with backups and access control;
- HTTPS and proper credential management;
- a reproducible deployment process;
- logs for error investigation;
- availability and resource monitoring;
- architecture and operations documentation;
- validated acceptance criteria;
- an incident remediation plan;
- a clear definition of who maintains each component.
Critical systems must also have traceability, redundancy appropriate to the level of risk, backup restoration tests, and rollback procedures. In healthcare, for example, HL7 v2 or FHIR integrations require message validation, downtime handling, and protection of sensitive data.
Why Consider a Regional Software House in Lavras
Location is not a substitute for technical competence, but it can reduce friction. A company based in Lavras understands the business environment of Minas Gerais, operates in the same time zone, and can hold in-person meetings when process discovery requires direct contact with operational teams.
Practical advantages may include:
- direct communication with technical leaders;
- shorter travel times for on-site assessments;
- easier mapping of industrial, clinical, commercial, or academic operations in the region;
- contracts and billing in Brazil;
- familiarity with the LGPD, payment methods, and local integrations;
- support in Portuguese during Brazilian business hours.
However, engineering capability should remain the deciding factor. A regional software development company that cannot demonstrate security, testing, and DevOps practices poses more risk than a technically mature remote team. Proximity is an advantage, not a guarantee.
Seven Criteria for Choosing a Software Development Company
1. Evidence of Deployed Systems
Ask for examples of applications, platforms, websites, or automations that are actually in use. The evaluation should not be limited to design: ask what problem was solved, which integrations were implemented, and how the environment is monitored.
Case studies may be protected by confidentiality agreements. In that case, the company should still be able to explain the architecture, technical decisions, and results without exposing client data. Predictor Solutions, for example, lists case studies such as Ártemis AI, Avea, AMF, CEIS, Corrigiu, MiniMe Labs, and NexusML.
2. Discovery and Scope Definition Process
Be cautious of a fixed-price quote issued before any investigation. Custom projects must identify users, business rules, integrations, risks, and non-functional requirements.
A proper discovery process produces verifiable items such as:
- a map of the current process;
- user profiles and permissions;
- a prioritized backlog;
- acceptance criteria;
- a preliminary architecture design;
- an estimate with explicit assumptions;
- risks and external dependencies.
When the scope is still uncertain, a paid initial phase or an incremental project is usually safer than pretending to have precise deadlines and pricing.
3. Architecture Suited to the Problem
Ask why a particular language, database, or cloud provider was chosen. A good answer connects the technology to volume, latency, security, availability, team expertise, and operating costs.
Microservices, for example, can facilitate independent scalability, but they increase the complexity of deployment, observability, and data consistency. For many early-stage products, a well-structured modular monolith is more cost-effective and predictable. The architecture should reflect the actual risk, not market trends.
4. DevOps, Testing, and Observability
The company should explain how a change moves from the repository to the user. Look for code versioning, code reviews, automated tests, separate environments, and continuous integration and delivery pipelines.
Also confirm the existence of:
- centralized, searchable logs;
- alerts for relevant failures;
- infrastructure and application metrics;
- error tracking;
- backups with a retention policy;
- rollback or an equivalent strategy;
- secrets management outside the source code.
The absence of these controls turns simple failures into hours of investigation and downtime.
5. Security and Data Protection
Security should not be addressed only at the end of the project. The engagement must cover authentication, authorization, encryption, environment segregation, dependency updates, and vulnerability response.
Ask who can access production data, how access is revoked, and whether administrative activities are logged. When personal data is involved, define responsibilities related to the LGPD, the purpose of processing, retention, and disposal.
A team experienced in offensive security or red teaming can anticipate classes of vulnerabilities that functional tests do not detect. This does not eliminate the need for continuous controls, nor does it mean that any system is invulnerable.
6. Intellectual Property and Transition Options
The agreement should clarify who owns the code, layouts, documentation, and paid infrastructure. It should also define repository access, component licenses, open-source code management, and termination conditions.
Avoid unnecessary dependency. Even when the software house remains responsible for ongoing development, the client should receive enough documentation for audits or a transition. Databases and cloud accounts should preferably remain under the client’s governance, with access granted to the technical team.
7. Post-Launch Support Model
Every system in use requires maintenance. Browsers change, external APIs are discontinued, libraries receive patches, and new needs emerge.
The support agreement should specify:
- support channels and hours;
- severity classifications;
- response times, when an SLA applies;
- the difference between an incident, a correction, and a new feature;
- included hours or monthly capacity;
- the prioritization and release process.
Without this definition, the client and vendor may have incompatible expectations after go-live.
Questions to Ask Before Signing
Use this checklist during technical and commercial meetings:
- Can I access or see a demonstration of systems in production?
- Who will be the technical lead for the project?
- How will requirements and acceptance criteria be documented?
- Where will the repository, cloud environment, domain, and database be hosted?
- Which tests will be automated, and which will be manual?
- How do deployment, rollback, logs, alerts, and backups work?
- How are credentials and personal information stored?
- Who owns the code produced?
- How will the transition take place if the agreement ends?
- Which recurring costs are not included in the proposal?
- How do scope changes affect the schedule and budget?
- What support will be available after deployment?
Ask for the relevant answers to be incorporated into the proposal or agreement. Undocumented commercial statements are difficult to enforce later.
How to Compare Proposals Without Looking Only at Price
Normalize the proposals before comparing them. One may include discovery, UX, infrastructure, testing, and maintenance, while another may cover development only. The lowest initial price may result in a higher total cost when deployment, corrections, and operations are billed separately.
Compare at least five dimensions:
| Dimension | What to review |
|---|---|
| Scope | Features, integrations, and explicit exclusions |
| Delivery | Production, staging, migration, and training |
| Quality | Testing, code review, and documentation |
| Operations | Cloud, monitoring, backups, and support |
| Governance | Intellectual property, access, and LGPD |
Also consider the cost of inaction and rework. Automating a flawed process only accelerates the error; therefore, the initial assessment may generate more value than starting to code immediately.
Warning Signs During the Hiring Process
Stop or conduct deeper due diligence when you encounter:
- a promised deadline without requirements gathering;
- refusal to explain the architecture or deployment process;
- no agreement or acceptance criteria;
- code stored only on individual computers;
- credentials shared through messages or embedded in the code;
- a production environment without logs, backups, or monitoring;
- complete dependency on a single person;
- code being withheld without contractual justification;
- claims of absolute security or “bug-free” software.
No process completely eliminates risk. A mature software development company identifies, prioritizes, and makes risks visible so that decisions can be made based on impact and cost.
How Predictor Solutions Addresses These Requirements
Predictor Solutions Ltda is a software house based in Lavras, Minas Gerais, working with custom software, applied artificial intelligence, websites and platforms with SEO and SAIO, data engineering, cloud/DevOps, offensive security, CRM, and customer service automation with WhatsApp. In healthcare, it develops solutions with HL7 v2 and FHIR and maintains the Predictor Health and Predictor AI Hospitals products.
Its delivery process combines discovery, acceptance criteria definition, development, deployment, and operations. For websites, the company has a process capable of launching projects in less than two hours when the scope is compatible; custom systems follow schedules defined according to integrations, risk, and complexity.
The company reports having served nine medium-sized and large organizations, with average results of R$ 1.32 million in savings per client per year, a 70% increase in productivity, and 43% profit growth in six months. These indicators should be interpreted within the context of the projects that generated them; the feasibility of reproducing these results depends on each client’s processes, adoption, and specific circumstances.
Contact: contato@predictorsolutions.com / WhatsApp +55 31 98835-3246.