How We Keep Your Project On Time, Secure, and Easy to Maintain

The technology we use, how we choose it, and the practices behind every DESOL project, with the evidence you receive at each step.

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Every project passes four checkpoints Select a checkpoint to read how it works
Your idea
Technology Proven stacks, chosen for your problem .NET Java React Cloud Quality Every change reviewed and tested Code review CI tests Sprint demos Security Protection built into every stage NDA Access control Masked data Responsible AI AI inside the limits you set Human review No lock-in
DESOL Live and easy to maintain
Technology Stack

Proven Technology, Layer by Layer

Widely adopted, well-supported technology at every layer, so your system is easy to hire for, maintain, and extend.

Application
FrontendWeb interfaces that work in the browser on desktop and mobile.
BackendBusiness logic, APIs, and integrations built for long-term maintenance.
DataRelational and document databases chosen for your data and scale.
Intelligence
AI & LLM Established AI platforms, connected to your data and systems. We build on these platforms; we do not train our own models. Models Retrieval & orchestration
Cloud & DevOpsEvery layer runs on major cloud platforms, packaged in containers with infrastructure defined as code.
How We Choose Technology

Chosen for Your Problem, Not for Trends

Every recommendation is checked against four questions, and you see the reasoning behind each choice before any work starts.

Your situation Number of users Your data Budget Existing systems Your team
Four questions
StableWidely used, with a large community and long-term support?
ScalableCan it grow with your users and data without a rebuild?
Cost-efficientAre licensing, infrastructure, and running costs reasonable?
Fits your teamCan your team take it over and maintain it after handover?
What you receive Recommended stack Reason for each component Estimated running cost Alternatives, where relevant
Engineering Quality

From First Line of Code to Live Operation

Every change follows the same path: reviewed, tested, deployed automatically, and monitored once it is live.

  1. Code Shared coding standards; every feature on its own branch. Git Conventions
  2. Review Another engineer reviews every change before it is merged. Pull requests
  3. Test Unit, integration, and UI tests run automatically on every commit. Unit Integration E2E
  4. Build Containerized, with infrastructure defined as code for identical environments.
  5. Deploy Automated releases to the cloud, with one-step rollback.
  6. Monitor Errors and performance tracked, with early alerts. Logs Metrics Alerts

Issues and metrics from monitoring feed into the next sprint

What you receive Test results every sprint Access to the CI pipeline A working demo of each release
Security & Data Protection

Security Built into Every Stage

Your code, data, and systems are protected from the first conversation to the final handover.

  • ConfidentialityAn NDA is signed before you share any business details or documents.
  • Access controlLeast-privilege access with individual accounts, removed when the project ends.
  • Secure codingCode reviews check for common security risks; dependencies are checked for known vulnerabilities.
  • Data handlingWe work with test or masked data. Production data is used only with your approval.
  • Separated environmentsDevelopment, staging, and production are kept apart, with secrets stored securely, never in code.
  • Clean handoverSource code, documentation, and credentials are handed over in full, and our access is removed.
What you receive Signed NDA Access list for every account Handover checklist, with our access removed
Responsible AI

AI That Works Within Your Boundaries

AI agents, knowledge assistants, and document processing all share one architecture: your data stays under your control, results are traceable, and people stay in charge.

Within the boundaries you control
  1. Input Your data Documents, databases, and the systems you already use. Documents Databases APIs
  2. Retrieve Search & index Only the data needed for each request is retrieved.
  3. Generate AI model Answers and actions are produced from the retrieved data.
  4. Approve Human review People approve important steps before anything is changed. Approval Audit log
  5. Output Result with sources Every answer links back to where it came from. Citations
  • Your data stays in your controlData is used only for your requests and is not used to train public models.
  • Every answer has a sourceResults cite the documents or records they were based on, so your team can check them.
  • People approve key stepsAI suggests; your team approves anything that changes data or reaches customers.
Deployment options:
Enterprise AI servicesManaged AI platforms with business data protection. Your own infrastructureModels run inside your cloud or servers. HybridSensitive data stays in-house; other tasks use managed services.
What you receive An accuracy report on your own data before go-live Usage and cost monitoring after launch

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