Technical software

Technical software, calculation engines and advanced data analysis.

Artik Lab develops advanced software for clients when the problem cannot be solved by a dashboard or a standard business application: technical calculations, mathematical models, operational data, expert rules and workflows that need to become verifiable.

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When it matters

When company know-how is too important to remain in spreadsheets, legacy code or the heads of a few experts.

Many industrial companies run on calculations, exceptions and technical decisions that have grown over time. Sometimes they live in fragile spreadsheets, sometimes in obsolete software, sometimes in procedures known only by long-time users. The service turns that knowledge into readable, testable and transferable systems.

What can be built

Systems that make repeatable what now depends on experience, files and manual checks.

Value comes from combining software engineering, data analysis and expert knowledge formalisation. The outcome is not a demo prototype, but a system with acceptance criteria, tests, documentation and clear boundaries.

  • Important calculations depend on undocumented files that are hard to verify.
  • A technical application still works, but nobody wants to change it anymore.
  • Operational data exists, but it does not yet guide priorities, anomalies or forecasts.
  • Technical decisions depend on a few experts rather than on a shared system.
  • Leadership needs to invest but lacks a clear technical dossier on risk, value and feasibility.

Calculation and verification engines

Deterministic algorithms for technical calculations, checks, scenarios, simulations and repeatable verification.

Data systems and advanced analysis

Collection, normalisation and reading of operational data to detect anomalies, patterns, priorities and risks.

Legacy software modernisation

Code audit, reconstruction of business logic, parsers for historical formats and progressive rewrite.

Interfaces, reports and APIs

Tools for technical offices and operational teams: decision dashboards, reports, exports and integrations.

Method

From technical process to verifiable system.

Not generic software. Software that embeds domain knowledge, mathematics and responsibility.

  1. 1
    Technical audit

    Read the existing system: data, formulas, flows, dependencies, known errors and operational risk.

  2. 2
    Domain formalisation

    Expert rules become entities, constraints, assumptions, edge cases and decision criteria.

  3. 3
    Verifiable architecture

    The calculation core is separated from interfaces, reports and AI components, so it remains controllable.

  4. 4
    Computable prototype

    Build a small complete flow: source data, data model, calculation, verification and usable result.

  5. 5
    Validation

    Automated tests, synthetic cases, regression and comparison with known references measure differences and risks.

  6. 6
    Production

    The system becomes usable through interfaces, APIs, reports, documentation and maintenance responsibilities.

Entregables

What remains inside the company.

  • Technical blueprint with architecture, risks, data, assumptions and open decisions.
  • Structured knowledge base with operating rules, constraints, sources and confidence levels.
  • Calculation engine, data system or technical application with automated tests.
  • Verification dossier with discrepancies, tolerances, acceptance criteria and remediation priorities.
  • Reports, interfaces or APIs to integrate the system into real work.
  • Roadmap in progressive work packages, with testable outputs and technical checkpoints.

Anonymised examples

Typical problems the service can address.

Technical spreadsheets grown over years

A technical office uses complex files for recurring decisions. Formulas are hard to verify and every change requires historical memory. The project reconstructs rules, turns them into a data model and adds tests to prevent regressions.

Legacy software that is hard to maintain

A critical application still works but depends on old technology and undocumented logic. The work starts from audit, separates what must be preserved from what must be redesigned and builds a progressive rewrite with result comparison.

Industrial data not yet used for decisions

The process produces data, but the company mainly uses it for retrospective reporting. The analysis looks for signals for operational priorities, anomalies, forecasts and control decisions, also stating when the data is not enough.

Expert knowledge concentrated in a few people

Some decisions depend on key roles' experience. The project makes rules, exceptions and warning thresholds explicit, so knowledge remains available when people, tools or work volumes change.

AI atlas

Before choosing the format, recognise the process.

The Atlas gathers concrete AI application examples across documents, operations, HR, marketing, software, governance, production, training and data. It helps decide whether the need requires consulting, data analysis, technical development or training.

Open the Atlas

Controlled AI

AI can help, but the technical core must remain explainable.

In technical systems, opaque components should not replace verifiable calculation. AI can help explore data, explain results, propose scenarios, read documents or assist the user. The deterministic core, domain rules and tests remain the control point.

FAQ

FAQ

Is this generic software development?

No. It is designed for problems that require technical domain knowledge, data, mathematics, algorithms, tests and verification criteria.

Do specifications need to be complete already?

No. Often the first task is to reconstruct specifications, rules, assumptions and edge cases from the existing system and expert users.

Does AI decide instead of technicians?

No. In technical contexts AI is used as support. Critical parts remain explainable, tested and under human responsibility.

How is know-how protected?

The project works with agreed boundaries, access, data and materials. Public examples use only anonymised descriptions that cannot identify the client.

Contact

A short conversation is enough to understand where to start.

The first 30-45 minute call clarifies the process, goal, available data, constraints and next useful step: training, consulting, data analysis or a controlled technical prototype.

Write to dtr@ar-tik.com