Software and artificial intelligence solutions for industry
Tech Fabrika builds custom software and artificial intelligence systems designed around what your business actually needs.
We build systems with software and AI
A good custom software project is far more than building a few screens and a backend.
Business processes, users, data, integrations, security, performance and future growth all have to be thought through together.
Tech Fabrika teams handle projects end to end:
- Discovery
- Product Design
- Architecture
- UX/UI
- Development
- AI
- Integration
- Testing
- DevOps
- Go-Live
- Scaling
From the first prototype to a production architecture that can handle millions of transactions, we work with the same engineering discipline.
IAI Architecture and Model Engineering
Enterprise RAG ArchitectureVector Database (Vector DB) IntegrationStandard large language models cannot reach a company’s own data, go stale over time, and answer questions with reasoning that is not grounded in fact (hallucination).Language Model TrainingGeneral-purpose models fall short against the jargon of specific sectors (law, medicine, finance, manufacturing and so on) or against company-specific business logic, and running general models in production brings high API and hardware costs.Metric-Driven EvaluationComparative Model Testing InfrastructureModel performance is judged by surface impressions, and once systems are in production their accuracy and safety rates cannot be tracked analytically.
IISoftware Adaptation, Legacy Systems and Microservice Architecture
Intelligent API LayersLegacy System ModernisationThe legacy ERP, CRM, mainframe or bespoke database systems that businesses have run for years — and that sit at the centre of their workflows — cannot technically accommodate AI integration.AI and BusinessTwo-Way AI Integration with Business Software Across DepartmentsAI stays a static chat window that only produces text and never turns into action inside the business software already in use (HR, sales, operations, finance).AI-Backed Microservicesand Distributed System Architecture DesignMonolithic AI applications collapse under heavy user load or sudden traffic spikes, cannot scale, and cause system-wide deadlocks.
IIIProcess Automation, Autonomous Agents and Workflows
LLM DeploymentClosed-Circuit (On-Premise / Private Cloud) LLM Deployment and Data Security LayersIn regulated sectors such as finance, healthcare, defence and law, sending sensitive data to third-party APIs creates legal risk in terms of data security and GDPR/KVKK.Intelligent Document Processing(IDP) and Complex Data PipelinesIn sectors such as logistics, customs, insurance and finance, the thousands of documents arriving daily in every conceivable format (PDFs, scans, handwriting, complex tables) must be keyed into systems by hand — a loss of both time and money.Department-Level DecisionSupport and Forecasting ModulesDecision-makers lose time picking strategic choices out of large piles of data, and the analysis stays stuck in what already happened.
IVSecurity, Data Privacy and LLMOps / Production Management
On-Premise / Private CloudIsolated Infrastructure Topology, Access Control and Proof of ComplianceInstalling the model inside the company is not security by itself. If the network it runs on, the model and embedding files on disk, the keys between services and the query logs go unaudited, data can leak inside the company too. In an audit, saying "the data never leaves" is not enough; you are asked to prove which user reached which document.Model MonitoringReal-Time LLMOps: Model Monitoring, Guardrails and Rollback InfrastructureModels in production lose performance over time (model drift), produce harmful or inappropriate answers (prompt injection), and none of it can be acted on the moment it happens.Token and InfrastructureCompute, Token and Infrastructure Cost OptimisationAs users and queries grow on production AI systems, hardware (GPU) and API token costs get out of hand.