Our Services and Capabilities
IAI Architecture and Model Engineering
AI That Works on Your Own Company Data (RAG)Vector 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).Company-Specific 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.Measuring AI AccuracyComparative 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
Connecting Legacy ERP and CRM to AILegacy 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 in Business ProcessesTwo-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 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 Deployment on Your Own ServersClosed-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.Invoice and Document Automation(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.Decision Support and ForecastingSupport 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 and Private Cloud SetupIsolated 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 Monitoring and LLMOpsReal-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.AI Cost OptimisationCompute, Token and Infrastructure Cost OptimisationAs users and queries grow on production AI systems, hardware (GPU) and API token costs get out of hand.
VIndustrial Applications: Production, Quality and Warehouse
Predictive MaintenanceFailure Prediction and Maintenance Planning from Sensor DataMaintenance happens either too early — unnecessary downtime and parts — or too late, as unplanned downtime. The signals that precede a failure are already in the sensor data, but nobody looks: the data stays in the PLC or the SCADA archive while the maintenance team works to a calendar.Visual Quality ControlSurface, Assembly and Label Inspection with CamerasVisual inspection varies from shift to shift, and full inspection is not possible by hand. By the time a defective part reaches the customer it costs a multiple of what it cost on the line.Warehouse and WMS AutomationAutomating Counting, Put-Away and DispatchThe stock record drifts from what is on the shelf. Counting takes weeks, operations stop while it happens, and between two counts the record becomes steadily less trustworthy. Most dispatch errors come from the same root: the place in the system is not the place on the rack.Production Data Collection and MES IntegrationLine Data, OEE and the MES/SCADA ConnectionProduction data exists as three separate truths — in the machine, in a spreadsheet and in the ERP. Downtime reasons are typed in by hand, OEE comes out differently in every department, and the shift report barely makes it to the morning meeting.