Trusted by Startups, SMBs and Fortune 500 Enterprises
Automate inventory counts, locate assets in real time, reduce losses and connect RFID events to ERP, warehouse, fleet and maintenance workflows with enterprise-grade dashboards and controls.
We design RFID asset tracking software around your users, workflows, data, integrations, security requirements and measurable production outcomes.
Track tagged assets, inventory, tools, containers and returnable equipment across sites and zones.
Combine RFID reads, zones, gateways and location rules to find assets and monitor movement.
Replace manual barcode counts with bulk reads, exception lists and scheduled cycle-count workflows.
Trigger alerts for missing, misplaced, overdue or unauthorized asset movement and route the next action.
Connect RFID events to SAP, Oracle, Dynamics, warehouse, CMMS, fleet and custom business systems.
Measure dwell time, availability, shrinkage, utilization, inventory accuracy and operational throughput.
Start with a focused workflow, a modernization programme, an integration or a complete platform. The architecture is selected for your operating model rather than a product demo.
End-to-end AI engagements, from strategy through to production systems.
Packaged AI products for recurring business workflows.
Agents already built and running in production, with the work documented.
Specialist AI engineers who join your team and ship inside your process.
AI-native developers working in modern agentic coding tools.
Our team handles discovery, architecture, experience design, engineering, integration, validation, deployment and production monitoring.
Identify where AI changes an outcome, size the value, check data readiness and sequence a roadmap before engineering starts.
LLM applications, assistants and content systems built on your data with prompt engineering, fine-tuning where justified, evaluation and guardrails.
Agents that plan, call tools and act across your systems, with permissions, error handling and approval gates so they can work unsupervised safely.
Connect AI to ERP, CRM, data warehouses, document stores and support desks so it works inside existing processes rather than beside them.
Pipelines, feature stores, deployment automation, evaluation harnesses, monitoring and retraining so models keep working after launch. Where the system should keep learning from outcomes rather than waiting for a scheduled rebuild, that is adaptive AI development.
Add intelligent search, document processing, assistants and automation to systems already in service, without a rebuild.
Controlled phases move the engagement from discovery and architecture through engineering, integration, testing, release and monitored operations.
Not every process benefits from AI. We map candidate use cases against business value, data readiness and delivery risk, then sequence them so the first release proves something real.
Most AI delays are data delays. We build the pipelines, cleaning and access controls that turn scattered systems into a dependable source for retrieval and training.
Model choice is a per-use-case decision. Around it we build the application, retrieval, tool calls, guardrails and evaluation that make output dependable enough to act on.
The value shows up when AI reads and writes to the systems people already use. We connect through APIs, webhooks and middleware, with the permissions your security team expects.
Deployment covers environments, secrets, access control, audit logging and rollback, plus the security review the system has to pass before it touches real data.
AI systems drift as data and usage change. We instrument quality, cost and latency, review the failures, and retrain or adjust rather than assuming launch-day accuracy holds.
Production systems need secure architecture, reliable integrations, measurable quality, observability and maintainable application engineering.
Assistants, copilots and language applications built on foundation models with retrieval, structured outputs and evaluation. The specialist page is LLM development; broader generative work including image, audio and multimodal is generative AI development.
Grounding answers in your own approved content with permission-aware retrieval, reranking and citation, so output can be traced to a source. Delivered as RAG development services.
Systems that act rather than answer: scoped tools, least-privilege permissions, approval gates and audit logging. Built as custom AI agent development, or agentic AI development where the work is genuinely multi-step.
Detection, classification, inspection and document extraction from images and scans, including edge and cloud inference. See computer vision development services.
Forecasting, scoring and anomaly detection built as services with feature pipelines, retraining and drift monitoring. See machine learning development, with advisory through ML consulting.
Connecting builds to ERP, CRM, warehouses and internal APIs with entitlements carried across the boundary through AI integration services, under controls defined by AI governance consulting.
Custom delivery makes sense when your workflows, integrations, data model, user experience or governance requirements create the operational advantage.
Each implementation is designed around the operating workflow, the people accountable for it and the systems that must exchange data.
Support assistants, multilingual chat and voice agents, and self-service that resolves rather than deflects — connected to order, ticket and account systems so answers are grounded in real records.
Turn contracts, forms, invoices and policy libraries into structured data and answerable knowledge, with permission-aware retrieval and citation of the source record.
Agents and automation that carry out multi-step operational work — validating, routing, scheduling and updating systems of record inside the rules you define.
Lead scoring, outreach assistance, forecasting and pipeline intelligence built into your CRM, so the model output lands where the team already works.
Automated quantity takeoff and estimating from drawings and specifications — measuring, counting and classifying scope so estimators price more bids without adding headcount.
Image and video intelligence for inspection, counting, condition assessment and compliance evidence, deployed at the edge or in the cloud depending on latency needs.
Real DreamzTech AI engagements, chosen to show the integration, governance and production complexity behind systems that people actually use every day.
Bulk-read tagged stock, reconcile exceptions and publish validated counts to warehouse and ERP systems.
Track trailers, tools and mobile assets across yards and facilities with zone-based alerts and utilization reporting.
Create timestamped movement histories, role-based handoffs and audit evidence for high-value or regulated assets.
DreamzTech pairs US-led engagement and project management with a global engineering organization, specialist access and overlapping working hours.
Engagement and project management run from the US, so scoping calls, demos, escalations and executive reporting all happen inside your working day rather than waiting on an overnight cycle.
Overlapping hours for the decisions that actually block progress — data access, integration questions, model behaviour reviews and UAT feedback.
Agreements, IP and source-code ownership defined up front under terms your legal and procurement teams are used to reviewing.
Three ways to work with us, depending on whether you need a partner to own delivery, a managed team alongside your product organization, or specific expertise added to engineers you already have.
concept to production
embedded specialists
named AI engineers
Bring the current process, systems, users, data and outcome you want. We will outline a secure, integrated and production-ready delivery path.
Share the use case and we will outline the architecture, integrations, delivery model and next practical step. NDA available.
Architecture choices are driven by fit, security, performance, integration, maintainability, data residency and cost at your operating scale.
| Category | Tools / technologies |
|---|---|
| Models | OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, plus open-weight models you can self-host |
| Orchestration | LangChain, LlamaIndex, custom agent frameworks, MCP tool servers |
| Retrieval & vector | pgvector, Pinecone, Weaviate, OpenSearch, Elasticsearch |
| ML & vision | PyTorch, TensorFlow, scikit-learn, Hugging Face, OpenCV |
| Data | Snowflake, Databricks, BigQuery, Redshift, Kafka, Airflow, dbt |
| Application | Python, Node.js, .NET, Java, React, Next.js, TypeScript |
| Cloud & MLOps | AWS Bedrock & SageMaker, Azure AI Foundry, Google Vertex AI, Docker, Kubernetes, CI/CD |
| Evaluation | Offline eval sets, human review workflows, tracing, drift and quality monitoring |
| Security | Role-based access control, encryption in transit and at rest, audit logging, SSO / MFA, environment separation |
Industry vocabulary, rules, data sources, user expectations and compliance obligations shape every production implementation.
Route optimization, demand forecasting, document AI, freight automation and AI agents, built on top of the logistics software and transportation management systems you already run, so planners keep one source of truth instead of a parallel AI tool.
Case study: AI agent handling logistics customer service across social and direct messages
Predictive maintenance, computer vision quality inspection, production forecasting and anomaly detection, wired into the manufacturing software and MES, ERP and historian data on the plant floor. See how we apply AI agents in manufacturing operations.
Case study: Parts inventory and MRP intelligence for a manufacturer
Document intelligence, workflow automation, patient engagement and RAG knowledge systems, delivered inside healthcare software with HIPAA-aligned handling, audit trails and clinician review built in. More on our approach to AI in healthcare software.
Case study: AI prior authorisation agent for a healthcare provider
Fraud detection, document processing, financial copilots and risk analytics for fintech platforms, with explainable decisions, model monitoring and the audit trail your compliance team will ask for. See our work on AI agents in finance.
Case study: AI invoice processing for a financial services firm
Recommendation engines, demand forecasting, customer-service agents and inventory optimization across retail software, POS and ecommerce data, so pricing, stock and merchandising decisions use the same signals. See AI agents in retail.
Case study: AI inventory forecasting that cut stockouts for a retail chain
Predictive maintenance, technician copilots, work-order automation and asset intelligence layered onto facility management software, CAFM and CMMS data, so planners and technicians act on the same asset history. See our CAFM and SAP integration work.
Case study: Intelligent CMMS platform for facility management
AI concierge, revenue intelligence and customer-service automation for hotels, restaurants and multi-site groups, connected to PMS, POS and booking data through our travel and hospitality software practice.
Case study: AI-powered restaurant POS and operations platform
AI takeoff, document intelligence, estimating and project-data analysis for contractors and specialty trades. Our AI takeoff software reads drawings, counts and measures scope, and feeds quantities straight into the estimate.
Case study: AI takeoff that let an electrical contractor bid 40% more work
Tell us what happens today, who uses it, which systems are involved and what a good outcome looks like.
We define the solution approach, integrations, controls, milestones, acceptance criteria and delivery estimate.
Release a focused production workflow, measure the outcome and expand based on real operating evidence.
Verified client feedback consistently highlights responsiveness, practical problem solving, communication and delivery quality.
We combine US-facing delivery leadership with the product, data, cloud, integration, security and operations engineering needed for production.
Engagement and project management run from the US, so planning, demos and escalations happen in your working day.
Most AI projects fail on integration, data and operations rather than modelling. Those are the disciplines we bring.
Ownership terms are defined in the engagement agreement, so you retain the codebase, documentation and roadmap.
Access control, audit logging, environment separation and human review designed in from the start, not added before launch.
Share the workflow, users, systems and target outcome. We will help determine the right architecture, scope and delivery plan.
Direct answers about scope, architecture, integration, security, ownership, cost drivers and delivery.
RFIDTracks is RFID asset tracking software that uses RFID tags and readers to automatically locate, count and monitor physical assets in real time. It bulk-reads tags with no line of sight, updates inventory automatically, and provides multi-location dashboards, alerts and analytics from one platform.
Barcodes must be scanned one at a time within line of sight. RFID reads hundreds of tags at once from a distance, so counts and audits that took hours finish in minutes — with far fewer errors and no manual line-of-sight scanning.
An IoT sensor detects an item entering an RFID zone and triggers the reader; the reader identifies the tagged items in milliseconds; output devices give green or red feedback; automation logic validates the reads against expected records; and clean data syncs to your ERP for real-time visibility.
RFIDTracks works with leading hardware including Zebra and Chainway handhelds, sleds and fixed readers, plus IoT sensors and gateways — reliable across warehouses, factories, stores and outdoor areas.
It offers ERP-ready integrations with SAP, Oracle NetSuite, Microsoft Dynamics and Odoo, accounting tools such as QuickBooks, Xero, Sage and TallyPrime, and IoT and inventory platforms — pushing clean data straight to your ERP or WMS.
Yes. RFIDTracks is AWS-hosted with enterprise security and industry certifications including ISO 27001, SOC, PCI and SOX, plus roles and permissions and full audit logging for accountability.
Teams using RFIDTracks report up to $130M saved in annual cost, 95% fewer inventory discrepancies, and 4x faster daily operations and tracking through automated, hands-free asset management.
