Capabilities / Systems, not silos

Deep enough for the model. Broad enough for production.

Tandemora combines applied AI, product engineering, and cloud operations so the critical path stays owned from evidence to handover.

Azure-first by default · portable where it matters

10Capability domains

60Implementation surfaces

01Accountable delivery system

Product discovery and UX/UI

Turn an ambiguous opportunity into a workflow, product boundary, evidence plan, and interface people can actually operate.

Related solution

AI opportunity and feasibility discovery
User and operational workflow mapping
Product requirements and acceptance criteria
Interaction design and rapid prototypes
Design systems and responsive interfaces
Usability evidence and rollout planning

Custom web, SaaS, and business applications

Design and ship complete browser products and purpose-built business systems around the exact workflow, users, data, and operating model.

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Custom business and operational applications
SaaS product architecture and delivery
Responsive accessible web interfaces
Authentication, permissions, and billing
Internal tools and administration surfaces
Real-time dashboards and control workflows

Mobile and desktop applications

Build dependable software for customer, field, workforce, engineering, and performance-sensitive workflows across devices and operating systems.

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Native and cross-platform mobile apps
Offline-first and synchronization behavior
Notifications, peripherals, and device APIs
Desktop and engineering applications
Performance-sensitive visualization and data
Distribution, updates, and release management

Backends, APIs, and distributed systems

Engineer the services, events, integrations, consistency, and failure handling behind high-volume products and operational applications.

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Backend services and domain architecture
APIs and enterprise integrations
Event-driven and asynchronous workflows
Distributed consistency and idempotency
High-volume processing and performance
Failure recovery, retries, and auditability

Azure, DevOps, and reliability

Create the cloud, delivery, observability, security, and recovery foundation required to operate the complete software system.

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Azure architecture and infrastructure as code
CI/CD and release automation
Observability, tracing, and alerting
Availability, scaling, and performance
Cost, access, and environment controls
Rollback, backup, and disaster recovery

Generative AI, agents, and speech

Build controlled language and conversational systems that use knowledge and tools while preserving permissions, review, latency, and recovery.

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Model selection and private deployment
Tool use and agent orchestration
Prompt, context, and state architecture
Voice, transcription, and audio workflows
Guardrails and human escalation
Latency, token, and cost optimization

Knowledge and document AI

Make internal knowledge and business documents searchable, structured, validated, and usable inside accountable workflows.

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Retrieval and RAG architecture
OCR, layout, and document classification
Field and entity extraction
Citation and provenance tracking
Business-rule validation and matching
Human review, audit, and export

Computer vision and edge inference

Convert images and video into measurable events under real camera, environment, latency, hardware, and privacy constraints.

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Detection, segmentation, and tracking
Visual inspection and defect analysis
Temporal event and zone logic
Camera and scene specification
Edge inference and model optimization
Quality evaluation across difficult conditions

Predictive ML, optimization, and simulation

Create forecasts, scores, recommendations, early warnings, and optimized plans tied to real decisions, constraints, and error economics.

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Demand and time-series forecasting
Classification and operational scoring
Anomaly and early-warning systems
Recommendation and ranking systems
Routing, scheduling, and allocation
Scenario, uncertainty, and what-if simulation

Data engineering, evaluation, and MLOps

Create a repeatable route from governed data and experiments to evaluated, observable, cost-controlled model releases.

Related solution

Data ingestion and quality contracts
Experiment and evaluation frameworks
Model registries and lineage
Deployment and rollback automation
Drift, regression, and cost monitoring
Retraining and release governance

Current boundary

Practical models over open-ended research.

We favor pretrained models and selective small-model adaptation. We do not sell speculative foundation-model training, standalone cybersecurity, brochure websites, or founder-provided 24/7 support.