Taqnify

Applied intelligence / data systems / distributed learning

Build intelligent systems without compromising your data.

We build production-ready AI systems, reliable data infrastructure and privacy-preserving distributed learning around your products, information and workflows.

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01Custom AI Systems
02Data Infrastructure
03Privacy-Preserving ML
04IoT & Edge Intelligence
05Secure Deployment

Three connected disciplines

Engineering beyond the AI demo.

Useful AI depends on dependable data, thoughtful integration and an architecture that respects where information can—and cannot—move.

Service 01

AI Engineering

Build intelligent systems around your products, data and workflows.

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Service 02

Data Engineering & Intelligence

Collect, structure and transform complex information into reliable, usable data.

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Service 03

Federated Learning & Privacy-Preserving AI

Build collaborative AI systems that learn from distributed data without requiring organizations or devices to centralize their raw information.

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Why work with us

Technical depth. Operational discipline.

We treat model choice as one decision inside a larger engineered system.

  • Solutions designed around real operational needs—not a predetermined model.
  • End-to-end engineering from architecture and data foundations to deployment.
  • Security, privacy and failure modes considered from the beginning.
  • Research-informed methods translated into usable, maintainable systems.
  • Clear documentation, measurable validation and code your team can operate.

Delivery method

A clear path from difficult question to working system.

  1. 01

    Discover

    Clarify the decision, workflow, constraints, risks and evidence of success.

  2. 02

    Design

    Choose an architecture and delivery plan grounded in your systems and operating reality.

  3. 03

    Build

    Implement in reviewable increments with tests, documentation and observable behaviour.

  4. 04

    Validate

    Evaluate quality, security, performance and failure modes against agreed criteria.

  5. 05

    Deploy & improve

    Integrate safely, monitor outcomes and evolve the system as requirements change.

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Where the work matters

Complex environments need more than generic software.

Healthcare

Sensitive clinical data, document workflows and multi-institution learning.

Cybersecurity

Distributed detection, secure automation and adversarial robustness.

IoT & Edge

Intelligence across constrained devices, intermittent links and diverse environments.

Finance

Governed data systems, risk workflows and privacy-aware collaboration.

Telecommunications

Distributed network data, edge workloads and operational intelligence.

Document operations

Extraction, classification and decision support across complex records.

Federated systems

Learn together. Keep raw data where it belongs.

Federated Learning changes where training occurs. It can reduce raw-data movement, while secure aggregation, update protection, privacy controls and governance shape the real security outcome.

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No claim of automatic privacy: architecture and threat modelling remain essential.

Client 01

Hospital

Raw data stays local

Client 02

Edge fleet

Raw data stays local

Client 03

Partner

Raw data stays local

Client 04

Branch

Raw data stays local

CoordinatedShared modelValidated updates
Conceptual flow: each client trains locally; controlled updates—not raw records—are validated and coordinated to improve a shared model. Additional privacy and security controls may still be required.

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Have a complex AI or data problem?

Bring us the operational constraint, difficult dataset or distributed-learning question. We’ll help define a credible next step.

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