HAYATEN Data Lab

Data, AI and software,engineered to be trusted.

HAYATEN Data Lab designs, builds and runs the systems behind better decisions, for businesses that cannot afford guesswork.

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Free 30-minute call
to scope your problem
NDA first
before you share any detail
Two business days
to reply with next steps

Most companies already own the data they need. It sits in systems that do not talk to each other, so decisions wait on spreadsheets and people. We connect it, clean it and put AI to work on it, with engineers accountable for every step.

Six areas, one engineering team

Each engagement is scoped by people who understand the technology and delivered by specialists chosen for the work in front of them.

01 of 04

Work is changing

AI is taking over routine work such as reading documents, sorting requests, drafting replies and spotting patterns. People move to the parts that need judgement.

02 of 04

Data is the fuel

AI is only as good as the data beneath it. Clean, connected and well-governed data comes first. Moving old systems to the cloud is one step on that road, not the destination.

03 of 04

AI inside real workflows

The value is in connecting AI to the systems people already use, with testing, measurement and human review, not in a demo that sits on its own.

04 of 04

Engineers make it trustworthy

Someone must design the architecture, check the results, secure the data and be accountable when something goes wrong. Software engineers turn AI into a dependable system.

Where we focus right now

Trusted data foundations for AI

Many AI projects stall on data that is scattered, inconsistent and ungoverned. We design the pipelines, quality checks and governance so the data behind your AI can be trusted.

AI-native software engineering

AI can speed up writing code, but speed alone does not make a team productive. We build with AI assistance while keeping review, testing and security in place.

Automation people can supervise

Agents and workflows can take on routine tasks, but only where someone can see what they did. We build in logging, approvals and human review.

Built to be watched, not guessed at

Every pipeline we build reports on itself. This is the kind of view your team gets.

Data platform health  Last 24 hours Sample values for illustration, not client data
Pipelines on schedule 98.6%
Rows checked today 4.2M
Data-quality alerts 3
Median report freshness 14 min

The tools behind modern data and AI

We work across the open-source and cloud stack, and choose what fits your existing systems rather than forcing a new one. Drag the sphere to turn it.

  • AI
  • Machine Learning
  • Deep Learning
  • Data Engineering
  • Data Science

Tools we work with include Python, SQL, PySpark, Airflow, dbt, Snowflake, Databricks, BigQuery, Redshift, Kafka, Iceberg, Delta Lake, Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Keras, Hugging Face, LangChain, MLflow, Ray, ONNX, CUDA, OpenCV, Jupyter, Matplotlib, PostgreSQL, MongoDB, Redis, Elasticsearch, Docker, Kubernetes, Terraform, AWS, Azure, Google Cloud, Linux, Git, Jenkins, Grafana, Prometheus, FastAPI, Django, Flask, Node.js, React, TypeScript, Go, Rust, Java, Spring, C++, GraphQL, gRPC, OpenAPI, RabbitMQ, Nginx, Pytest, Selenium, Hadoop, Power BI, Tableau, Azure Data Factory.

How we earn trust

No logos or numbers to hide behind. These are the commitments we work to on every engagement.

Scope in writing

Every engagement starts with an agreed scope, milestones and price, so there are no surprises later.

Confidential from day one

We sign your NDA before you share anything, and limit access to the people on your project.

You own the work

Custom code and documentation we deliver belong to you once paid, as set out in the agreement.

Tested and documented

Code is reviewed, pipelines are checked for data quality, and everything is documented for handover.

Honest advice

If a project is not a good fit, or a simpler option exists, we will tell you.

Reachable people

You deal directly with the engineers who scope and build your project, not a call centre.

Clear stages, no surprises

  1. Discover

    We learn your data, systems and goals, and agree scope and measures of success.

  2. Design

    We propose an architecture and a delivery plan with fixed milestones.

  3. Build

    Small tested releases with regular demos, so you steer while change is easy.

  4. Operate

    We monitor and improve the system, or hand over cleanly with documentation.

Straight answers

The questions buyers ask us first. More on the FAQ page.

Who owns the code?

Custom work delivered to you belongs to you once paid, as set out in the written agreement. We state any open-source or pre-existing components up front.

Do you sign NDAs?

Yes. We sign yours before you share any detail, or provide ours.

How is pricing set?

Fixed price for well-defined scope, with payments tied to milestones. Time-and-material for team augmentation. Estimates are written, and any change is agreed before it affects cost.

Which time zones do you work in?

We agree working hours that overlap with yours for meetings and decisions, and communicate in clear written English.

How do we start?

A free 30-minute call, then usually a small fixed-scope first piece such as an audit or pilot, so you can judge our work before committing more.

Ready for your data?

Tell us what you are trying to fix or build. We reply within two business days.

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