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How We Work

At InData Labs, we put our clients at the heart of everything we do. Our approach 
to the AI development process is built on trust, transparency, and a commitment 
to delivering exceptional quality at every stage of the project. From the first consultation to final delivery, we focus on understanding your goals, tailoring solutions to your unique needs, and ensuring measurable results that drive your success

Trusted by Innovative Companies

Our Delivery Principles

We follow a structured yet flexible process that ensures timely execution without compromising quality. Every milestone is guided by clear communication, proactive risk management, and continuous improvement — so our clients receive reliable solutions that deliver lasting value.
  • Business outcomes first Start with KPIs and constraints, then pick the tech that delivers impact.
  • Fast value delivery Weeks, not quarters: tight iterations with demoable outcomes and metrics.
  • Production-grade from day one Built for reliability, latency, cost control, and scale.
  • Trust by design Security, compliance and full transparency.
Custom AI software development empowers businesses to innovate, automate, and scale like never before — turning data into intelligence and efficiency 
into growth.

AI Software Development Process

Explore the steps we follow to ensure your AI project succeeds.
  1. AI Process Discovery & Planning
    • Understand business goals and user requirements
    • Provide high-level project estimations
    • Offer technology and architecture recommendations
    • Conduct exploratory data analysis to assess feasibility
  2. Project Initialization
    • Choose the most suitable engagement model
    • Assemble a dedicated project team
    • Create a detailed project roadmap
    • Prepare technical documentation and specifications
  3. Development & Iteration
    • Custom AI model development
    • Follow Agile methodology for iterative progress
    • Share regular progress updates and demos
    • Conduct internal testing and quality checks at each stage
  4. Deployment & Launch
    • Perform thorough end-to-end system testing
    • Address any final issues before release
    • Deploy the solution to production
    • Gather user feedback and monitor initial performance
  5. AI Tech Support
    • Сonduct knowledge transfer for your team
    • Monitor servers and system performance continuously
    • Allocate resources for bug fixes, updates, and improvements
  • AI Process Discovery & Planning
  • Project Initialization
  • Development & Iteration
  • Deployment & Launch
  • AI Tech Support

Our Engagement Models

At InData Labs, we offer flexible engagement models to match your project’s scope, budget, and goals.
  • engine integration

    Time & Materials

    Flexible and transparent model for evolving projects with adjustable scope and priorities.
  • AI

    Fixed Price

    Clear timelines and costs from the start, perfect for well-defined project requirements.
  • Strong ML team

    Dedicated Team

    A handpicked team fully focused on your goals, offering long-term collaboration and scalability.

Why Work with us

  • AI
    10+ Years of Experience
    With over a decade in the AI and data science industry, we’ve honed our expertise across diverse domains and technologies.
  • icon five stars
    150+ Delivered Milestones
    Proven track record of successful project delivery, helping clients achieve measurable business impact at every stage.
  • Highly Experienced Team
    Top-Tier AI Engineers
    Our team of skilled data scientists, ML engineers, and analysts brings deep technical know-how and innovation to every solution.
  • security
    Governance & Security
    We follow strict data governance and security standards to ensure compliance, transparency, and protection of client assets.

Let Our Clients Do the Talking

  • Reviewed on Clutch
    Pavel Nurminskiy
    Pavel Nurminskiy
    Head of Machine Learning at Creative Research, Wargaming

    If you want to have a stable and efficient product, I recommend working with InData Labs.

    They created an anti-fraud solution for our company, implemented and improved algorithms, and collaborated to deliver a working product. InData Labs’ work helped us save a significant percentage of our marketing budget. Clients can expect a partner who excels at delivering products.

  • Reviewed on Clutch
    Ivan Akulovich
    Ivan Akulovich
    Project Manager of Business Development Department, AsstrA

    The competitiveness of InData Labs in the field of data science impressed us. More importantly, we learned many things from them.

    InData Labs built a new freight rates prediction software for our company. The increased quality of the data results we received dramatically improved our metrics. InData Labs did an excellent job and became our trusted data science partner.

  • Vishal Gurbuxani
    Vishal Gurbuxani
    Co-Founder & CTO, Captiv8

    Without InData Labs we wouldn’t have gotten all the exclusive data from social media that we offer to our customers today. With no doubt, I highly recommend InData Labs for any big data related projects.

  • Reviewed on Clutch
    Eudis Anjos
    Eudis Anjos
    Senior Engineering Manager of GSMA

    InData Labs completed the deliverables on time and met our expectations. They recommended improvements and shared ideas for new features. We appreciated the team’s friendly approach, engagement with the project and the friendliness.

    They were like part of my team, I had the confidence to reach any of the team members at any time. It was as if we were working in the same physical environment.

  • Reviewed on Clutch
    Brent McCarthy
    Brent McCarthy
    CEO & Co-Founder, Myka LLC

    Not only is the team fully capable of delivering what we want, but they also deliver in a timely manner.

    Thanks to our team at InData Labs, we were able to quickly correct all issues/bugs from our previous developers, while implementing a custom algorithm for our most pertinent feature of our app, artificial intelligence. They turned our app around from unusable to outstanding and marketable in a matter of months.

  • Reviewed on Clutch
    Andrew Kovzel
    Andrew Kovzel
    CTO, Flo: Smart Period Tracker

    As a growing company, we found InData Labs’ expertise in data science invaluable. In almost two years of our cooperation, they’ve helped us define our data analytics strategy, build a scalable data pipeline, and improve menstrual cycle predictions with a sophisticated neural network.

  • Reviewed on Clutch
    David Cairns
    David Cairns
    CEO & Co-Founder, Skorebee

    We have used InData Labs help us create not only the scoring models, but also build frontend and backend components which have all been completed with high quality, within expected timelines, and with clear visibility into ongoing status. The InData team think in terms of being a long-term partner…not just a provider, and I would recommend them to anyone who values intelligent, diligent & proactive development partners.

  • Reviewed on Clutch
    CEO and Co-Founder of an Spinout from the University of Copenhagen

    Their competence in data science, machine learning is second to none. The algorithms and methods were extremely well-explained and documented. We were likewise impressed by the friendly and proactive engagement we got from every member of the team. We’re a very small organization with a limited budget, but they always treated us like our problems and our business was of utmost importance. In short, we got the same level of service that a company 1000x our size would have gotten.

  • Reviewed on Clutch
    Head of Business Development, Corporate Start-Up

    Their drive was strong, and the whole team pushed their limits to meet deadlines and make everything work. Their strengths showed throughout our collaboration.

    Give them a try, even with small projects to test them out. They won’t disappoint you, and they’re very open about what they can and can’t do. I would recommend them to anyone.

FAQ

  • The process of developing AI generally follows these key steps:

    1. Define the Problem – Identify the goal and success metrics.
    2. Collect & Prepare Data – Gather relevant data, clean, label, and split it for training and testing.
    3. Develop the Model – Choose the right AI approach, train, and fine-tune the model.
    4. Evaluate & Validate – Test the model’s performance using appropriate metrics.
    5. Deploy & Monitor – Integrate into systems, monitor performance, and ensure scalability.
    6. Maintain & Improve – Continuously update the model with new data and optimize results.
  • The stages of AI development are:

    1. Problem Definition – Set goals and success metrics.
    2. Data Collection & Preparation – Gather, clean, and label data.
    3. Model Development – Train and fine-tune the AI model.
    4. Evaluation – Test performance with appropriate metrics.
    5. Deployment – Integrate into systems and monitor.
    6. Maintenance – Update and improve the model continuously.
  • Around 85% of AI projects fail due to a mix of technical and organizational challenges:

    1. Unclear Goals – AI applied without a real business problem or measurable success metrics.
    2. Poor Data Quality – Insufficient, biased, or siloed data hinders model performance.
    3. Lack of Skilled Talent – Teams often lack expertise to bridge AI and business needs.
    4. Deployment Issues – Models fail in real-world systems due to integration or scalability problems.
    5. Organizational Resistance – Low adoption, poor change management, or lack of training.
    6. Ethics & Compliance – Ignoring privacy, bias, or regulatory concerns can halt projects.
  • AI can be categorized into three main types based on capability:

    1. Narrow AI (Weak AI) – Designed to perform specific tasks very well, such as virtual assistants, recommendation engines, or image recognition. It cannot perform tasks outside its designated scope. Most AI today falls into this category.
    2. General AI (Strong AI) – Hypothetical AI that can understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. It would be capable of reasoning, problem-solving, and adapting to new situations.
    3. Superintelligent AI – A future concept where AI surpasses human intelligence in all areas, potentially excelling at creativity, problem-solving, and decision-making far beyond human capability.

    These types reflect the current state and future potential of AI, from specialized applications to systems that could think and act autonomously like humans—or beyond.

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