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Innovaith Solutions

How we work

AI for speed. People for precision. Checks for confidence.

Every project follows the same six steps. AI takes the repetitive first pass, trained people review and correct it, and independent checks happen before anything is delivered. Here is what happens at each step, and what you get.
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The workflow

Six steps, with clear hand-offs between AI and people.

The labels show who does each step. AI never has the last word: people review its work, and a separate check comes before delivery.
  1. 01
    People

    Understand

    We learn what you need and what good looks like.

  2. 02
    People

    Prepare

    We organise the inputs and set up the work.

  3. 03
    AI

    AI Assist

    AI takes the first, repetitive pass.

  4. 04
    People

    Human Review

    Trained specialists check and correct the work.

  5. 05
    Checks

    Quality Control

    Independent checks before anything leaves.

  6. 06
    People

    Deliver

    Ready-to-use output, in the format you need.

  • AI

    AI provides speed.

    Models take the first pass on repetitive work, so large volumes move faster than they would by hand.

  • People

    Human expertise provides precision.

    Trained specialists review and correct the output, and handle the cases a model cannot judge on its own.

  • Checks

    Quality control provides confidence.

    Independent checks against targets you agreed, with the results written down before anything is delivered.

Step by step

What happens, and what you get.

No black box. At every step you know what we are doing and what you will have at the end of it.

  1. Step 01

    Understand

    People

    What happens

    A short call and a look at sample data or requirements. Together we agree the goal, the rules, the output format and how quality will be measured.

    What you get

    A written scope with clear acceptance criteria.

  2. Step 02

    Prepare

    People

    What happens

    Data is cleaned and organised, guidelines are written with examples and edge cases, and the tools are configured. A small pilot tests everything before full volume.

    What you get

    Guidelines you can review and a pilot you can check.

  3. Step 03

    AI Assist

    AI

    What happens

    Models pre-label images and video, draft transcripts and text labels, or generate code and tests. This is where the speed comes from, and nothing stops here.

    What you get

    Large volumes moving quickly, ready for review.

  4. Step 04

    Human Review

    People

    What happens

    People review the AI's output item by item, fix its mistakes and make the judgement calls models get wrong: unclear images, unusual cases, context that needs a person.

    What you get

    Precision on the details that matter to you.

  5. Step 05

    Quality Control

    Checks

    What happens

    A separate reviewer audits samples against the guidelines, agreement and error rates are measured, and automated checks catch format problems. Anything below the agreed target goes back for rework.

    What you get

    A quality report with every delivery.

  6. Step 06

    Deliver

    People

    What happens

    Data arrives in your format, for example COCO, YOLO, JSON or CSV, and software is released to your environment. Your feedback shapes the next batch or release.

    What you get

    AI-ready data or working software, and a way to keep improving it.

In practice

The same steps, for data and for software.

Whether we are labelling training data or building an app, the workflow does not change. Here is what it looks like for each.

Data & annotation projects

Images, video, text and audio labelled for AI models, from a pilot batch to ongoing volume.

  1. 01A pilot batch is labelled, reviewed and scored before anything scales.
  2. 02Guidelines with visual examples and edge cases, agreed with you.
  3. 03AI pre-labelling for boxes, polygons, transcripts and text labels.
  4. 04Every item reviewed and corrected by trained annotators.
  5. 05Sample audits by a second reviewer, with agreement and error rates.
  6. 06Delivery in the format your training pipeline expects.

Software & AI builds

Web, mobile and AI products, from the first conversation to launch and support.

  1. 01Discovery and a written plan before the build starts.
  2. 02Designs and clickable prototypes you can react to early.
  3. 03AI-assisted development, with every change reviewed by an engineer.
  4. 04Automated tests plus manual testing on real devices.
  5. 05AI features evaluated against test sets agreed with you.
  6. 06Launch, monitoring and support once it is live.

Quality & accuracy

How we keep quality high.

Quality is not a final step. It is built into how the work is set up, reviewed and measured from the first batch.

An honest note on accuracy

No team can promise perfect data or bug-free software, and we won't. We agree a measurable quality target with you at the start, check the work against it, and report the results with every delivery.

  • Clear guidelines

    Written rules with examples and edge cases, agreed with you before the work starts.

  • Trained, calibrated reviewers

    Reviewers learn your guidelines and are calibrated on the pilot before they touch full volume.

  • More than one pair of eyes

    Work is checked by someone other than the person who did it, in stages.

  • Measured, not assumed

    Agreement and error rates are tracked on audited samples and shared with you.

  • Fast feedback loops

    Questions and corrections go back into the guidelines, so the same mistake is not repeated.

  • Careful data handling

    Access is limited to the people on your project, under the data terms agreed with you.

Questions

Common questions.

Something else on your mind? Talk to our team.
Do you use AI on every project?

Where it helps. AI takes the first pass on repetitive work and people review what it produces. If AI would not make the work faster or better, we don't use it.

Who reviews the work?

Trained specialists review and correct the AI's output, and a separate reviewer audits samples before anything is delivered.

How is quality measured?

We agree the measure with you at the start, for example agreement or error rate on audited samples for data, or test results for software, and report it with every delivery.

Can we start with a small pilot?

Yes. Most projects start with a pilot batch or a short discovery, so you can judge the quality before committing to volume.

What do we receive at the end?

Data in the format you need with a quality report, or working software in your environment with documentation and a plan for support.

Understand → Prepare → AI Assist → Review → Check → Deliver

Ready to see it on your project?

Start with a short conversation or a small pilot. You will see the workflow on your own data or product before committing to anything bigger.

Start a ProjectTalk to Our Team