Effective Data.ai

Hyderabad

Structure. Visualise. Automate.

A data and AI engineering team in Hyderabad. Companies come to us when the numbers they run on are scattered across systems that were never meant to talk to each other.

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Results

What changed

Sample data
  1. Month-end close Sample 9days 2days 11 weeks

    What was broken

    Finance rebuilt the same figures by hand every month from four systems that disagreed with each other. Nobody trusted a number until it had been checked twice.

    What we built

    ELT pipelines from the source systems into one warehouse, with the reconciliation rules written down as code instead of living in someone's head.

    • BigQuery
    • ELT pipelines
    • GCP
    • CI/CD

    Practice

    Data Infrastructure

    Sample engagement — written to build the page, not drawn from a client.

  2. Manual reconciliation Sample 34hrs / week 4hrs / week 7 weeks

    What was broken

    Two people spent most of their week copying rows between a CRM export and an operations sheet, then chasing the differences.

    What we built

    Scheduled ingestion from the CRM, matching logic that handles the messy cases explicitly, and an exceptions queue holding only what genuinely needs a human.

    • HubSpot
    • Workflow automation
    • Document processing

    Practice

    AI-Driven Solutions

    Sample engagement — written to build the page, not drawn from a client.

  3. Collections recovery rate Sample 41per cent 63per cent 4 months

    What was broken

    The collections team worked the list top to bottom by outstanding amount, which meant the most recoverable accounts were reached last.

    What we built

    A prioritisation model scoring each account on likelihood of recovery rather than size, retrained on outcomes, delivered into the queue the team already worked from.

    • Risk scoring
    • Pattern recognition
    • Snowflake

    Practice

    Predictive Analytics

    Sample engagement — written to build the page, not drawn from a client.

  4. Ad-hoc report turnaround Sample 5days Sameday 6 weeks

    What was broken

    Every question from the leadership team became a ticket for one analyst, and the answer arrived after the meeting it was needed for.

    What we built

    Modelled tables the business actually recognises, and dashboards built around the questions people were already asking instead of the tables that happened to exist.

    • Interactive dashboards
    • BigQuery
    • Custom reports

    Practice

    Business Intelligence

    Sample engagement — written to build the page, not drawn from a client.

  5. Model into production Sample 6weeks 2days 9 weeks

    What was broken

    Models were finished long before they were useful. Each one was hand-carried into production by whoever wrote it, and no two arrived the same way.

    What we built

    One deployment path with automated testing, monitoring for performance and data drift, and rollback that does not require the person who trained the model.

    • Model deployment
    • Drift monitoring
    • CI/CD
    • AWS

    Practice

    MLOps

    Sample engagement — written to build the page, not drawn from a client.

These five figures are samples, not client results. They are placeholders written so the page could be built and reviewed, and they are replaced with real anonymised engagement figures before this site is published.

Structure. Visualise. Automate.

Structure

Pull the systems you already run into one place that can answer a question.

Visualise

Turn what is in there into the handful of numbers that change what you do.

Automate

Then let the work happen on a schedule, without anyone doing it.

Pipeline

Five stages, in order

Schematic

You cannot forecast on numbers you do not trust, and you cannot put a model in production without somewhere for it to run. Most companies arrive somewhere in the middle of this and only need the next stage — not all five.

Structure

  1. Stage 1
    5 systems become 1 warehouse

    Data Infrastructure

    Produces: one place where the numbers live.

    ETL and ELT pipelines that pull from the systems you already run — HubSpot, Zoom, and the rest — and land them somewhere queryable. Cloud data warehousing on BigQuery, Snowflake, or your existing platform. Cloud infrastructure on GCP, AWS, or Azure with CI/CD so deployments stop being events.

Visualise

  1. Stage 2
    1 warehouse becomes 1 dashboard · 1 API

    Business Intelligence

    Produces: dashboards someone opens on Monday.

    Analytics that surface the patterns already sitting in your data, dashboards built around the decisions people actually make, and reports carrying the handful of metrics that change what you do — rather than every metric the warehouse can produce. The same figures are exposed as business APIs, so the systems that need them do not wait for a person to export a file.

  2. Stage 3
    1 record becomes 1 forecast

    Predictive Analytics

    Produces: a forecast you can plan against.

    Decision systems that recommend rather than merely report. Risk assessment for collections prioritisation, investment analysis, and compliance monitoring. Time-series forecasting for cash flow, inventory, and revenue projection.

Automate

  1. Stage 4
    1 forecast acts on 3 systems

    AI-Driven Solutions

    Produces: work that happens without anyone doing it.

    Custom machine learning trained on your data and retrained on outcomes. Workflow automation, document processing, and customer service handling for the repetitive middle of a process. Computer vision for object detection, quality assurance, and monitoring.

  2. Stage 5
    1 run becomes every run

    DevOps & MLOps

    Produces: models that survive contact with production.

    Data pipeline management that holds quality and consistency, model deployment with automated scaling, monitoring for performance and data drift, and continuous integration so a model update is routine instead of a project. The outcomes feed back to the top, and the whole thing runs again without being asked.

Terms

Working with us

Standing

We start where you are

The first conversation is about which stage you are actually at, not which services we sell. Often the honest answer is that you need one stage, not an engagement.

We build on your platform

We work in the cloud you have already chosen and inside the tools your team already opens, rather than moving you onto ours.

  • GCP
  • AWS
  • Azure
  • Snowflake
  • BigQuery
  • HubSpot
  • Zoom

You own what we build

Pipelines, models, and infrastructure are written down and version-controlled as they are built, so nothing important lives only in our heads.

  • Version control
  • CI/CD
  • Documented pipelines

Based in Hyderabad

We work with teams in India and abroad. Say where you are and we will tell you honestly whether the overlap works.

Ticket

Tell us which number is wrong

Open

The most useful first message names one figure you do not trust, or one report that takes too long. That is enough for us to tell you whether we can help and roughly what it would take.