AI-ready data services are the work of turning records scattered across your systems into one clean, documented copy that dashboards, forecasts and AI models can use without manual clean-up. Eicra Soft Limited, a Dhaka-based team serving international companies, builds the pipelines and warehouse inside your own cloud, and a quality assurance (QA) lead reconciles every loaded number with your finance figures.
Every step can be the last; nothing binds you long term. We start with the two or three source systems behind the numbers you argue about most. We map every source, choose the platform you already pay for over a new one, and cost every route to one reconciled warehouse in your own numbers.
The verdict comes in writing — a warehouse and pipelines, a dashboard first or nothing yet. If nothing in it justifies a build, you stop here.
AI-ready data services include six deliverables and are one of the engagements in our data engineering services. You get a source audit with a pipeline map and extract, transform, load (ETL) or extract, load, transform (ELT) pipelines. You also get a cloud data warehouse on your platform, data quality tests and governance, AI-ready datasets, and orchestration with alerts and a runbook. You receive a running platform, not a diagram.
A data engineer does exactly this work: maps the sources, writes and tests the pipelines, models the warehouse, sets the alerts and fixes a load that fails overnight. A QA lead then checks the loaded figures against finance before you see them.
Every system that holds a number you report on is mapped with its owner, its keys and its refresh rate before any pipeline is written. The map shows which two or three sources sit behind the numbers you argue about most, so the pilot starts where trust is lowest.
Pipelines extract from CRM, ERP, billing and product databases, transform in Structured Query Language (SQL) and load on a schedule, with every step logged. Each load records its source, row counts, test results and time, so any figure in the warehouse can be traced back to the system it came from.
One governed store on Snowflake, Databricks, BigQuery or Microsoft Fabric, modelled so finance, sales and operations read the same figures. We choose the platform you already pay for over a new one, and the warehouse loads data raw, tests it and then models it on one customer key.
Every load is tested for duplicates, nulls and broken keys, and a catalogue lists each table and its owner. Personal fields such as names, emails and IDs are tagged in the catalogue and masked in every table that does not need them, with access controlled by a least-privilege role.
Clean, joined, documented tables and document collections that a model, a retrieval system or an AI agent can use without manual clean-up. Each dataset is complete, fresh on a known schedule, joined on stable keys and governed, which is what AI-ready data means on this page.
Scheduled runs on Airflow or your platform’s scheduler, with alerts that page the owner when a load fails or drifts. A runbook lists the alerts, the failed-load steps and the table owners, so your own team can follow it after handover and keep every load running.
Revenue, cash and cost tables are reconciled with the ledger and loaded from ERP, billing and bank exports. Because every pilot is reconciled with your ledger before production is quoted, finance can sign the revenue figure that dashboards, forecasts and AI models later read from the warehouse.
Pipeline, churn and campaign tables are joined on one customer key, loaded from CRM, billing and ad platforms. One key means the churn table and the campaign table describe the same customer, so a churn model and a sales dashboard read the same record rather than competing copies.
Orders, stock, deliveries and tickets sit in one warehouse, refreshed hourly where the shop floor needs it. Every load is tested for duplicates, nulls and broken keys, so the stock figure an operations dashboard or a demand forecast reads has passed the same checks every time.
Headcount, attrition and payroll tables are loaded from your human resources information system (HRIS) and payroll. Personal fields are tagged in the catalogue and masked in every table that does not need them, and access is controlled, so analysts and models see the figures without exposing the people behind them.
An AI-ready data architecture in data engineering has four layers. Source systems such as CRM, ERP and billing feed scheduled ETL/ELT pipelines that log every step. A cloud data warehouse loads the data raw, tests it and models it on one customer key. Documented datasets then feed dashboards, machine learning (ML) models and AI agents.
Databricks’ documentation on the medallion architecture states: “The medallion architecture describes a series of data layers that denote the quality of data stored in the lakehouse.” The raw, tested and modelled layers in the example follow the same idea.
Four parts · sales and billing warehouse
What the part holds · its result
Sources
The systems that feed the warehouse.
Pipelines
Scheduled ETL/ELT that logs every step.
Cloud data warehouse · a person signs
Four layers, from raw to reconciled.
AI-ready datasets
Documented datasets for three uses.
Click a part, or a number below, to switch the result
Alerts when a load fails or drifts, a catalogue of tables and owners, and a runbook your team runs.
Illustrative example. Yellow marks the step where a person signs; every load is logged with source, row counts and test results.
AI-ready data services with us follow five steps, and each ends with a written result you can stop on. A free scoping call comes first, then a two-week audit maps your sources and picks the platform. A three-to-four-week pilot connects the sources and loads one reconciled warehouse. Production takes four to eight weeks, and Managed Ops follows.
The Apache Airflow documentation states that Airflow is “an open-source platform for developing, scheduling, and monitoring workflows”. Your loads run on it or on your platform’s own scheduler, with alerts that page the owner when a load fails.
| # | Step | Time | What you get |
|---|---|---|---|
| 01 | Scope | 30 min · free | A call on the numbers you cannot trust and their source systems. |
| 02 | Diagnose | 2 weeks | Every source, owner and key mapped; platform chosen; a written verdict. |
| 03 | Pilot | 3–4 weeks | Two or three sources loaded, tests written, figures reconciled with finance. |
| 04 | Production | 4–8 weeks | All sources, orchestration, alerts, catalogue, runbook training and 30 days of fixes. |
| 05 | Managed Ops | Monthly · optional | Failed-load response, schema-change fixes, one new source a month, review call. |
A first working warehouse takes five to six weeks: a two-week audit, then a three-to-four-week pilot that loads one reconciled warehouse. Production takes four to eight weeks more, and each step closes with a written result, so you can stop there without paying for the next one.
Not every data problem needs data engineering. One source system with clean records needs a data visualization dashboard on top of it. Records already in one trusted place, plus a question about the future, need a machine learning pilot from our data science team. Several sources moved by exports and copy-paste need pipelines first. Five questions show which fits.
1. How many systems hold the numbers you report on?
2. Is there one place where the cleaned data lives today?
3. How does data move between systems today?
4. What will the data feed first?
5. How fresh must it be?
Several sources, no single clean copy, moved by hand or by scripts: a pilot connects the sources and loads one reconciled warehouse in three to four weeks.
How the verdict is decided: one source and one clean place → a dashboard first · data already in one place and a prediction wanted → a machine learning pilot · no single clean copy, or moved by exports → a warehouse and pipelines, piloted first · pipelines exist on scripts → hardening and Managed Ops.
A data engineering company is judged on whether the numbers in the warehouse match the numbers finance signs. We reconcile every pilot with your ledger and price each step in writing. Personal fields stay tagged and masked under a processor agreement that follows the General Data Protection Regulation (GDPR). We deliver data engineering solutions on the platform you already run.
Outsourcing data engineering to an offshore team is safe when access, transfer and ownership are settled in writing. As a Bangladesh-based company, we sign a mutual non-disclosure agreement (NDA) and a data processing agreement (DPA) before any record moves. We build in your cloud with least-privilege access, and every pipeline stays your intellectual property (IP).
Three agreements are signed before any record moves, and access is set up in the order shown below. Each agreement names what we may see, where it may be processed and when our access ends, and certifications are listed only when they are actually held.
Five controls apply to every engagement and are written into the contract rather than promised in a proposal. They cover where the pipelines run, how personal fields are handled, how any figure can be traced, who owns the result and how a pipeline that fails its tests is fixed.
Before you pay for data engineering, you can check the figures yourself. You get three measured commitments and a pilot warehouse reconciled with your own ledger before production is quoted. A free 30-minute source review comes first. Client case studies follow as clients agree to be named.
To a first warehouse loaded from your sources and reconciled with your finance figures.
Tested for duplicates, nulls and broken keys, and logged with source, row counts and time.
Of fixes after handover, with orchestration, alerts and a runbook your own team runs.
Buyers ask twelve questions about AI-ready data services before they commit. They ask what the services cost, what they are, what AI-ready data means and how a project starts. They also ask when a warehouse reaches production, who owns it, where data is stored, how personal data is protected, takeovers, tools, AI and when a warehouse is not worth it.
More answersAI-ready data services cost $1,500 for a two-week data audit and $4,000 for a fixed-price pilot, with production from $6,000. Half the audit fee is credited to the pilot, which connects your sources and loads one reconciled warehouse in three to four weeks. Production is quoted in writing once the pilot ends, and Managed Ops from $750 a month is optional.
AI-ready data services build the pipelines and warehouse that turn scattered records into one clean, documented copy a model can use. They connect CRM, ERP, billing, HR and product databases, load them on a schedule into a cloud data warehouse, test every load, and hand over documented datasets, alerts and a runbook your own team can follow.
AI-ready data is data a model, a retrieval system or an AI agent can use without manual clean-up: complete, documented, fresh on a known schedule, joined on stable keys and governed. The warehouse is where readiness is built, and every warehouse we build ships with documented, tested datasets.
A project starts with a free 30-minute source review, and every later step is priced in writing before it begins, so you can stop after any step without paying for work you did not ask for. Each step ends with a written result, and the steps run in this order.
A first working warehouse takes five to six weeks: a two-week audit, then a three-to-four-week pilot that loads one reconciled warehouse. Production takes four to eight weeks more and adds all agreed sources, orchestration, alerts, a catalogue of tables and owners, team training on the runbook and 30 days of fixes after handover.
You do. Pipelines, warehouse schema, tests, orchestration and runbook live in your cloud and repositories, and the contract makes them yours. No record leaves your accounts: our engineers use a least-privilege role that is removed at handover, with a signed data processing agreement in place first.
Your data stays in your own cloud and warehouse accounts. Pipelines run there and data lands there; nothing is copied to our side, and our team in Dhaka works through a least-privilege service role. Personal data is processed only under a signed DPA, and international transfers use standard contractual clauses or the instrument your jurisdiction requires.
Personal data is protected by contract first, then by the build. A mutual NDA is signed before any credential, schema or export leaves your hands, and a DPA meeting Article 28(3) of the GDPR covers any personal data. Names, emails and IDs are tagged in the catalogue and masked in every table that does not need them, and every load is logged.
Yes. A takeover starts with the two-week audit, which maps every source, owner, key and refresh rate behind the pipelines you already run. Where pipelines exist on scripts, the written verdict is hardening and Managed Ops rather than a rebuild: tests, alerts, a catalogue and a runbook are added around what already works.
We use Python, SQL, dbt and Airflow or your platform's own scheduler. The warehouse runs on Snowflake, Databricks, BigQuery or Microsoft Fabric, and we choose the platform you already pay for over a new one. Everything runs in your cloud, warehouse account and repositories, so the stack stays one your own team can maintain.
Not the pipeline, only some of the typing. AI tools now write much of the transformation code and documentation, which keeps our pilots short. They cannot decide which of three revenue figures is right, reconcile it with finance, or answer for a failed overnight load. A person still owns the pipeline.
A warehouse is not worth it when one source system already holds clean records; a dashboard on top of that system serves better. It is also the wrong first step when the data already sits in one trusted place and the real question is a prediction, which needs a machine learning pilot instead. The audit says so in writing.
Start with a free 30-minute source review or the two-week audit.
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