AI-Ready Data Services for Pipelines and Warehouses
Pipelines and a Warehouse Your AI Can Trust
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.
30 minutes 01 Scope · free call
2 weeks 02 Diagnose · $1,500, half credited to the pilot
3–4 weeks 03 Pilot · $4,000 fixed
4–8 weeks 04 Production · from $6,000, quoted after the pilot
Monthly 05 Managed Ops · from $750 a month, optional
What do AI-ready data services include?
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.
Source audit and pipeline map
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.
ETL/ELT pipeline development
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.
Cloud data warehouse or lakehouse
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.
Data quality and governance
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.
AI-ready datasets for models
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.
Orchestration, monitoring and runbook
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.
Not included: Cloud and warehouse platform costs, bought in your name · a new platform licence when the one you already pay for will do · pipelines for sources outside the agreed list, priced in writing first.
Finance and ledger tables
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.
Sales and marketing
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.
Operations and stock tables
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.
HR and payroll
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.
What does an AI-ready data architecture look like?
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.
CRMERPBillingHRISProduct database
5
Source systemsfeeding one cloud data warehouse
Pipelines
Scheduled ETL/ELT that logs every step.
On a schedulenightly load at 02:00, every step logged
Transform in SQLdbt models, Airflow or your platform’s scheduler
Alertsa failed or drifting load pages the owner
02:00
Nightly loadevery step logged
Cloud data warehouse · a person signs
Four layers, from raw to reconciled.
Raw14 tables, as received
Tested22 checks: duplicates, nulls, broken keys
Modelledone customer key across sales and billing
Reconciled · a person signsrevenue matches the ledger to 0.1%
0.1%
Revenue matches the ledgersigned by a person
AI-ready datasets
Documented datasets for three uses.
Dashboardsone set of figures for finance and sales
Forecasts and ML modelsclean history on stable keys
AI agents and retrievaldocumented tables and document collections
One set of figuresfor reports, forecasts and retrieval
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.
How do AI-ready data services work?
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.
What each AI-ready data step delivers
| # | 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. |
How long does a first warehouse take?
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.
Runbook Alerts, failed-load steps and table owners your own team can follow
Platform Snowflake, Databricks, BigQuery or Microsoft Fabric — yours
Tools Python, SQL, dbt, Airflow and your platform’s scheduler
Sources CRM, ERP, billing, HRIS, product databases, spreadsheets
QA Tests on every load, reconciliation with finance, logged runs
Hosting Your cloud, your warehouse account, your repositories
Do you need pipelines or a dashboard first?
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?
A warehouse and pipelines, piloted first
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.
Sample run log · sales warehouse
1
Sources connectedCRM, billing and ERP · 3 systems, 14 tables
2
Pipelines builtNightly load at 02:00 · every step logged
3
Tests written22 checks for duplicates, nulls and broken keys
4
ReconciliationMonthly revenue matches the ledger to 0.1%
5
Handed overWarehouse live · alerts set · runbook signed
Illustrative run
Yellow = a person signs
Every load logged
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.
Why choose us for AI-ready data services?
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.
Without a reconciled build
!!!!!
- A warehouse whose revenue figure nobody in finance will sign
- A new platform sold to you when yours would do
- Hourly quotes that grow as each source turns out messier than promised
- Pipelines that only the engineer who left knows how to run
With Eicra Soft Limited
Pilot report · Sales warehouse
Sources3Tables14Tests22/22Reconciled99.9%VerdictGo to production
Illustrative example
- Every pilot reconciled with your ledger before production is quoted
- Built on the platform you already pay for, with open tools
- Fixed prices at every step; production quoted in writing after the pilot
- Tests, alerts and a runbook your own team runs from day one
Is it safe to outsource data engineering?
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).
Which agreements are signed, and when?
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.
NDA — mutual, signed before any credential, schema or export leaves your hands.
DPA — processor terms that meet Article 28(3) of the GDPR and matching national laws, for any personal data.
International data transfers — standard contractual clauses or the transfer instrument your jurisdiction requires, signed before personal data moves.
Access — a least-privilege service role in your cloud and warehouse; credentials never leave your accounts.
Certifications — stated only when held; none appears here or in a proposal.
Which controls, IP and rework terms apply?
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.
Your cloud Pipelines run and data lands in your cloud and warehouse accounts; nothing is copied to our side.
Personal fields Names, emails and IDs tagged in the catalogue and masked in every table that does not need them.
Logged loads Every load logged with source, row counts, test results and time, so any figure can be traced.
Ownership Pipelines, warehouse schema, tests and runbook are yours under the contract; our access is removed at handover.
Rework A pipeline that fails its agreed tests or reconciliation is fixed free; new sources are priced first as a change.
What proof do you get before you pay?
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.
3–4 weeks
To a first warehouse loaded from your sources and reconciled with your finance figures.
Every load
Tested for duplicates, nulls and broken keys, and logged with source, row counts and time.
30 days
Of fixes after handover, with orchestration, alerts and a runbook your own team runs.
Case studies: warehouse results with client numbers go here once each client agrees. Ask for references from your sector on the scoping call.
What do buyers ask about AI-ready data services?
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.
Reviewed by Mohammad Nazrul Islam, Managing Director, Eicra Soft Limited.
How much do AI-ready data services cost?
AI-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.
What are AI-ready data services?
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.
What is AI-ready data?
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.
How does a project start?
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 scoping call covers the numbers you cannot trust and the systems they come from.
- The two-week audit maps every source, owner, key and refresh rate.
- A fixed-price pilot loads one warehouse and reconciles it with finance.
- Production follows only if the pilot’s go/no-go note says it is worth building.
When does the warehouse reach production?
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.
Who owns the pipelines, warehouse and data?
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.
Where is our data stored and processed?
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.
How is personal data protected?
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.
Can you take over existing pipelines?
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.
Which tools and platforms do you use?
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.
Will ETL be replaced by AI?
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.
When is a warehouse not worth it?
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.