Data Science & ML
Machine learning development services build a model on the records your company already keeps — CRM, ERP, warehouse and event logs — and put it to work: churn scored every week, demand forecast every month, risky transactions flagged as they happen. As a Dhaka-based company, we build, validate and deploy inside your own cloud, for international companies, with ML engineers, a data engineer and a QA lead who validate every model on held-out data before you see a number.
One exit at each step. No long-term commitment at any of them. We start with one prediction the business would act on tomorrow. Your records are audited for enough history, a recorded outcome and leakage, a baseline is measured first, and every route to a working model is costed in your own numbers.
You get a written verdict — a model, a rule, a report 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 is included in machine learning development services?
Machine learning development services include six deliverables: a data readiness audit with a measured baseline, prepared and labelled records with a feature sheet, a custom model validated on held-out history, the pipeline that feeds it, deployment with monitoring and a retraining runbook, and forecasts delivered as scores people act on. You receive a working model, not a notebook.
Strategy and assessment
A data readiness audit before any model is proposed: enough history, a recorded outcome, no leakage, and a baseline measured on your current rule.
Data preparation and labelling
Records cleaned, joined and de-duplicated; missing outcomes labelled by your team or our reviewers; every feature written into one documented feature sheet.
Custom model building
Supervised and unsupervised models trained on your history, validated on a time-based holdout and compared with the rule you use today.
Data engineering for ML
Pipelines from CRM, ERP, warehouse and event logs into a versioned training set, with a feature store only where volume justifies it.
Deployment and MLOps
Scheduled or real-time scoring into the system people already use, drift and accuracy monitoring, and a retraining runbook your own team can run.
Forecasting and prediction
Demand, cash, churn, attrition and risk models with confidence ranges, delivered as scores and alerts that a person can act on today.
Not included: Cloud, GPU and model-hosting costs, bought in your name · large-scale image or video labelling · research on outcomes your business has never recorded · work outside the agreed data sources, priced first as a change.
Finance
Cash-flow forecasting, invoice-payment prediction and transaction anomaly flags built from ERP and bank exports.
Sales and marketing
Lead scoring, churn prediction and next-offer models built from CRM, billing and campaign data.
Operations
Demand forecasting, delivery-time prediction and maintenance risk built from orders, tickets and sensor logs.
HR and payroll
Attrition risk and payroll anomaly detection built from HRIS and payroll exports, one reviewer per flag.
How does machine learning development validate a model before production?
Machine learning development validates every model on months it has never seen: the history is split by time, the model learns from the earlier 80% and is scored on the latest 20%, and the result is compared with the rule you use today. In the example below, recall rises from 0.38 to 0.71 before a person signs it off.
Time-based split · 41,600 customer-months
Training: the earlier 80% of months
Holdout: the latest 20%, never seen in training
Gradient boosting, trained only on the earlier months; the latest months stay unseen until the model is scored, as they would be in production.
Scored on the held-out months
26 features documented · 3 removed for leakage
Model lifecycle
Data pulled→Features built→Model trained→Validated · a person signs→Deployed→Drift monitored→Retrained↺
After deployment, drift is monitored and the model is retrained on schedule and validated the same way.
Illustrative example. Yellow marks the step where a person signs; every number can be regenerated from the recorded data version and seed.
How do machine learning consulting services work, from data audit to production?
Machine learning consulting services with us run in five steps: a free scoping call, a two-week data readiness diagnostic, a three-to-four-week pilot that trains and validates one model on your history, a four-to-eight-week production build with pipeline, deployment and monitoring, then optional Managed Ops. Every step ends with a written result you can stop on.
01 30 min · free
Scope A call about the prediction you want and the records you have. If the sources are known, you leave with a diagnostic quote.
02 2 weeks · credited
Diagnose A data readiness audit: history, recorded outcome, leakage and a measured baseline, then a written verdict — model, rule, report first, or not yet.
03 3–4 weeks · fixed price
Pilot One model trained on your data and validated on a time-based holdout against the baseline, with reproducible code and a go/no-go note.
04 4–8 weeks · quoted after pilot
Production Training pipeline, deployment into the system people use, monitoring with drift alerts, a retraining runbook, team training and 30 days of fixes.
05 Monthly · optional
Managed Ops Accuracy and drift monitoring, scheduled retraining, one model change a month and a monthly review call. Cancel any month; everything stays yours.
How long does a first machine learning model take?
A first model takes five to six weeks: a two-week diagnostic, then a three-to-four-week pilot validated on held-out history. Production takes four to eight weeks more.
Runbook Retraining and drift checks your own team can run after handover
Tools Python, scikit-learn, XGBoost, PyTorch, MLflow and SQL
Communication Weekly call, shared channel, written change log
Data CRM, ERP, warehouse exports, event logs, spreadsheets
QA Time-based holdout, baseline comparison, reproducible runs
Hosting Your cloud, your repositories, your credentials
Do you need a machine learning model, a rule, a report or an AI agent?
Not every prediction needs machine learning. A few hundred records with a pattern people can write down need a rule; an outcome nobody records yet needs a report first; decisions made on text and documents need an AI agent with a person approving. Good data science consulting sorts this out before anyone is billed; five questions show which fits.
1. Past records with the outcome you want to predict?
2. Is that outcome recorded today?
3. What does the prediction start from?
4. What does a wrong prediction cost?
5. Who acts on the prediction?
A machine learning model, piloted first
A thousand or more records with a recorded outcome: a pilot trains and validates one model on your history in three to four weeks.
Sample model run log · customer churn model
1
Data pulled41,600 customer-months from CRM and billing
2
Features built26 features documented · 3 removed for leakage
3
Model trainedGradient boosting · last 20% of months held out
4
ValidationRecall 0.71 · precision 0.60 · baseline 0.38
5
DeployedWeekly scoring into the CRM · drift alert set
Illustrative run
Yellow = a person signs
Every step logged
Reproducible from data version and seed
How the verdict is decided: outcome not recorded → a report first · the decision starts from text or documents → an AI agent with approvals · under 1,000 records → a rule or a workflow · 1,000 or more records with a recorded outcome → a machine learning model, piloted first.
Why choose us as your machine learning development company?
A machine learning development company is judged on what the model does on unseen data, not on a demo. We validate every model against your current rule, price each step in writing, pseudonymise personal fields, a safeguard the GDPR names, and deliver ai ml development services inside your cloud, with every run reproducible from the log.
Without a validated process
!!!!!
- A notebook that scores well on training data and fails in production
- No baseline, so nobody knows if the model beats the current rule
- Hourly quotes that grow as the data turns out messier than promised
- Code and model on a laptop you cannot reach after the contract
With EICRA
Pilot report · Churn model
Holdout20%Recall0.71Precision0.60Baseline0.38VerdictGo to production
Illustrative example
- Validated on held-out history against your current rule before production
- A QA lead signs every result; every run reproducible from the log
- Fixed prices at every step; production quoted in writing after the pilot
- Code, model, pipeline and runbook in your cloud from day one
Is it safe to outsource machine learning development?
Outsourcing machine learning development to an offshore team is safe when data access, transfer and ownership are settled in writing before any record moves. As a Bangladesh-based company, we train and deploy inside your cloud with least-privilege access, pseudonymise personal fields before modelling, sign a data processing agreement before personal data is touched, and remove our access at handover. Reviewed By Eicra.com team
Which machine learning agreements are signed, and when?
NDA — mutual, signed before any data, model or notebook is shared with anyone.
DPA — processor terms under Article 28(3) of the GDPR and the equivalent national law, for any personal data.
International data transfers — standard contractual clauses or the transfer instrument your jurisdiction requires, signed before personal data moves.
Access — a project role in your cloud with the least privilege that does the job; credentials never leave your accounts.
Certifications — listed only when held; none are claimed on this page or in any proposal.
What machine learning controls, IP and rework terms apply?
Your cloud Training, storage and deployment stay in your cloud accounts; nothing is hosted on our side.
Pseudonymisation Names, emails and IDs replaced before modelling wherever the prediction does not need them.
Reproducibility Every training run logged with data version, code version and seed, so any number can be regenerated.
Ownership Code, model, pipeline, evaluation set and runbook are yours under the contract; our access is removed at handover.
Rework Free when a delivered model misses the metric agreed for the pilot; new data sources priced first as a change.
What proof do you get before you pay for machine learning development?
Before you pay for machine learning development, you get evidence instead of promises: measured commitments, a pilot model validated on your own held-out history against the rule you use today, and a free 30-minute data review. Client case studies with numbers are added here as clients give permission to name them.
3–4 weeks
To a first model trained on your history and validated on held-out data against your current rule.
Every run
Logged with data version, code version and seed, so any number on a report can be regenerated.
30 days
Of fixes after a production build, with a retraining runbook and training for your team.
Case studies: client results with numbers are added here as clients give permission to name them. Ask on the scoping call for references in your industry.
What do buyers ask about machine learning development?
How much do machine learning development services cost?
Our machine learning development services are fixed-price per step, and each price is on the price cards at the top: a two-week data readiness diagnostic half credited to the pilot, a three-to-four-week pilot that trains and validates one model on your data, a production build quoted in writing after the pilot, and optional Managed Ops.
What are machine learning development services?
Machine learning development services turn the records a business already keeps into a working model: a data readiness audit with a measured baseline, prepared and labelled data, a custom model validated on held-out history, the pipeline that feeds it, and deployment with monitoring and a retraining runbook. You receive a working model, not a notebook.
Do we have enough data for machine learning?
Our rule of thumb from data science consulting: a thousand or more past records with the outcome you want to predict already recorded, covering at least a year of normal business. Below that, a rule or a report usually serves better, and the two-week diagnostic says so in writing before any model is proposed.
How long does it take, and why do so many machine learning projects never reach production?
A first model takes five to six weeks: a two-week diagnostic, then a three-to-four-week pilot validated on held-out history; production takes four to eight weeks more. Projects that never reach production usually lack validation, a deployment path and an owner; the runbook and monitoring are the answer to all three.
Who owns the model, the code and our data?
You do. Code, model, training pipeline, evaluation set and runbook are built in your cloud and repositories and remain yours; ownership is written into the contract. Your data never leaves your accounts; our offshore team works through a least-privilege role, signs a data processing agreement before personal data is touched, and is removed at handover.
Start with a free 30-minute data review or the two-week diagnostic.