Machine learning development services are the work of turning the records a business already keeps into a model that predicts what happens next. Eicra Soft Limited, a Dhaka-based team serving international companies, builds, validates and deploys that model inside your own cloud, and a quality assurance (QA) lead checks every result on held-out data before you see a number.
Stop after any step; there is no long-term contract. We start with one prediction the business would act on tomorrow. We audit your records for enough history, a recorded outcome and leakage, measure a baseline first, and cost every route to a working model in your own numbers.
The written verdict says which fits — a model, a rule, a report first or nothing yet. If nothing in it justifies a build, you stop here.
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.
A data readiness audit comes before any model is proposed. It checks for enough history, a recorded outcome and no leakage, and measures a baseline on the rule you use today, so every later result is compared with something your team already trusts.
Records are cleaned, joined and deduplicated, and missing outcomes are labelled by your team or our reviewers. Every feature is written into one documented feature sheet, so the next engineer can see what the model reads and why each field is there.
Supervised and unsupervised models are trained on your history and validated on a time-based holdout, so the score reflects months the model has never seen. Each model is compared with the rule you use today, and only a model that beats it goes forward.
Python and Structured Query Language (SQL) pipelines move records from CRM, ERP, warehouse and event logs into a versioned training set. A feature store is added only where volume justifies it, so the pipeline stays simple enough for your team to run.
Scores are delivered on a schedule or in real time into the system people already use. Drift and accuracy monitoring raise an alert when the model starts to slip, and a retraining runbook lets your own team refresh it without calling us.
Demand, cash, churn, attrition and risk models come with confidence ranges, so a person can see how sure each forecast is. Results are delivered as scores and alerts that someone can act on today, not as a report that waits for a meeting.
Cash-flow forecasting, invoice-payment prediction and transaction anomaly flags, built from your ERP and bank exports. Every forecast is validated on held-out months and compared with the method the finance team uses today, so it replaces a spreadsheet only when it is measurably better.
Lead scoring, churn prediction and next-offer models, built from CRM, billing and campaign data. Scores are written back into the CRM your team already works in, so sales and marketing act on a ranked list each week instead of exporting data to guess who to call first.
Demand forecasting, delivery-time prediction and maintenance risk, built from orders, tickets and sensor logs. Forecasts arrive with confidence ranges, so planners can see how sure each number is, and drift alerts warn when patterns change before stock or staff plans go wrong.
Attrition risk and payroll anomaly detection, built from your human resources (HR) system and payroll exports, with one reviewer per flag. Personal fields are pseudonymised before modelling wherever the prediction does not need them, and personal data is processed only under a signed data processing agreement.
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%, then 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.
The AI Risk Management Framework from the National Institute of Standards and Technology (NIST) states: “AI systems should be tested before their deployment and regularly while in operation.” The holdout test comes before deployment; drift monitoring and scheduled retraining cover the rest.
Model lifecycle · churn model
What the step does · its result
Data pulled
Split by time before any training.
Features built
Written into one documented feature sheet.
Model trained
Gradient boosting, on the earlier months only.
Validated · a person signs
Scored on the held-out months against today’s rule.
Deployed and monitored
Drift is watched after deployment.
Click a step, or a number below, to switch the result
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.
Machine learning development with us takes five steps, from a free scoping call to optional Managed Ops. A two-week diagnostic checks your data, and a three-to-four-week pilot trains and validates one model on your history. A four-to-eight-week production build then adds the pipeline, deployment and monitoring. Each step closes with a written result, so you can stop there.
| # | Step | Time | What you get |
|---|---|---|---|
| 01 | Scope | 30 min | A call on the prediction you want and your records; a diagnostic quote. |
| 02 | Diagnose | 2 weeks | Data readiness audit and measured baseline, then a verdict: model, rule, report or not yet. |
| 03 | Pilot | 3–4 weeks | One model validated on a time-based holdout, with reproducible code and a go/no-go note. |
| 04 | Production | 4–8 weeks | Pipeline, deployment, drift monitoring, retraining runbook, team training and 30 days of fixes. |
| 05 | Managed Ops | Monthly · optional | Monitoring, scheduled retraining, one model change a month and a review call. |
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, and each step closes with a written result, so you can stop there without paying for the next one.
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 data analytics report first. Decisions made on text and documents need an AI agent with human approval. Good data science consulting sorts this out before anyone is billed; five questions show which fits.
Google’s Rules of Machine Learning guide states: “Rule #1: Don’t be afraid to launch a product without machine learning.” That is why the verdict can be a rule or a report, and the two-week diagnostic says so in writing before any model is proposed.
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 thousand or more records with a recorded outcome: a pilot trains and validates one model on your history in three to four weeks.
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.
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 and price each step. Personal fields are pseudonymised, a safeguard the General Data Protection Regulation (GDPR) names. We deliver AI/ML development services inside your cloud, with every run reproducible.
Outsourcing machine learning development is safe when data 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 train and deploy inside your cloud with least-privilege access, and the work 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 work runs, how personal fields are handled, how any number can be regenerated, who owns the result and how a model that misses its metric is fixed.
Before you pay for machine learning development, you can test the claims. You get three measured commitments and a pilot model validated on your own held-out history against the rule you use today. A free 30-minute data review comes first. Case studies with client numbers follow as clients agree to be named.
To a first model trained on your history and validated on held-out data against your current rule.
Logged with data version, code version and seed, so any number on a report can be regenerated.
Of fixes after a production build, with a retraining runbook and training for your team.
Buyers ask twelve questions about machine learning development before they commit. They ask what it costs, what it is, whether their data is enough and how a project starts. They also ask when a model reaches production, who owns it, where data is stored, how it is protected, takeovers, tools and when machine learning is not worth it.
More answersEvery step of our machine learning development services has a fixed price on the cards at the top. The two-week diagnostic is half credited to the pilot, which trains and validates one model on your data in three to four weeks. Production is quoted after the pilot, and Managed Ops is optional.
Machine learning development is the work of turning the records a business already keeps into a working model. It covers a data readiness audit, prepared data, a model validated on held-out history, the data engineering pipeline that feeds it, and deployment with monitoring. You receive a working model, not a notebook.
As a rule of thumb, you need a thousand or more past records with the outcome already recorded, covering at least a year of normal business. Below that, a rule or a report serves better, and the two-week diagnostic says so in writing before any model is proposed.
A project starts with a free 30-minute call, 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. The steps run in this order.
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 answer all three.
You do. Code, model, training pipeline, evaluation set and runbook are built in your cloud and repositories and stay yours under the contract. Your data never leaves your accounts; our team works through a least-privilege role and is removed at handover.
Your data stays in your own cloud accounts. Training, storage and deployment run there, never on our side, and our team in Dhaka works through a least-privilege project role. Personal data is processed only under a signed DPA, and international transfers use standard contractual clauses or the instrument your jurisdiction requires.
Confidential data is protected by contract first. A mutual NDA is signed before any record, model or notebook changes hands, a DPA covers any personal data, and names, emails and IDs are pseudonymised before modelling wherever the prediction does not need them. Credentials never leave your accounts, and our access is removed at handover.
Yes. A takeover starts with the two-week diagnostic, which audits the data, the existing model and its results against a measured baseline. It ends in a written verdict on whether to rebuild the model, replace it with a rule or a report, or leave it running. You can stop after the diagnostic, and half its fee is credited to the pilot.
We use Python, scikit-learn, XGBoost, PyTorch, MLflow and SQL, chosen for each problem. Supervised and unsupervised models are trained on your history, and the sample churn model on this page uses gradient boosting. Everything runs in your cloud and repositories, so the stack stays one your own team can maintain.
After you book, a 30-minute scoping call confirms the prediction you want and the records you hold. You then give a least-privilege role to the agreed sources, the two-week diagnostic runs, and a written verdict follows with a pilot quote when a model is justified. Nothing is built until you approve that quote.
Machine learning is not worth it with fewer than about a thousand past records or no recorded outcome; a rule or a report serves better, and the diagnostic says so in writing. It is also the wrong tool when the decision starts from text or documents, where an AI agent with human approval fits better.
Start with a free 30-minute data review or the two-week diagnostic.
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