01
Diagnose
Two to three weeks with your data and the people who use it. We come back with a written assessment: what is achievable, what the data will not support, what it costs, and whether a simpler non-ML approach would do the job better.
Applied AI engineering · Zirakpur, India
Most AI projects don't fail at the model.
They fail after it.
A prototype that works on a laptop is the easy part. Blutrain Private Limited builds the part that comes next — the pipelines, the evaluation harness, the fallbacks and the monitoring that let a model run unattended on real data, on a Tuesday, when nobody is watching.
L1Dataingestion · quality · lineage
L2Evaluationtest sets · regression gates
L3Modeltraining · retrieval
L4ServingAPIs · caching · fallbacks
L5Interfacewhat your people touch
01 / Position
We are engineers who happen to work on AI.
There is no shortage of firms who will show you a chatbot demo. Far fewer will tell you that your ticket data has three conflicting definitions of "resolved", that the label set your vendor promised does not exist, or that the honest answer to your question is a SQL query rather than a language model.
Blutrain Private Limited is an applied AI engineering firm. We take a business problem, work out whether machine learning is genuinely the right instrument for it, and then build the whole system — ingestion, features, model, serving, evaluation and monitoring — to a standard your own engineers can maintain after we leave. When the answer is that you do not need AI, we say so early, while it is still cheap to hear.
Every engagement ends with a documented repository in your organisation, running on
your infrastructure, with the evaluation suite that proves it works.
02 / How we work
Five stages, each with something you can refuse to sign off.
Nobody should approve a six-month AI programme on a slide deck. Every stage produces a reviewable artefact, and you decide at each boundary whether the next one is worth funding.
01/05
Diagnose
- Diagnose
- Establish the baseline
- Build the thin slice
- Harden
- Hand over
02
Establish the baseline
Before any model, we measure how the process performs today — accuracy, cost, handling time, error rates. Without this number there is no honest way to claim an improvement later, and most projects skip it.
03
Build the thin slice
One narrow path through the whole system, end to end, running on production-shaped data. It is deliberately unimpressive and deliberately real. It tells us what the architecture gets wrong while changing it is still cheap.
04
Harden
The stage most vendors skip. Retries, timeouts, rate limits, graceful degradation, cost ceilings, an evaluation suite in CI, and an answer to the question 'what happens when the model is wrong or the provider is down?'
05
Hand over
Documentation written for the engineer who inherits it, a runbook for the team who operates it, and as much or as little ongoing support as you want. No lock-in to us.
The shape of it
From raw data to a monitored system.
03 / Capabilities
Ten things we build, and are willing to be measured on.
Each of these is a real delivery practice with its own tooling, evaluation approach and failure modes — not a service page written to catch search traffic.
How we scope an engagementData Analytics
Turn scattered operational data into decisions people actually act on.
Capability
Machine Learning
Custom models trained on your data, evaluated against your economics.
Capability
Natural Language Processing
Systems that read documents, tickets and conversations at volume.
Capability
Computer Vision
Inspection, counting and recognition on the factory floor and in the field.
Capability
Predictive Maintenance
Catch mechanical failure while it is still a maintenance ticket.
Capability
Personalised Recommendations
Ranking and discovery that lifts revenue without eroding trust.
Capability
Risk & Fraud
Scoring that holds up to auditors, regulators and adversaries.
Capability
Supply Chain Optimisation
Forecasting, allocation and routing under real-world constraints.
Capability
Customer Insights
Segmentation and churn modelling grounded in behaviour, not personas.
Capability
Voice Infrastructure
Speech pipelines for contact centres in Indian and global languages.
Capability
04 / The gap
The demo is 40% of the work. Budget for the rest.
A model that is right 85% of the time is a good afternoon's work. A system that is right 85% of the time, knows which 15% to escalate, costs a predictable amount per call, and tells you when it starts drifting — that is a quarter of engineering.
We price and plan for the second thing, because the first one has never once been what a client actually needed.
How engagements are priced05 / Sectors
Domain context is not optional.
A fraud model and a crop-yield model are the same mathematics and completely different projects. The difference is knowing which errors are survivable, who signs off, and what the regulator expects to see. These are the sectors where we have that context already.
06 / In the open
Numbers we can actually show you.
Every figure below is either published on this site or written into our terms. None of it is a claim you would have to take on trust.
126
services with an hourly rate published in the open, across 20 categories — no 'contact us for pricing'.
8
currencies, selected automatically from your locale, so the rate you see is the one you would be quoted.
100%
of the code, models, prompts and evaluation sets we build for you, in your own repository from the first commit.
0
proprietary runtimes you have to keep licensing from us after an engagement ends.
07 / Commitments
What you can hold us to.
- Ownership
- Code, models, prompts, evaluation sets and documentation are yours, in your repository, from the first commit. We do not hold artefacts hostage and there is no proprietary runtime you have to keep licensing from us.
- Your infrastructure
- We deploy into your cloud account, or on-premise where data residency requires it. If you would rather we host it, that is a separate, explicitly-priced decision — never a default.
- Honest scoping
- If your problem is better solved by fixing a data-entry form, buying an off-the-shelf tool, or writing a hundred lines of SQL, we will tell you in the diagnostic and invoice you for the diagnostic only.
- Named engineers
- You meet the people who will do the work, and they stay on the engagement. We do not sell senior staff and staff the delivery with juniors.
- Evaluation before claims
- Every performance number we quote to you comes with the test set, the baseline it beats, and the conditions under which it fails.
Next step
Bring us the problem before you've picked the solution.
The most useful conversations start with a process that is slow, expensive or error-prone — not with a technology you have been told to adopt. Tell us what is not working and we will tell you honestly whether this is our kind of problem.