Tell us what has to be custom, and why
If a general tool would do the job, the feasibility study will say so and you will have spent nothing.
Scope your custom AI build
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Custom AI Development · India
Custom AI development,
built around
your data and your process
Off-the-shelf models are trained on everyone’s data and fit nobody’s process exactly. Colan Infotech builds AI from your own records, your own vocabulary and your own decision rules.
- Trained on your data only
- 100% IP ownership
- On-premise option
- Free feasibility study
Bespoke model architecture where a general-purpose model will not do
Fine-tuning and retrieval over your own documents and transaction history
Deployed into the screens where the decision is actually made
Explainable outputs, because somebody has to act on them and defend them
Source code, model weights and documentation handed over at every milestone
Free AI feasibility study
Tell us what needs to be custom and why. We reply within one working day.
When off-the-shelf AI is the wrong purchase
Most people who reach this page have already tried a general tool or evaluated a platform. These are the five reasons they tell us it did not work.
01
It does not know your data
A general model has never seen your part numbers, your customer segments, your claim codes or your failure modes. It produces plausible output about a business that is not yours.
02
The platform caps you where it matters
You can configure within the vendor’s boundaries, and the boundary sits exactly at the thing that makes you different from your competitors. That is rarely the part worth standardising.
03
You cannot explain the output
A regulator, an auditor or a customer asks why the system decided that, and there is no answer. For credit, claims, pricing and clinical decisions that is not a limitation, it is a blocker.
04
Your data leaves your control
Sending your transaction history or your document corpus to a third-party endpoint is a commercial and compliance decision, not just a technical one. Sometimes it is simply not available to you.
05
It does not reach your systems
The output lives in a separate tool and somebody copies it across. A prediction that does not surface inside the workflow where the decision is made changes nothing.
06
Per-seat pricing that scales against you
Usage growth becomes a recurring bill set by someone else. A model you own has an infrastructure cost, not a licence that reprices every renewal.
What we build to order
Custom does not mean exotic. In most engagements it means a well-understood technique applied properly to data nobody else has.
Custom ML model development
Problem framing, feature engineering from your own records, model selection, training and evaluation against a held-out period of your history. Built for your data shape, not a tutorial dataset.
Custom LLM applications
Retrieval over your own document corpus, grounded assistants that cite the source record, structured extraction from your forms, and fine-tuning where your domain language genuinely differs.
Bespoke AI architectures
Where a standard approach does not fit: multi-stage pipelines, hybrid rule-and-model systems, custom loss functions tied to your actual cost of being wrong.
Custom predictive analytics
Forecasts built to your planning horizon, your seasonality and your constraints, surfaced as the number your planner actually needs rather than a generic time series.
AI product development
If AI is the product rather than an internal tool: model serving, API design, usage metering, multi-tenancy and the operational tooling a product needs to be supportable.
Private and on-premise deployment
Models that run inside your network or your cloud account, with no data leaving your boundary. Built this way from the start where compliance requires it.
How a custom AI engagement runs
Custom carries more risk than configuring a product, so the sequence is designed to surface that risk in week one rather than month six.
01
Day 1
NDA, then a technical call
We sign before you describe anything. The call is with an engineer, and the question is what specifically has to be custom and why the general option failed.
02
Week 1
Feasibility study on your data
A sample of your real records, examined properly. We report whether the outcome is achievable, what approach we would take, and where the risk sits. Free, and the write-up is yours.
03
Week 2
Written scope and fixed quote
The measurable definition of success, the evaluation method, milestones, dates and price. Agreed before any model work begins.
04
Weeks 3 to 8
Proof of concept, demonstrated weekly
A working model on your real data, shown every week and evaluated on a period it has not seen. You can stop at the end with no further commitment.
05
Then
Production build and integration
Serving infrastructure, integration into your systems, access control, audit logging, and the output surfaced where the decision is made.
06
Ongoing
Monitoring, drift and retraining
Tracked against real outcomes, not against the test set. Drift alerts and a retraining schedule, documented and handed to your team.
What you get in writing, before any model is trained
We cannot show you another company’s custom model, and most of our clients would not want us to. What we can do is put the things that usually go wrong into the contract.
100% IP ownership, from milestone one
Source code, trained model weights, pipelines and documentation transfer to you at every milestone, not at project close. Written as a contract clause. We retain no right to reuse any component for another client.
Your data is never used to train for anyone else
NDA signed before any technical discussion, not at proposal stage. Your data stays in your environment where you need it to, and is never pooled into a model we sell to someone else.
A feasibility verdict in week one
We look at a sample of your real data and tell you whether it can support the outcome you want, before you commit to a build. If the answer is no, you hear it in week one and you keep the written assessment.
A fixed, itemised scope before model work starts
What the pilot covers, what success is measured against, milestones, dates and price. Agreed in writing. Scope additions are priced and approved separately before work begins.
No black box delivery
You see the data pipeline, the features, the evaluation method and the code. Model decisions are explainable to the person who has to act on them, or we have not finished.
Weekly demos on your own data
Not on a sample dataset and not on slides. You see the model running against your real records every week, and you can stop at the end of the pilot.
We will tell you when AI is the wrong tool
A great deal of what gets sold as AI is a well-written rule. If a threshold and an alert solve your problem, that is what we will build, because it is cheaper, faster and easier to audit.
Monitoring, drift detection and retraining
A model that was accurate at handover and unmonitored at month nine is a liability. Performance tracking, drift alerts and a retraining schedule are part of the build, not an upsell.
A named team you meet before you sign
Engineers, project manager and a domain lead allocated and introduced during the feasibility study. You are not handed to a different team after the contract is signed.
Every item above is a contractual commitment, not a marketing claim. Ask us to point at the clause during the feasibility study and we will.
Custom or off-the-shelf: an honest comparison
We lose some of these conversations on purpose. A client who should have bought a product and was sold a build is an unhappy client in month four.
| Criterion | Off-the-shelf / platform | Custom build by us |
|---|---|---|
| Time to first value | Days to weeks | 4 to 8 weeks for a proof of concept |
| Upfront cost | Low | Higher |
| Recurring cost | Per seat or per call, repriced by the vendor | Infrastructure only |
| Fit to your process | Within the vendor’s boundaries | Built to it |
| Trained on your data | No | Yes |
| Explainability | Usually limited | Designed in |
| Data residency | Vendor’s terms | Your infrastructure if you want |
| Ownership | Licence | Asset you own |
If the left column fits your problem, use it and keep your money. We will say so in the feasibility study rather than after you have paid for a build.
Questions custom AI buyers ask
When the answer depends on data only you hold, when the output has to be explainable to a regulator or an auditor, when the data cannot leave your network, or when the prediction has to run inside a process the vendor’s product does not reach.
If none of those apply, a general tool is usually the better purchase and we will tell you that.
If none of those apply, a general tool is usually the better purchase and we will tell you that.
It depends on the problem, not on a universal number. What matters is whether the outcome you want to predict is recorded consistently, over enough history, with the drivers captured alongside it.
Some problems work on a few thousand well-labelled examples. Others need years. We check your specific case in week one rather than quoting a threshold.
Some problems work on a few thousand well-labelled examples. Others need years. We check your specific case in week one rather than quoting a threshold.
You own the model weights, the pipelines, the source code and the documentation, transferred at every milestone. Your training data remains yours and is never pooled into anything we build for another client. The NDA is signed before discovery, not at proposal stage.
Yes. On-premise and private cloud deployment are supported, and where compliance requires it we design for that from the start rather than retrofitting it.
There is a cost trade-off and we will be explicit about it during scoping.
There is a cost trade-off and we will be explicit about it during scoping.
Often that is exactly the right answer, and it is cheaper. Fine-tuning, retrieval augmentation and prompt engineering over a strong base model solve a large share of what gets described as a custom build.
Building an architecture from scratch is reserved for problems where the shape of the data or the constraint genuinely demands it. We will tell you which category you are in.
Building an architecture from scratch is reserved for problems where the shape of the data or the constraint genuinely demands it. We will tell you which category you are in.
By designing for it rather than adding it afterwards. Feature attribution, confidence scores, retrieval systems that cite the source record, and interfaces that show the reasoning next to the recommendation.
For regulated decisions we will also tell you where a simpler, more auditable model is the better engineering choice even if it is marginally less accurate.
For regulated decisions we will also tell you where a simpler, more auditable model is the better engineering choice even if it is marginally less accurate.
Then the feasibility study says so, you have spent nothing, and you keep the write-up. That outcome happens often enough that we build the study as a separable, free stage rather than folding it into a paid engagement.
Feasibility one week. Proof of concept four to eight weeks. Production build three to nine months depending on integration scope and whether the data pipelines already exist.
The pipelines are usually the variable. Modelling is rarely the long pole.
The pipelines are usually the variable. Modelling is rarely the long pole.
Custom AI, scoped honestly before it is sold
One week to a feasibility verdict on your own data. If custom is wrong for you, we will say so.
Technology-Driven Transformation with Real Business Growth
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