The AI development company that stays past the demo

That's the first question we try to answer, and often the answer is no. If the rule is stable and writable, a rules engine is cheaper, faster and easier to defend. We use discovery to test the idea against your data before you commit to a build, and we'd rather lose a project at that stage than deliver an expensive way to do something simple.

Both, and choosing between them is part of the job. Where a good product already exists and fits, integrating it is faster and cheaper, and we'll tell you so. We build custom AI solutions for business cases where no product covers the specific task, the data is proprietary, or the workflow is too particular for a generic tool. Often the answer is a mix — an off-the-shelf model for the generic parts, custom work only where it earns its cost.

The model work is similar; the surrounding requirements are not. Enterprise AI development has to account for access control, audit trails, data residency, and integration with systems like your ERP, CRM and identity provider — plus a governance story your compliance team can sign off. It also usually means deploying inside your own cloud accounts rather than a third-party tool. We build for those constraints from the start, because retrofitting them later is far more expensive than designing for them.

It depends on the task. Classification on your own labelled examples can work with a few thousand well-labelled records; a retrieval assistant over your documents needs the documents to be findable and current more than it needs volume. What kills projects is not quantity but inconsistency — two teams labelling the same thing differently will beat any model architecture. The data audit in step one tells you where you stand.

Three things drive cost, roughly in order: data readiness (cleaning and labelling is frequently the largest line item), the accuracy bar (moving from decent to reliable takes far more iteration than getting to decent), and ongoing inference spend, which is an operating cost you should model before you price the feature. On timeline, a focused feasibility and baseline usually takes a few weeks; a production-ready feature with monitoring is typically a few months, depending mostly on the state of your data.

No. Your data stays within your environment and your accounts wherever the architecture allows, and it isn't used for anything outside your project. Where a third-party model provider is involved we'll tell you exactly which one, what leaves your infrastructure, and what the retention terms are — before you sign off on the design.

Yes, and it's a common arrangement. Research teams often have strong modelling work that never reached production because the serving, monitoring and pipeline engineering wasn't their focus. We can take that hand-off and productionise it, or embed engineers alongside your team on shared infrastructure.

Our own in-house engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted. You meet the engineers who will be on your project before you sign, and the person demonstrating the work each sprint is the person who built it.

It will — user behaviour shifts, your catalogue changes, upstream data formats move. That's why monitoring for drift and accuracy is part of the deployment rather than an afterthought. You get alerting when scores fall below the agreed threshold and a documented retraining procedure. Most clients keep us on a support arrangement for exactly this, though the runbook is written so your team can run it alone.