Practical AI
engineered.

The short answer is 10 to 14 weeks for a well-scoped project. The longer answer is that "well-scoped" is doing a lot of work in that sentence. Most delays we see — not just in our projects, but across the industry — come from data that isn't ready, stakeholders who weren't involved early enough, or integrations that turned out to be more complex than the initial assessment suggested. We spend the first two weeks specifically to surface those risks before they become timeline problems. If your project involves legacy systems or regulated data, budget 16 to 24 weeks. We will tell you which applies to you before you sign anything.

Yes — and frankly, for any client handling sensitive data, we'd recommend it regardless of whether they asked. We are a Microsoft Solutions Partner for Data & AI, so building inside your private Azure tenant is something we do routinely, not occasionally. Private Link, VNet injection, your own encryption keys — all standard. If you want to avoid cloud entirely, we can deploy open-weight models like LLaMA or Mistral on your own hardware. The default on every engagement is simple: your data doesn't leave your environment. You have to explicitly decide otherwise.

Written SLAs, not verbal commitments. That distinction matters more than the numbers themselves. Standard support covers 99.5% uptime with a four-hour P1 response. Premium is 99.9% uptime, one-hour P1, 24/7 monitoring. Both include automated drift detection — which matters because AI models degrade quietly over time as your data changes, and most teams only notice when something goes visibly wrong. We catch it before that. Monthly performance reports and quarterly retraining reviews are included as standard. Healthcare and financial services clients can negotiate RTO, RPO, and penalty terms. Ask any vendor for their draft SLA before you sign. The answer tells you a lot.

Here is something worth saying plainly: no AI system is hallucination-proof. Anyone who tells you otherwise is either misinformed or overselling. What you can do is build an architecture that makes hallucination very difficult and catches it when it happens. We use retrieval-augmented generation so the model answers from your verified content rather than its own training memory. Confidence thresholds route uncertain outputs to a human reviewer before they reach anyone else. Guardrail classifiers handle out-of-scope responses at the edge. Before launch, every model is evaluated against a benchmark built from your actual documents. After launch, accuracy is monitored continuously. Flagged outputs feed the next retraining cycle. It is never a set-and-forget system.

Yes. And the detail that actually matters to most clients is this: augmented engineers work inside your tools, follow your processes, and are covered by NDA and IP assignment from day one — they are not contractors working at arm's length. We have certified engineers across Azure AI Engineer (AI-102), Azure Data Engineer (DP-203), Azure Solutions Architect (AZ-305), and Power Platform. Three ways to engage: full-time embedded resource, part-time sprint specialist, or a hybrid where our engineers lead while your team learns alongside them. Tell us what certification level and seniority you need. We match to that, not to whoever is currently available.

Nine to sixteen months to full ROI is the range we see most often. But the number that actually moves decisions is the per-document cost comparison. Manual processing — labour, QA, fixing errors — typically runs ₹15 to ₹40 per document. Automated pipelines bring that to ₹1.50 to ₹4, and process in under two minutes what used to take hours. At 10,000 documents a month, you are looking at ₹1 to ₹5 crore in annual savings. We have done this at 120,000+ documents a month. We cut manual data entry by 83% for an insurance client. Those are real numbers, not projections. Before we propose anything, we model ROI against your actual volume — conservative, base, and optimistic — because a business case that only works in the best-case scenario is not a business case.

Yes, and the certification that matters most here is ISO 27001:2022 — because it governs how we handle information security operationally, not just on paper. For HIPAA clients we execute a Business Associate Agreement before any protected health information is in scope. Encryption in transit and at rest, audit logging, minimum-necessary access — all standard. For GDPR, data residency, right-to-erasure, and data processing agreements are handled at the architecture stage, not retrofitted later. If you are doing vendor due diligence, ask us for controls documentation, penetration test summaries, and audit reports. We share them under NDA without making it a production. That is just part of how enterprise engagements work.