Insights, tips, and updates on digital transformation, app development, and more.
MCP went from spec to default in 18 months — 78% of enterprise AI teams now have at least one MCP-backed agent in production. Here is what to actually build, what to buy, and how to run it safely.
AI search is now ~18% of all queries and rising. Here is how to structure content, schema, and authority so generative engines cite your brand — not your competitor — in their answers.
A practical evaluation framework for hiring an AI development company — what to ask, what to test, what red flags actually matter, and how to structure the contract.
Real pricing ranges for chatbots, copilots, agents and enterprise AI platforms in 2026 — what drives the budget, where teams under-estimate, and how to forecast year-two cost of ownership.
Most AI MVPs slip because the scope grew, not because the team underdelivered. Here is the scoping discipline that ships in 60 days and lets you learn.
Red-teaming is no longer optional once your AI feature touches customers. Here is the playbook we run before any regulated-industry system goes live.
AI tutors that answer questions are easy to build and rarely effective. The ones that help learners do the harder work of asking the right next question.
Most on-device AI demos run on Pixels. The Indian mid-market runs on RAM-constrained Snapdragon 4-series. Here is what survives that.
Western retail CV models assume well-lit aisles and predictable SKU layouts. Indian retail breaks both. Here is the stack that actually performs in kirana and modern trade.
Synthetic data fills gaps for English models. For Indian languages it can do the opposite — entrenching translation artifacts rather than fixing data scarcity.
Most teams add LLM observability after the first production incident. By then the gaps in telemetry are expensive. Here is the day-one stack.
Retrieval and generation each need their own evaluation. Mix them together and you cannot tell what is broken when quality regresses.
Most teams overpay for LLM inference by 40–60%. Here is the optimisation order we run on every production system before touching the model.
Most multi-agent systems in the wild are over-engineered single-agent loops. Here are the three orchestration patterns that survive production.
Buy a SaaS, build in-house, or hire a partner? The right answer is project-specific. Here is the decision framework we use.
Insurance underwriting AI sells well in demos. Surviving an actuarial team’s review is a different challenge. Here is what gets through.
Tier-2 logistics has different constraints than Mumbai or Bengaluru. Here is the AI dispatch pattern that actually works in Lucknow, Indore, and Kanpur.
PdM is sold as a unicorn. The real ROI in Indian manufacturing comes from three specific failure modes — and the rest is just instrumentation.
Government tendering on GeM is finally being reshaped by AI. Here is how vendors and buyers are using it — and what is still rules-bound.
Patient-facing AI in India sits at the intersection of HIPAA, DPDP, and the ABDM stack. Here is the architecture that satisfies all three.
Hindi-first voice agents for collections is one of the highest-ROI AI deployments in Indian BFSI. Here is the implementation pattern that works.
Vision-LLMs replaced rules-based OCR for KYC document extraction in 2025. Here is what changed and what you should ship in 2026.
The 40,000-items-a-day annotation pipeline isn’t magic. Here is the operational discipline that makes it work.
Most AI cold-outbound is loud spam at scale. Done right, it’s a careful research-and-write engine. Here is what separates the two.
Bulk scanning, Indic OCR, metadata indexing, DMS deployment — what actually works for court, land, and library archives in India.
AI voice agents work financially when the unit economics work. Here is the model we use to decide if your call centre is a candidate.
Headless Shopify plus AI personalisation is the highest-leverage replatform we ship for D2C brands. Here is what works and what does not.
App store policies tightened in 2025 across every major market. A practical compliance checklist for your next mobile submission.
Design systems are usually treated as a Series A luxury. They’re actually a pre-seed investment that pays back faster than almost anything else.
The next half-billion users coming online in India will not look like your existing user base. Here is how product design has to change to actually serve them.
Both stacks ship app-store-quality apps. Picking between them is mostly about your team, your ecosystem, and one or two narrow technical needs.
The App Router has been in production for two years now. Here is what we still recommend, what we quietly walked back, and what is finally worth adopting.
RPA worked when processes were rigid. With agentic AI, the bot can finally handle exceptions — which is most of what bank back-offices actually do.
You don’t need the FAANG stack. Here is the lean MLOps setup we deploy for Indian AI startups in their first six months.
Regulators don’t accept “the model said so.” Building AI for regulated industries means engineering for what the model isn’t allowed to say.
Four vector databases, four right answers depending on scale and stack. Here is the call I make for each, without the vendor pitch.
RAG and prompting handle most LLM work. But there is a real 10% where fine-tuning genuinely wins. How to tell the difference before you spend money on a training run.
Three frameworks, three philosophies. Here is the call I would make today depending on your team and your stakes.
Evals are the missing discipline in most LLM projects. Here is the eval stack we run on every Velura LLM build to catch regressions before they reach production.
If your retrieval pipeline only speaks English, you are locking out 800 million Indians. A practical guide to building RAG for Hindi, Tamil, Bengali and beyond.
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