Practical notes from the engineers who build agents, copilots and AI products.
LLM application development takes eight to twelve weeks for a single workflow and twelve to sixteen for two or three connected ones, including two to four weeks of shadow mode. Discovery adds ten days if the workflow is not yet chosen. Integrations and approvals, not model work, cause most slippage.
A LLM application development RFP should name one workflow, the measured baseline, the accuracy and latency thresholds that define acceptance, the systems to integrate, the data residency rule, the ownership terms and the support expectation. Without thresholds, bids are not comparable and the quotes will not hold.
The hidden costs of LLM application development are inference that scales with usage rather than seats, integration work behind each connector, the evaluation suite and its upkeep, migration when a model is deprecated, human review during rollout, and the monthly care that stops quality drifting.
Ask a LLM application development vendor five things: how they measure accuracy, who owns the code and prompts, what the system costs to run, what happens when the model is wrong, and how you leave. Vendors who have shipped answer all five with numbers and artefacts.
LLM application development in India typically costs ₹13,60,000 to ₹56,00,000, or $21,000 to $84,000, for a production build. The spread is set by integration count and evaluation depth, not by the model. Here is what drives the price, which delivery model fits, and which data rules apply.
Buy when the workflow is common to your industry and the differentiation sits elsewhere. Build when the workflow is how you compete, when the data is yours alone, or when the integration surface is unusual. Most companies should do both, on different problems, at the same time.
The ROI of AI copilot development is the annual value created divided by the build price plus twelve months of running and support cost. With builds from $19,500 or ₹12,80,000, the case holds when you can name the hours removed or the revenue retained, and collapses when it rests on a productivity percentage.
Eight to twelve weeks for a first AI copilot release covering three jobs, from kick-off to a beta cohort using it daily. Two of those weeks are evaluation and shadow running, not features. Missing API endpoints, undecided scope and self-hosted deployment are what push the timeline out.
An AI copilot development RFP needs nine things: the named jobs, the systems and API surface, the data and permission model, the action list with approval thresholds, acceptance criteria as numbers, the evaluation method, security and residency terms, ownership of code and prompts, and the support model after launch.