Generative AI that reaches production

Generative AI services cover the work between a business problem and a running AI system: deciding which use cases are worth building, choosing the models and architecture to build them on, engineering the application around the model, and operating it once real users depend on it. In practice that means generative AI consulting and readiness assessment, custom LLM application development, retrieval-augmented generation over your own content, AI agents that take actions in your systems, and the deployment and monitoring that keep accuracy from drifting. The model itself is rarely the hard part. The retrieval layer, the guardrails, the integrations, and the evaluation harness are where a demo becomes a production system.

It removes the work that scales linearly with headcount. Voice automation let one partner run citizen surveys at 500,000 calls per day, volume no calling floor could absorb, while cutting per-minute cost by 60%. An interview platform we built scores five behavioural dimensions and produces a full evidence-cited report in under three minutes, work that previously meant a human rewatching a recording. A learning platform turns each uploaded chapter into a quiz, flashcard set, mindmap, and summary in roughly 30 to 60 seconds, with no manual authoring effort behind it. The pattern is consistent: AI absorbs the high-volume, repetitive judgment work, and people keep the exceptions and the decisions that matter.

Retrieval-augmented generation grounds a language model in your own documents rather than its training data. When a question arrives, the system retrieves the passages most likely to contain the answer, then asks the model to answer from those passages and cite them. Retrieval is where most deployments break, not generation: a well-tuned prompt still answers wrong when the passages it receives are incomplete or off-topic. On our PrismBot RAG pipeline, structured ingestion before embedding, multi-query expansion, and reranking raised retrieval relevance roughly fourfold against single-query top-K search, with vector search staying under 300ms as the corpus grew.

Yes, and we build them as state machines with explicit phase transitions rather than a single prompt looping over tools. A flat prompt doing retrieval, context assembly, and answer generation is quick to write and hard to fix, because debugging turns into prompt roulette. Our production agent systems run on LangGraph with separate agents owning retrieval and query expansion, context enrichment, response generation, and a grounding check that decides whether the system has the evidence to answer at all. Each agent is independently testable, and when the grounding check fails the conversation escalates to a human with the transcript and retrieved passages attached.

Yes. For Indian languages we build on Bhashini's Dhruva inference APIs, which gave one public welfare program speech recognition, neural machine translation, and speech synthesis across 14 Indian languages in a single pipeline running under two seconds end to end, with no human translators in the loop. For other markets we integrate commercial speech and translation providers behind the same interface. The pipeline is provider-agnostic by design, so adding a language or a market is a routing change rather than a rebuild.

Cost splits into build and run, and the run cost is the one that surprises people. A focused RAG assistant or a single-workflow agent is a small build; a multi-agent platform with voice, integrations, and compliance requirements is a large one. Running cost depends on model choice, token volume, and where inference happens, and it is controllable: we routinely pair a frontier model for quality with a self-hosted open-source model for cost and data sovereignty, as our learning platform does with OpenAI gpt-4.1 and a Llama 3.1 fallback. We size both numbers in discovery and tell you when the honest answer is that AI is not the cheapest way to solve your problem.

A working proof of concept against your real data takes weeks, not months, and that is deliberate: validating accuracy on your actual content early is what stops a project spending a quarter on something the data will not support. Production hardening is the longer phase, because guardrails, evaluation, integrations, monitoring, and fallback behaviour are what separate a demo from a system people depend on. The PrismBot RAG implementation ran roughly three months end to end. Timelines move with integration surface and compliance requirements more than with model complexity.

The strongest fit is anywhere high volumes of unstructured language meet repetitive judgment. We have shipped production systems in government and public sector work, where multilingual voice reaches citizens in their own language; education, where content generation and assessment scale a small teaching team; human resources, where structured interviews and evidence-anchored scoring replace rewatching recordings; and customer support, where retrieval over company documents answers questions that previously queued for a person. Healthcare, BFSI, and retail follow the same pattern, with the added requirement that inference often has to run inside your own infrastructure, which is why we build for on-premise deployment as a first-class option.