Generative AI development company in Pune, agents that do real work.
KINETECH is a generative AI development company in Pune that also builds agentic AI. We create AI assistants, retrieval-augmented generation (RAG) over your documents, copilots inside your tools and agents that complete multi-step tasks — on OpenAI, Anthropic, Gemini or open-source models, with evaluation and guardrails so answers stay grounded and safe.
- Grounded in your own data
- Evaluated before launch
- Human approval where it matters
Why hire a generative AI development
Generative AI projects succeed or fail on details: which documents the assistant may read, which actions an agent may take, and who signs off. A Pune-based team works your IST hours, can workshop those rules with your people face to face when useful, and knows the sectors that shape the city's economy.
Fast loops on prompts and policies
Tuning an assistant means daily rounds of test questions, reviewed answers and policy tweaks. Sharing a time zone keeps that loop tight, so a pilot improves week by week instead of stalling between calls.
Workshops that map real workflows
Before building an agent we map the workflow it will join. That can happen over video, or around a table in Pune if your team prefers — whichever gives the clearest picture of how work actually flows.
Practical about data privacy
We design with India's Digital Personal Data Protection Act, 2023 and your internal policies in mind: approved sources only, role-based access, full logging, and model choices that keep sensitive data where it belongs.
Vendor-neutral model choice
We are not tied to one model provider. We benchmark OpenAI, Anthropic, Gemini and open-source models on your own questions and choose on accuracy, latency, cost and privacy.
Generative AI solutions
We build four kinds of generative AI system: knowledge assistants that answer from your documents with citations, copilots embedded in the tools your staff already use, agents that carry out multi-step tasks through your APIs, and customer-facing assistants. Every one ships with an evaluation set and guardrails.
RAG knowledge assistants
Chat over policies, manuals, SOPs and product catalogues, retrieving from a vector database such as Pinecone or Weaviate and citing sources so staff can verify every answer.
AI copilots in your tools
Assistants inside your CRM, helpdesk or internal portal that summarise records, draft replies and suggest next steps without switching windows.
Agentic workflows
Agents that read a request, call your systems through approved tools and complete tasks like order lookups, ticket routing or report preparation, pausing for approval on anything risky.
Customer-facing assistants
Website and messaging assistants that answer common questions, capture leads and hand over cleanly to a human, with multilingual replies where the chosen model handles them well.
Evaluation and guardrails
Test sets built from real questions, automatic scoring, prompt-injection defences and output filters, so you know the quality before your customers do.
Generative AI use cases
Pune's mix of IT services, manufacturing, education and financial services gives generative AI plenty of practical work: searching technical documentation, answering student and customer queries, drafting reports and pulling facts out of long documents. The common thread is a lot of text and the same questions asked again and again.
IT services & product companies
Internal assistants over runbooks and code documentation, and support copilots that draft first replies for engineering teams in Hinjewadi, Kharadi or Baner.
Manufacturing & engineering
Assistants that answer from maintenance manuals, quality procedures and supplier specifications, helping shop-floor and service teams find the right step faster.
Education & coaching
Admission and course-query assistants for institutes, and study helpers that explain concepts from approved material rather than the open internet.
BFSI & insurance
Assistants that summarise policy documents and loan files, and agents that pre-check applications against rules before a person makes the decision.
From pilot to production
We take generative AI from idea to production in five steps: choose one workflow and a success measure, connect approved data, build a pilot against an evaluation set, roll it out to a small group with human review, then widen access as quality holds. Each step ends with a clear go or no-go check.
- 01
Use-case selection
Pick one workflow with real volume and a measurable outcome, such as answer accuracy or time saved per ticket.
- 02
Data and tool access
Connect approved documents, databases and APIs, with permissions that mirror who may see what today.
- 03
Pilot with an evaluation set
Build prompts, retrieval and agent tools with LangChain or LlamaIndex, scored against real questions.
- 04
Controlled rollout
Release to a small group with feedback buttons, logging and human approval on sensitive actions.
- 05
Monitor and expand
Track quality, cost and usage, refresh the knowledge base, then extend to the next workflow.
The tools
We stay model-agnostic and pick the stack per use case. Common building blocks include:
KINETECH,
- Based in Pune, Maharashtra, India
- Working hours Monday to Friday, 9:00–18:00 IST
- Startups, SMEs and enterprises in India and worldwide
- Instant ballpark from our online pricing estimator
Questions from
01How much does generative AI development cost in Pune?
Cost depends on the number of data sources, whether the assistant only answers or also takes actions, your security requirements and expected usage, which drives model API spend. A narrow pilot costs a fraction of an enterprise rollout. Try our pricing estimator for a ballpark, then share your use case so we can scope it accurately.
02What is the difference between generative AI and agentic AI?
Generative AI produces content, such as an answer, summary or draft, in response to a prompt. Agentic AI adds planning and tools: the system breaks a goal into steps, calls software such as your CRM or database, checks the results and continues until the task is done. Agents therefore need stricter guardrails and approvals.
03Will our data be used to train public AI models?
Not through anything we build. We choose provider plans whose terms exclude training on your prompts, or self-hosted open-source models, keep retrieval indexes in your own cloud where required, and restrict each assistant to approved sources. We also document the data flow so your security team can review it.
04How do you stop AI from making things up?
We ground answers in your documents with retrieval, require citations and instruct the model to say when it does not know. Before launch we score it against a test set of real questions, and after launch we review flagged answers. Hallucination cannot be removed entirely, so high-stakes actions keep a human approval step.
05Can an AI agent work with our existing software?
Yes. Agents act through tools we define, usually your existing APIs for CRMs, ERPs, ticketing or databases. Each tool has narrow permissions, every call is logged, and risky actions such as refunds or record changes can require a person to approve them before they run.
06Why hire a generative AI company in Pune rather than abroad?
You get the same models and frameworks either way. A Pune company adds IST working hours, easier in-person workshops when you want them, and familiarity with India's data protection expectations and the way local businesses operate. For teams outside India, we also work comfortably across time zones.
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Ready to pilot generative AI?
Bring one workflow your team repeats every day. We will show you what an assistant or agent could take off their plate.
Pune, Maharashtra · Mon–Fri, 9:00–18:00 IST