AI & LLM Integration Services
Computer Kingdom integrates large language models — Claude, OpenAI, Gemini, and open-source alternatives — into the systems Indian businesses actually run. We are not a generic AI consultancy and we do not chase hype: we ship working AI features into your CRM, your customer-support tooling, your internal back-office, your call centre, your knowledge base, and your customer-facing products. The work is grounded in 25+ years of building real software, not in slide decks.
Most of our AI engagements take an existing process where a person spends 30-60 minutes doing a task that mostly follows a pattern, and either automates it end-to-end or builds an AI-assisted interface that cuts the time to a few minutes with a human reviewing the output. That framing — concrete process, concrete time saving — is how we keep AI projects from becoming research experiments.
What We Build
Common engagements include:
- Custom AI assistants — domain-specific chat interfaces that answer questions over YOUR documents, codebase, ticket history, or product data.
- Retrieval-augmented generation (RAG) — vector search + LLM stack to ground answers in your data and avoid hallucinations.
- AI-assisted internal tools — draft email replies, summarise tickets, classify support volume, extract structured data from unstructured input.
- Document understanding — extract structured data from PDFs, scanned documents, contracts, invoices — even handwritten Indian-language forms.
- Conversational interfaces — WhatsApp, web-chat, and voice chatbots that handle real customer queries with context awareness, not scripted FAQs.
- AI in DialPro & call centres — real-time agent assist, post-call summarisation, sentiment scoring, automated QA on call recordings.
- AI agents & workflow automation — multi-step LLM agents that read systems, take actions, and report results — with human-in-the-loop where it matters.
- Compliance & safety guardrails — PII redaction, prompt injection defences, output validation, audit logs — AI that survives a security review.
AI Project Process
AI projects fail more often than regular software because teams skip the framing step. Our process is built to avoid that.
- Process discovery — we map a specific business process step-by-step and identify where AI helps versus where it's the wrong tool.
- Data and access audit — what content does the model need access to, what's its quality, what are the privacy / compliance constraints?
- Model selection — Claude, GPT-4 / GPT-4o, Gemini, or open-source (Llama, Mistral) — chosen based on quality, cost, latency, and data residency requirements.
- Prototype & eval — we ship a measurable prototype within 2-4 weeks with a documented evaluation set so you can judge quality, not vibes.
- Production hardening — cost controls, prompt versioning, output validation, observability, fall-backs when the model fails.
- Iterate on real usage — the first version is never final. We instrument, watch real traffic, and improve weekly for 90 days post-launch.
Technologies We Work With
We work with the major model providers and the supporting infrastructure that turns a model into a production feature.
- Models & APIs: Anthropic Claude, OpenAI (GPT-4 / GPT-4o), Google Gemini, Mistral, Llama, Cohere
- RAG & vector search: pgvector, Pinecone, Qdrant, Weaviate, ChromaDB
- Frameworks: LangChain, LlamaIndex, Anthropic SDK, OpenAI SDK, Vercel AI SDK
- Backend: Python (FastAPI, Django), Node.js, .NET
- Frontend: React, Next.js, Vue, plain HTML for embedded widgets
- Speech & multimodal: Whisper / faster-whisper for STT, ElevenLabs and Azure Neural for TTS, Claude / GPT for vision
- Deployment: AWS Bedrock, Azure OpenAI Service, self-hosted on-premises for data-sensitive clients, Cloudflare Workers AI for low-latency edge use cases
Why Choose Computer Kingdom
- 25+ years of track record. We have been delivering custom IT work in Pune since 1999.
- Local Pune presence. Our team is based at M.G. Road, Camp — in-person meetings and local support are easy.
- End-to-end delivery. We cover discovery, design, build, QA, deployment, and support under one roof.
- Pragmatic technology choices. We pick tools that match your team’s capacity to maintain the system long-term.
- Honest communication. You get direct access to the people doing the work. If something is slipping, you hear it from us early.
Frequently Asked Questions
Is AI ready for production use in Indian businesses today?
For specific tasks, yes — document understanding, customer-support summarisation, internal search, and content drafting are all production-grade in 2026. For autonomous AI agents that take actions without human review, the answer is more nuanced: we deploy them only where the failure cost is low and there's a verification step.
How do you handle data privacy and the DPDP Act?
We don't send sensitive data to LLM providers without contractual data-processing terms in place — Anthropic and OpenAI both offer enterprise contracts with no-training clauses and EU/US data residency. For DPDP-sensitive workloads we deploy open-source models (Llama, Mistral) on Indian cloud infrastructure or on-premises. Architecture is decided up-front based on the specific data classes involved.
Will an AI feature replace our employees?
Almost never, in the projects we ship. The best outcomes are AI doing the boring 80% of a task and a human reviewing or extending the last 20%. Volume capacity goes up, headcount usually stays roughly the same but does higher-value work.
How much does an AI project cost?
An MVP AI feature integrated into an existing system typically ranges ₹5L-₹15L for the build, plus ongoing API / inference costs (usually ₹10K-₹5L per month depending on volume). A larger AI-assisted internal platform with multiple workflows ranges ₹20L-₹50L+. We provide line-item estimates after a 2-week discovery.
How do you prevent hallucinations?
Three layers: (1) ground every answer in retrieved context using RAG, so the model cites your data rather than its training memory; (2) validate output structure and key facts before showing them to users; (3) instrument the system so wrong answers get flagged and fed back into evals. Hallucinations don't go to zero, but with these layers they go from 'frequent' to 'rare and recoverable'.
Can you train a custom model on our data?
We rarely recommend full fine-tuning for Indian SME use cases — it's expensive, requires a lot of data, and gets stale quickly. Instead we use RAG (retrieval-augmented generation), few-shot prompting, and prompt engineering to adapt frontier models to your domain. These approaches deliver 80-90% of the value of fine-tuning at 5-10% of the cost.
Will you support and maintain the AI feature after launch?
Yes. AI features need more post-launch attention than regular software because models change, prompts drift, and edge cases keep surfacing. We offer maintenance retainers covering model updates, prompt revision, eval expansion, and cost monitoring.
Start Your Project
Ready to discuss your requirements? Call +91 99609 03132, email rakesh@ecomputerkingdom.com, or send us a message. Initial consultations are free and no-obligation — we will give you an honest view of whether what you need is a good fit for us.