From enterprise chatbots and RAG knowledge systems to autonomous agents and fine-tuned domain models. Kovil AI engineers build production generative AI applications that solve real business problems, not demos.
Generative AI development is the engineering discipline of integrating large language and multimodal models into working products. This is not prompt writing. It involves architecture decisions across the retrieval layer, orchestration layer, evaluation suite, and production infrastructure.
Most companies underestimate the gap between a working demo and a production system. A chatbot demo is a weekend project. A chatbot that handles 10,000 conversations a day with consistent quality, auditability, and fallback handling is a 6-10 week engineering effort.
Kovil AI brings engineers who have shipped generative AI systems at that level of complexity. We scope accurately, build the evaluation infrastructure from day one, and deliver code you own and can operate.
Scope, timeline, and model recommendations for the most common enterprise GenAI applications.
| Application | Build Timeline | Complexity | Recommended Model | Key Challenge |
|---|---|---|---|---|
| Enterprise Chatbot | 6-8 weeks | Medium | GPT-4o / Claude Sonnet | Consistent quality at scale, escalation logic |
| RAG Knowledge System | 6-10 weeks | High | Any LLM + Pinecone | Retrieval quality, hallucination control |
| Document Extraction | 4-6 weeks | Medium | GPT-4o / Claude Opus | Structured output accuracy, edge cases |
| Autonomous Agent | 8-12 weeks | Very High | GPT-4o + LangGraph | Reliability, loop control, error recovery |
| Fine-Tuned Domain Model | 10-14 weeks | Very High | Llama 3 / GPT-4o-mini | Data labelling quality, eval regression |
| Image Generation Pipeline | 3-5 weeks | Low-Medium | DALL-E 3 / SD XL | Style consistency, brand compliance |
Six core generative AI application types Kovil AI has shipped in production.
Customer support, internal knowledge assistants, HR bots, IT helpdesks. Multi-turn conversation with memory, escalation logic, and analytics dashboards.
Contract analysis, invoice extraction, report summarisation, regulatory document review. Structured output with citation links back to source documents.
Internal knowledge bases, technical documentation assistants, research Q&A. Hybrid search, re-ranking, RAGAS evaluation, and source citations built in.
Task automation agents that browse, write code, query APIs, and complete multi-step workflows. Human-in-the-loop checkpoints for high-stakes actions.
Marketing asset generation, product image variants, personalised content at scale. Style consistency enforcement and brand guideline compliance.
Domain-specific models for medical coding, legal clause classification, financial sentiment. Requires labelled training data; delivers higher accuracy at lower inference cost.
We start with a 2-week discovery sprint to validate your use case and de-risk the build before committing to full development.
Book a Discovery SprintFour layers every production GenAI system needs. We handle all of them.
A mid-market asset manager needed to automate regulatory compliance Q&A across 8,000 pages of SEC filings, internal policy documents, and FINRA guidance. We built a RAG system with hybrid search, clause-level citations, and a confidence-gating layer that escalates to a human compliance officer when the model is uncertain.
Generative AI development is the process of building applications that use large language models, image generation models, or multimodal models to create text, code, images, audio, or structured data. This includes chatbots, RAG pipelines, AI agents, document automation systems, and fine-tuned domain-specific models.
A production-ready AI chatbot with RAG takes 6-10 weeks. A simple API integration takes 1-2 weeks. A custom fine-tuned model with evaluation infrastructure takes 10-14 weeks. Timeline depends on data availability, integration complexity, and how many edge cases need to be handled in the evaluation suite.
We work with the full model landscape: GPT-4o and GPT-4o mini (OpenAI), Claude Sonnet and Opus (Anthropic), Gemini 1.5 Pro (Google), and open-source models including Llama 3, Mistral, Mixtral, and Phi-3. Model selection is driven by your accuracy requirements, latency targets, privacy constraints, and infrastructure preferences.
Use RAG when your knowledge is large, frequently updated, or needs source citation. Use fine-tuning when you need a specific output style, domain vocabulary, or structured output format that prompt engineering alone cannot reliably produce. The most powerful systems often combine both: a fine-tuned model that retrieves from a RAG pipeline.
We use evaluation frameworks including RAGAS (for RAG quality: context recall, faithfulness, answer relevancy), LLM-as-judge for open-ended quality, human review for edge cases, and task-specific metrics like F1 for extraction tasks or BLEU/ROUGE for summarisation. Every production system we ship includes a regression eval suite that runs on each deployment.
Yes. We build fully private GenAI stacks using open-source models (Llama 3, Mistral) hosted on your own infrastructure (AWS, Azure, GCP, or on-premise). Documents never leave your environment. For regulated industries like healthcare and finance, we design the architecture to meet HIPAA, SOC 2, and GDPR requirements from the start.
It starts with a scoping call to define the use case, success metrics, and data availability. We then run a 2-week discovery sprint to prototype and validate the approach before committing to a full build. Full builds follow 2-week sprints with weekly demos. You own all code and model weights at completion.
Hallucination control is layered: RAG with source grounding for factual questions, structured output schemas (JSON mode, function calling) to constrain output format, RAGAS faithfulness scoring in the eval suite, confidence thresholds that trigger human escalation, and citation enforcement so users can verify claims. No system eliminates hallucination entirely, but these layers reduce it to acceptable rates for most use cases.
We scope your use case, validate the approach in 2 weeks, and ship a production-ready system. All code and models are yours to own.
Start the Conversation