Codevibe's AI service builds production systems - LLM integration, RAG pipelines, workflow automation, and custom agents - grounded in your real data and workflows, not demos. Engagements typically run 4 to 12 weeks, delivered by senior engineers with evaluation and guardrails built in, and measured against real business outcomes rather than novelty.
- Typical engagement
- Proof-of-concept to production AI feature
- Timeline
- 4 to 12 weeks by scope
- Stack
- LLMs (GPT/Claude/Gemini), RAG, Python/Node
- Delivery model
- Senior-led; outcome-focused, evaluated
- Based in
- Gurgaon, India - clients globally
Built for your stage.
LLM-Powered Products
Chat interfaces, AI assistants, document Q&A systems, and content generation tools powered by GPT-4o, Claude, or Gemini.
RAG Systems
Retrieval-Augmented Generation - AI that answers questions from your own data. Accurate, auditable, hallucination-reduced.
Workflow Automation
Intelligent automation pipelines that replace repetitive manual processes - extraction, classification, routing, summarisation.
AI-Assisted Internal Tools
Internal tools that use AI to surface insights, draft content, route requests, or flag anomalies.
Custom Model Integration
Fine-tuning and integrating smaller, domain-specific models where a general-purpose LLM would be overkill.
Evaluation & Prompt Engineering
Systematic evaluation frameworks, prompt optimisation, and output quality measurement for AI in production.
Right tools. Right reasons.
| Layer | Technologies | Why we use it |
|---|---|---|
| Foundation Models | GPT-4oClaudeGemini | Selected based on capability, cost, and latency requirements. |
| Open Source | Llama 3MistralPhi-3 | For cost control, data privacy, or specialised domains. |
| Vector DBs | PineconepgvectorWeaviate | Semantic search and retrieval for RAG systems. |
| Orchestration | LangChainLlamaIndexCustom | Multi-step agent flows and document processing pipelines. |
| Deployment | AWS SageMakerLambdaECS | Scalable, monitored, cost-controlled inference. |
| Evaluation | Custom eval harnessesPromptfoo | Regression testing for AI outputs - not just vibes. |
Start small. Validate fast.
AI Feasibility Sprint
We assess your use case, data, and constraints - and give you an honest recommendation on whether AI will deliver the ROI you're hoping for.
Discovery onlyPrototype Build
A working proof-of-concept with real data, ready to demo to stakeholders or test with real users. No hype - a real, working thing.
Most popular startProduction Build
Full build with reliability, evaluation, monitoring, and documentation to the same standard as all Codevibe engineering.
After validationWhat an engagement looks like.
What a typical engagement includes
| Area | What’s included |
|---|---|
| Discovery & use-case fit | Identify high-ROI workflows, assess data readiness, and a build-vs-buy plan. |
| Data & retrieval | Pipelines, embeddings, and RAG retrieval over your own content. |
| AI build | LLM integration, prompt and agent design, and guardrails against hallucination. |
| Automation | Connecting the model to your tools — CRM, email, WhatsApp, and internal APIs. |
| Evaluation & handover | Accuracy testing, monitoring, cost controls, and documentation. |
We start from the workflow, not the model — the goal is measurable time or revenue saved, not AI for its own sake.
Indicative pricing
Indicative ranges for scoping only — every project is quoted to a fixed price against your feature list before we start.
Typical timeline
Selected work
Travalot: Travel Platform Digital Transformation
AI sales tooling in production: an automated call-audit system and automated lead assignment built into a live travel platform.
Read the case study →Utazzo Holidays: Travel Platform Rebuild
A rebuilt travel platform with AI automating the sales pipeline across the web app, CRM, and admin console.
Read the case study →More on building real AI products.
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Frequently asked questions
What kinds of AI features do you build?
LLM-powered chat and assistants, document Q&A with RAG, workflow automation (extraction, classification, routing, summarisation), and custom agents - plus the evaluation frameworks that keep them reliable once they are in production.
How do you stop the AI from hallucinating?
We ground models in your own data using retrieval (RAG), constrain outputs, and build evaluation harnesses that measure accuracy before and after launch. The goal is auditable, reliable answers - not a demo that impresses once and fails in the wild.
How much does an AI project cost?
A focused proof-of-concept can start around ₹3 to 5 lakh; a production feature with RAG, integrations, and evaluation typically runs ₹8 lakh and up, depending on your data and scale.
Which AI models do you use?
We are model-agnostic - GPT, Claude, Gemini, or smaller domain-specific models - and pick per use case based on accuracy, cost, latency, and data-residency needs.
Will our data be used to train external models?
No. We architect around your privacy requirements, use enterprise or API tiers that do not train on your data, and can keep sensitive workloads inside your own cloud environment.
We'll tell you honestly if AI is the right call.
Start with a feasibility sprint. If it makes sense, we build it properly. If it doesn't, we'll tell you that too.