SERVICES · AI & AUTOMATION

AI That Works in Production.

Most AI projects fail not because the technology is wrong, but because the integration is shallow. We build AI capabilities that are reliable, measurable, and genuinely useful to your users or your team.

// WHAT WE DELIVER

Built for your stage.

01.

LLM-Powered Products

Chat interfaces, AI assistants, document Q&A systems, and content generation tools powered by GPT-4o, Claude, or Gemini.

02.

RAG Systems

Retrieval-Augmented Generation — AI that answers questions from your own data. Accurate, auditable, hallucination-reduced.

03.

Workflow Automation

Intelligent automation pipelines that replace repetitive manual processes — extraction, classification, routing, summarisation.

04.

AI-Assisted Internal Tools

Internal tools that use AI to surface insights, draft content, route requests, or flag anomalies.

05.

Custom Model Integration

Fine-tuning and integrating smaller, domain-specific models where a general-purpose LLM would be overkill.

06.

Evaluation & Prompt Engineering

Systematic evaluation frameworks, prompt optimisation, and output quality measurement for AI in production.

// TECHNOLOGY STACK

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.

// WHAT GOOD AI INTEGRATION LOOKS LIKE

Production AI.
Not demo AI.

Latency is managed

Users should not wait 8 seconds for a response. We use streaming, caching, and model selection to keep it fast — from the first interaction.

Costs are controlled

LLM calls at scale are expensive. We design token-efficient prompts, caching layers, and model tiers from the start — not as an afterthought.

Outputs are evaluated

AI outputs are tested against benchmarks, not just eyeballed in a demo. Regression testing catches quality degradation before your users do.

Failures are handled

When the model fails, times out, or produces nonsense, your system handles it without breaking. Graceful degradation is not optional.

// ENGAGEMENT APPROACH

Start small.
Validate fast.

Option 01 · 1 week

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 only

Option 02 · 2–4 weeks

Prototype 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.

Production Build

Full build with reliability, evaluation, monitoring, and documentation to the same standard as all Codevibe engineering.

After validation

// EXPLORE AN AI INTEGRATION

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.

hello@codevibe.in · +91 70677 09224 · Gurgaon, India