Do you use OpenAI, Anthropic, or open-source models?

Whatever fits the job. We benchmark cost, quality, latency, and privacy needs against each. For sensitive data we can run open models in your own infrastructure so nothing leaves your environment.

Is my data safe and private?

Yes. We architect for privacy from the start - PII handling, data isolation, and self-hosting options when required. Your knowledge base is yours; we don't train public models on it.

What's the difference between RAG and fine-tuning?

RAG retrieves relevant facts from your data at query time and feeds them to the model - best for knowledge that changes. Fine-tuning bakes behavior into the model itself - best for tone and format. We often combine both and tell you which your use case needs.

How do you control AI costs?

We monitor token usage, cache aggressively, route simple requests to cheaper models, and tune prompts to be efficient. You get visibility into spend instead of a surprise bill.

AI Integration - LLM Agents & RAG Pipelines - Shunya

AI Integration

Put real AI to work inside your own product.

Custom LLM agents, retrieval-augmented generation, and automation workflows built on OpenAI, Anthropic, or open models - running in your secure infrastructure.

Overview

AI Integration

We help you leverage OpenAI, Anthropic, or open-source models like LLaMA to automate customer support, analyze data, and generate content - all within your own secure infrastructure. The work goes well beyond dropping a chatbot on a page: we design retrieval-augmented generation (RAG) pipelines grounded in your data, engineer and evaluate prompts, wire up tool-calling agents, and put guardrails in place so the output is accurate and on-brand. Why it matters: generic AI hallucinates and leaks context. AI that's grounded in your knowledge base, integrated into your workflows, and measured against real evaluations is the difference between a demo and a system your team and customers actually trust.

Shunya AI

Scaffold a Next.js 15 dashboard with auth and a Prisma schema.

Scaffolding App Router with Auth.js + Prisma. Generated 12 files in 3.1s.

app/layout.tsxlib/auth.tsprisma/schema.prisma

What we deliver

Everything that lands in your hands.

Custom RAG pipelines with a vector database grounded in your own content

Chatbot and assistant UI/UX, embedded in your product or site

Prompt engineering with evaluation harnesses to measure accuracy and regressions

API integration with OpenAI, Anthropic, or self-hosted open models

Tool-calling agents that take actions, not just answer questions

Fine-tuning and dataset curation where a base model isn't enough

Guardrails, PII handling, and cost/latency monitoring

Client analytics - live report

+18% MoM

Our process

How we ship it, step by step.

Use-Case Scoping

We identify where AI actually creates value for you, pick the right model and pattern (RAG vs. fine-tune vs. agent), and define how we'll measure 'good'.

Ground & Build

We ingest and chunk your data into a vector store, build the retrieval pipeline, and wire the model into your app with a clean interface.

Evaluate & Tune

We run prompts against an evaluation set, tune retrieval and prompting, and add guardrails so answers stay accurate, safe, and on-brand.

Deploy & Monitor

We ship to production with cost, latency, and quality monitoring - so you can see exactly how the system performs and what it spends.

Where it fits

Real scenarios this unblocks.

Most teams arrive with one of a handful of problems. Here are the ones we solve most often with ai integration - and what changes once we do.

Case 01

Support assistant grounded in your docs

A chat assistant that answers customer and internal questions from your own knowledge base, help center, and product docs - with citations - so it deflects tickets without inventing policies that don't exist.

Case 02

Document and data analysis at scale

An AI layer that ingests contracts, reports, or tickets and extracts structure, summaries, and insights in seconds - turning a folder no one has time to read into searchable, queryable answers.

Case 03

Tool-calling agent that takes action

An agent that doesn't just answer but acts - creating records, triggering workflows, and calling your APIs - with guardrails and human-in-the-loop approval where the stakes are high.

Case 04

Content and workflow automation

Drafting, classifying, tagging, and routing content or requests automatically, integrated into the tools your team already uses - so the busywork shrinks and people focus on judgment calls.

Tech stack

The tools behind the work.

We pick proven, modern technology - not whatever is trending - so what we build stays maintainable long after launch.

Models

OpenAIAnthropicLLaMA / open modelsEmbeddings

Retrieval

pgvectorPineconeChunking & rerankingHybrid search

Orchestration

LangChainCustom agentsTool callingEval harnesses

Platform

PythonTypeScriptGuardrailsCost & latency monitoring

Outcomes

What you can expect.

RAGGrounded in your own data

EvalMeasured accuracy, not vibes

SecureCan run entirely in your infra

CitedAnswers your team can stand behind

Investment

Transparent pricing, no quote walls.

Pick the tier that matches your stage. Final pricing depends on scope, complexity, and timeline - these are honest starting points in USD.

MVP / Startup

Rapid prototyping and launch for early-stage products.

starts at

$1,599

Next.js 15 Architecture
Responsive UI/UX (Tailwind)
Basic CMS Integration
Authentication (Auth.js)
Standard SEO Setup
Contact Form Integration
2 Weeks Support

Select

Popular

Scale / Business

Production-grade systems for growing businesses.

starts at

$3,499

Everything in MVP
PostgreSQL/Prisma DB
Payment Gateway (Stripe)
Admin Dashboard Panel
Advanced Animations (Framer)
90+ Performance Score
30 Days Support

Select

Enterprise

Complex distributed systems for large organizations.

project scope

$8,500

Microservices Architecture
Custom AI/LLM Integration
Real-time Systems (WebSockets)
Global CDN Strategy
RBAC & Audit Logs
Dedicated Project Manager
90 Days Priority Support

Select

Need a different currency or a custom configuration? See the full rate card.

FAQ

AI Integration,
answered

Common questions about how we approach ai integration.

Start a Project Contact Us

We minimize hallucination by grounding responses in your data with RAG, constraining the model with careful prompting and guardrails, and running an evaluation set before launch. The goal is answers your team can stand behind, with citations where it matters.

Let's build it

Ready to ship ai integration?

One conversation is enough for us to scope your project and tell you exactly how we'd deliver it.

Start a project All services

AI Integration

Everything that lands in your hands.

How we ship it, step by step.

Use-Case Scoping

Ground & Build

Evaluate & Tune

Deploy & Monitor

Real scenarios this unblocks.

Support assistant grounded in your docs

Document and data analysis at scale

Tool-calling agent that takes action

Content and workflow automation

The tools behind the work.

Models

Retrieval

Orchestration

Platform

What you can expect.

Transparent pricing, no quote walls.

MVP / Startup

Scale / Business

Enterprise

AI Integration, answered

Ready to ship ai integration?

AI Integration,
answered