---
title: "RAG development: AI search over your own data"
description: "Senior engineers build RAG and AI search that finds the right content, cites it and stays fast, often inside the Postgres you already run."
url: https://zeroic.in/rag-development
---

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# RAG and AI search that finds the right answer in your data.

[Talk to an AI engineer](https://zeroic.in/book?service=retainers) [See the work](https://zeroic.in/work)

**Starting at**

$4,000/mo

**Commitment**

Monthly

**Products shipped**

40+

## Where most RAG projects are

Most RAG systems fail at retrieval, not generation. If the right passage never reaches the model, no prompt will save the answer. For an analytics app used by 1.5M+ people we built retrieval on pgvector inside the app's own Postgres, and we start every RAG project by measuring what search actually finds.

Answers miss what's in your docs → Retrieval measured on real questions

Search ignores exact names and codes → Keyword and vector search combined

A second database just for vectors → Vectors in the Postgres you already run

Nobody measures retrieval quality → Answers that cite their sources

## What goes into RAG that works

### Chunking that keeps meaning

Split documents by their structure, headings and sections, not a fixed character count, and keep titles and metadata with each chunk so filters and citations still work.

### Hybrid search and reranking

Vector search misses exact terms like product codes and names. Combining it with keyword search, then reranking the top results, finds what one method alone misses.

### pgvector before a new database

pgvector inside the Postgres you already run handles similar-question lookup, context for the model and caching answers by meaning, with no second database to keep in sync.

### Measured, not guessed

We build a test set of real questions and check whether the right passage comes back before tuning any prompt. Most quality problems turn out to be retrieval problems.

## How it works

1. 01

### Pick a tier

Part-time, dedicated or embedded, from $4,000 a month. Tiers differ in hours and in how closely we work with your team.
2. 02

### Measure retrieval first

A senior engineer builds a test set from real questions and measures what your search finds today, before changing it.
3. 03

### Ship every week

Retrieval, prompts and evals. The engineer doing the work is the one on your calls. Change tier or stop any month.

Starting at $4,000/mo

Part-time from $4,000/month, month to month. Building a new AI product around search? A fixed-price [MVP Sprint](https://zeroic.in/services/mvp-development) gets a first version live in 5-6 weeks.

[Talk to an AI engineer](https://zeroic.in/book?service=retainers)

Shankar Prasad and Sudhanshu Sharma have been with FormulaBot for 3+ years. We started as a simple English-to-SQL formula generator. It's now a 'chat with your data' product serving 1.5M+ users. They built it like it was theirs.

David Bressler

David Bressler Founder @ FormulaBot

## Work we've shipped

AI · Analytics

### [Better Analyst (formerly FormulaBot)](https://zeroic.in/formulabot)

An analytics app used by 1.5M+ people. We built pgvector retrieval and caching inside its own Postgres.

AI · EdTech

### [QuizBot](https://zeroic.in/quizbot)

AI that finds the key ideas in any text or URL and turns them into quizzes. 2,500+ users, then acquired.

AI · MVP → Retainer

### [Performalise](https://zeroic.in/performalise)

An engineering analytics suite. We built the pipeline that turns commits, PRs and deploys into one stream.

Further reading

- [pgvector vs Pinecone: when Postgres stops being enough](https://zeroic.in/blog/pgvector-vs-pinecone)
- [English-to-SQL at a million users: what breaks, in order](https://zeroic.in/blog/english-to-sql-at-1m-users)
- [GPT vs Claude vs Llama in production: choose by task type](https://zeroic.in/blog/gpt-vs-claude-vs-llama-production)

## How we can help

Four ways to work with us - pick what fits, switch when it doesn’t.

From $1,000 · 1-2 weeks

### [Audits](https://zeroic.in/services/ai-audit)

Find what's broken - or whether AI is worth it - before you build.

Learn more

From $12,000 · 5-6 weeks

### [MVP Sprint](https://zeroic.in/services/mvp-development)

Zero to production in 5-6 weeks. A real product, not a prototype.

Learn more

From $4,000/mo · Recurring

### [Retainer](https://zeroic.in/services/senior-developers)

A senior engineer embedded on your team, month to month.

Learn more

From $6,000 · 2-4 weeks

### [Code Rescue](https://zeroic.in/services/code-rescue)

Vibe-coded app breaking under real users? We rebuild it to hold.

Learn more

[Compare all services](https://zeroic.in/services)

FAQ

## Frequently asked questions

How much does RAG development cost?

Most RAG work runs on a retainer, from $4,000 a month for the part-time tier. A new product built around AI search can be an MVP Sprint, with one fixed price agreed before the build. Sprints start at $12,000. Want to know what your search finds today? An AI audit starts at $1,000.

Do we need a vector database like Pinecone?

Often not. pgvector inside Postgres handles more than most teams expect. We move to a dedicated vector database only when size, filtering or speed show it is needed.

How do you know the answers are right?

We measure retrieval and answers against a set of real questions, show the sources with every answer, and re-run the tests whenever content or prompts change.

Can it search our existing data?

Yes. Docs, help centres, PDFs and database records can all be indexed, with permissions so people only find what they are allowed to see.

Do we own everything you build?

Yes, 100%. You own the code, indexes, prompts, docs and credentials.

## Hear from us within 24 hours.

**Prashant and Khushi** Partners @ Zeroic

Want AI search over your data? Tell us what people need to find and where it lives. We'll suggest where to start and which tier fits.

Rather talk it through? [Book a 30-minute call](https://zeroic.in/book?service=retainers)

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