RAG KNOWLEDGE BASE — AI SPRINT

Jake McMahon — ProductQuant

B2B SaaS · Product · Growth

Your internal knowledge, instantly searchable and conversational.

Your product teams or internal operations are managing vast stores of documents, support tickets, or process guides. A traditional search interface can’t surface the right answers. We build and deploy a retrieval-augmented generation system trained on your data, turning your knowledge base into a live, queryable feature.

Book a Scoped Call → See Deliverables ↓

Fixed price · Blueprint sprint with money-back guarantee · Full handoff

WHAT YOU HAVE AT THE END

RAG System Production-deployed, containerised, your environment

Accuracy Dashboard Query volume, correctness, unanswered questions

Integration Package API docs, sample code, deployment guides

Trained Model Embedding model + vector database — you own it

Update Pipeline Add documents, system retrains automatically

Fixed price · Everything stays with you

We build a search system trained on your documents.

Your team or your customers type a question in plain English. The system reads your documents and gives them the answer — with the exact source cited. Here’s what that looks like:

CUSTOMER SUPPORT

Customer asks “How do I cancel my enterprise plan?”

The system finds the answer in your terms doc, your help centre, and your internal policy — and gives the agent a single, sourced answer in seconds. No more digging through four PDFs.

PRODUCT FEATURE

You ship an “Ask our docs” feature inside your app

Your users search your knowledge base in plain language instead of keywords. They get the exact paragraph that answers their question — not ten articles that might.

INTERNAL OPS

New hire asks “What’s the refund policy for annual contracts?”

Instead of messaging three people on Slack, they type the question and get the answer with a link to the source document. Onboarding time drops. Slack noise drops.

SALES ENABLEMENT

Rep asks “What integrations do we support for healthcare?”

The system pulls from your integration docs, compliance guides, and case studies — and gives the rep a ready-to-send answer before the prospect finishes typing their follow-up.

MONITORING

Live Dashboard

Every system includes a live accuracy dashboard — query volume, answer quality, source citations tracked from day one.

OWNERSHIP

Full Handoff

Trained model, vector database, and update pipeline are yours permanently. No lock-in.

METHODOLOGY

3 Phases

Blueprint sprint, full build, deploy & hand off. Each phase has a clear deliverable before the next begins.

THE KNOWLEDGE IS THERE. THE ANSWERS AREN'T.

Unsearchable knowledge

“We have thousands of documents. When someone asks a question, we Ctrl+F through PDFs until someone gives up and asks on Slack.”

OPS TEAM LEAD

Manual answer hunting

“Support spends hours assembling answers from three different wikis. By the time they reply, the customer has already escalated.”

CS DIRECTOR

Static knowledge base

“We built a help centre. Nobody uses it because the search returns ten articles and none of them answer the actual question.”

PRODUCT MANAGER

AI feature stalled

“We have ‘AI search’ on the roadmap. Engineering scoped it at three months. That was six months ago.”

VP PRODUCT

WHAT THE BLUEPRINT SPRINT UNCOVERS

The gap between having knowledge and being able to use it.

Most search fails at retrieval, not generation

The bottleneck is rarely the LLM. It’s the retrieval layer — wrong chunks, missing context, broken metadata. The blueprint sprint maps exactly where your retrieval breaks down.

Document quality drives answer quality

Messy formatting, duplicate content, and inconsistent structure cause hallucinations. The audit identifies which documents need cleanup before the model can be trusted.

Users don’t search the way you think

Your team assumes users search by keyword. In practice, they ask full questions in natural language. The query analysis reveals the gap between what users ask and what your system can answer.

Accuracy without monitoring is a liability

A RAG system that answers questions without tracking whether those answers are correct degrades silently. The dashboard we build makes accuracy visible from day one.

WHY THIS IS DIFFERENT

A RAG system that nobody monitors is worse than no RAG system at all.

Most RAG implementations focus on getting answers out the door. The model generates something plausible, the team ships it, and nobody measures whether the answers are actually correct. Six months later, users have learned not to trust it.

We build the monitoring into the system from day one. Every answer is tracked for accuracy, source citation, and confidence. Your team sees which questions the system handles well and which need attention — before your users lose trust.

HOW IT WORKS

From document audit to live, queryable system.

PHASE 1

Discover

Audit your knowledge sources, define target query types and accuracy metrics, scope integration requirements.

→

PHASE 2

Build

Develop data pipeline, fine-tune retrieval models on your corpus, build answer generation layer and performance dashboard.

→

PHASE 3

Deploy & Hand Off

Deploy to your environment, integrate with your product or internal tools, hand over full control and documentation.

After handoff: your team adds documents and the system retrains — no engineering involvement needed.

WHAT YOU GET

Everything your team needs to launch and maintain the system.

CORE SYSTEM

Production RAG System

Fully deployed, containerised application that ingests your documents, processes queries, and returns sourced answers.

Integrates into your product or works as standalone internal tool
Runs in your environment — cloud or on-prem
Source citations on every answer for user trust

MONITORING

Accuracy & Performance Dashboard

Live monitoring interface showing query volume, answer correctness, and top unanswered questions.

Track ROI and prioritise knowledge updates
Spot accuracy drops before users notice
Exportable metrics for stakeholder reporting

INTEGRATION

Integration Package & Documentation

Complete API documentation, sample integration code, and deployment guides.

Your engineering team connects and manages the system independently
Sample frontend integration code included
Deployment and scaling documentation

ASSETS

Trained Model & Vector Database

Proprietary embedding model fine-tuned on your data corpus plus the associated vector database.

You own and control both assets
No vendor lock-in or ongoing licensing
Optimised for your specific document types

ONGOING

Knowledge Update Pipeline

Configured process that allows you to add new documents and expand the knowledge base without engineering involvement.

Cloud storage sync or API-based ingestion
Automatic reprocessing when new documents are added
No dependency on ProductQuant for ongoing updates

FIT CHECK

Is this the right sprint for your team?

GOOD FIT

Product teams adding AI search

The situation

You have a SaaS product and want to add an intelligent search or Q&A feature powered by your own data. Or your internal operations team manages thousands of documents that are hard to search. You need production-grade accuracy, not a prototype.

What changes

A live RAG system your users or internal team queries in plain language
Accuracy dashboard tracking answer quality from day one
Full ownership — model, data, and update pipeline stay with you

Your knowledge becomes instantly accessible — searchable, conversational, and monitored.

NOT A FIT

Too early for RAG

If you don’t have a meaningful volume of documents or knowledge to search against, the system won’t have enough data to deliver accurate answers. If you need a simple FAQ bot rather than document-grounded search, a simpler solution is a better fit.

Jake McMahon — ProductQuant

Jake McMahon

B2B SaaS · Product & Growth · Behavioural Psychology & Big Data (Master’s)

I build and deploy RAG systems for B2B SaaS products and enterprise operations teams. The work covers the full stack — document ingestion, embedding models, retrieval architecture, answer generation, and the monitoring layer that keeps it honest.

Every system I build comes with an accuracy dashboard and a knowledge update pipeline. You own the model, the data, and the infrastructure. No lock-in, no ongoing dependency.

What does my team need to provide?

Read-only access to your document sources and a point of contact who understands the query patterns your users or team members typically have. No engineering time required during the build.

Teams Jake has worked with

PRICING

One sprint. Full system. Everything stays with you.

$2,500–$3,500

Blueprint sprint · entry engagement

Fixed scope · Money-back guarantee

Audit of your existing knowledge sources and query patterns
Defined accuracy targets and success metrics for your use case
Technical architecture proposal for production deployment
Tested proof of concept returning answers from your documents
Full handoff of blueprint, architecture docs, and POC code
Recommendation for full build scope and timeline

Book a Call →

If the blueprint sprint doesn’t include a tested proof of concept that returns accurate answers from your documents, the sprint is free.

Questions.

Or book a call →

How do you define and measure answer accuracy? +

We define it with you during the Discover phase, based on your use case. Typically, it’s a percentage of queries where the returned answer is both correct and properly sourced from your documents, verified by a sample set of real queries.

What happens to our data? +

Your data is used exclusively to train your system. We do not retain it for other purposes. The final trained model and vector database are assets we hand over to you.

Can we update the knowledge base ourselves after deployment? +

Yes. We provide a configured update pipeline. Adding new documents triggers an automatic processing update without requiring our engineering involvement.

What if our internal documents are messy or unstructured? +

We start with an audit. The system includes a data preprocessing layer designed to handle common formats and inconsistencies. We identify any critical gaps that need manual cleanup during the Discover phase.

How is this different from just using an OpenAI API with our files? +

We build a system specifically fine-tuned on your corpus for retrieval accuracy and source grounding. It runs in your environment, on your data, giving you control, cost predictability, and higher accuracy for domain-specific queries than a generic API.