Your AI system should be an asset, not a liability.
We find out why RAG systems fail, then help engineering teams fix them. Retridge evaluates, secures, and builds production AI systems grounded in your organization's data.
Most AI pilots don't fail because the model isn't powerful enough.
They fail because the right information never gets retrieved, nobody defined what “good” means, the attack surface was never tested, and cost and latency were never measured. Four failure classes, four disciplines.
The system finds the wrong context.
Documents exist, but the right information never reaches the model. Chunking, embeddings, ranking, and metadata all quietly decide the answer.
Nobody knows what “good” means.
Teams test demos instead of measuring performance against real queries. Without a dataset, every release is a guess.
The attack surface was ignored.
Prompt injection, data leakage, and permission failures appear after launch — usually in front of the people you least want to show them to.
Costs and latency become unpredictable.
A convincing prototype becomes an unreliable production system. Token spend, p95 latency, and regressions arrive together.
The RAG
System Audit.
In two weeks, Retridge evaluates your AI system against 50–100 real user queries, classifies failures by root cause, analyzes reliability and cost, and delivers a prioritized remediation roadmap.
What you receive
Nine artifacts, not a slide deck of opinions. Every finding traces back to a specific query, a specific chunk, and a specific measurement.
The Retridge RAG Reliability Model
Five layers. Every audit, build, and training program is scored against the same structure — so improvement is measurable across releases and comparable across systems.
Retrieval Quality
Grounded Generation
Security
Production Reliability
Economics
We diagnose the whole retrieval chain.
A bad answer is rarely the model's fault. It is usually a decision made six stages earlier — in a parser, a chunk boundary, or a metadata filter. So we instrument the entire chain.
Four ways engineering teams work with us.
Diagnose, secure, build, or build capability in-house. Most clients start with the audit — it is the cheapest way to find out what is actually wrong.
RAG System Audit
Find exactly where an existing retrieval system fails — and what it will cost to fix.
AI Security & Red Teaming
Find prompt injection, leakage, access-control weaknesses, and misuse paths before attackers or users do.
Production RAG & Agents
Design and implement reliable RAG systems and agentic workflows over private organizational data.
Corporate AI Training
Train internal engineering, product, and leadership teams in RAG design, evaluation, and AI security.
We don't start with opinions. We start with evidence.
Four steps, run in the same order every time. The output of each one is written down, so you can check our reasoning rather than take our word for it.
Discover
Understand the system, users, data, and failure symptoms. Read the architecture, not the pitch deck.
Measure
Run real user queries through structured evaluation. Retrieval and generation scored separately.
Diagnose
Trace failures to retrieval, generation, data, orchestration, security, or architecture.
Improve
Prioritize fixes by user impact, engineering effort, reliability, security, and cost.
Build the AI capability inside your organization.
Technical workshops for engineering and product teams covering RAG architecture, evaluation, AI security, failure diagnosis, and production readiness.
Certificate in RAG & Context Engineering: Designing and Building Production-Grade AI Systems.
Capstone: building and defending a full retrieval-augmented system end to end.
Evaluation-first
Every engagement begins with measurable behavior rather than opinions.
Security-aware
Accuracy is not enough if the system can leak data or be manipulated.
Production-focused
Latency, observability, reliability, architecture, and cost all matter.
Reserved for a named client quote. Retridge publishes references only with written permission — nothing here is fabricated.
Built for teams where AI has to actually work.
You're already running AI
Your assistant performs well in demos but fails unpredictably with real users. “It hallucinates.” “It can't find the right documents.” “We don't trust it enough to launch.”
CTO · Head of EngineeringYou're preparing to launch
You're planning a first AI-on-our-data initiative and need evidence that retrieval, permissions, evaluation, and security are production-ready before it ships.
Product · Innovation leadYou're building internally
Your engineering team needs a repeatable RAG and evaluation discipline — not a one-off fix that decays after the next model upgrade.
Engineering managerYou're handling sensitive information
Your AI system operates over financial, legal, healthcare, operational, or proprietary business information, and needs a security assessment before launch.
Compliance-sensitive industriesRAG Engineering Notes
Your AI system doesn't need more guesswork.
Let's find out what's actually wrong.