RAG Reliability Pilot — $15,000 Fixed-Scope Engagement | DODO LLC

AI SEMANTIC RELIABILITY PILOT — $15,000 FIXED-SCOPE ENGAGEMENT

Your RAG found the document. Did it miss the exception? Retrieved answers may sound correct while overlooking restrictions, historical corrections, conflicting sources, or conditions that change a decision.

DODO LLC helps teams operating AI assistants and retrieval-augmented generation (RAG) systems reproduce and investigate costly answer failures, then measure whether a bounded correction preserves the evidence and constraints.

THREE FAILURE CLASSES: (1) Hidden constraints: missed exceptions, prerequisites, temporal conditions, and scope boundaries. (2) Unsupported evidence: conflicting, incomplete, or dependent sources presented as authoritative. (3) Regressions: changes to the model, prompt, retrieval logic, or source corpus that silently break previously correct answers.

WHAT THE PILOT INCLUDES: One agreed, high-value failure class; a baseline of representative and adversarial questions grounded in customer-provided sources; an investigation of why the current system fails; one prioritized remediation experiment where feasible; before/after comparison and reusable regression tests; a written findings and limitations report.

HOW IT WORKS: 1. Agree on failure class, source access, and acceptance criteria. 2. Reproduce baseline failures. 3. Test one targeted improvement. 4. Verify on agreed examples and report limitations, including inconclusive findings.

PROPOSED INVESTMENT: $15,000 for a fixed-scope pilot, subject to qualification and written statement of work. $5,000 kickoff upon signed agreement; remaining $10,000 at agreed milestones. No payment is requested before scope and terms are agreed.

IMPORTANT BOUNDARIES: No guarantee of zero hallucinations, universal semantic completeness, regulatory certification, or specific cost savings. Sensitive data and security requirements must be agreed before access. Findings and measurable outcomes—not impressive architecture—determine success.

REQUEST A SCOPE DISCUSSION: Email cyrille.eloundou@dodollc.net with subject 'RAG Reliability Pilot'. Include one failure case, the source or expected answer, why the error matters, and what AI system is currently in use.

DODO LLC | Enterprise architecture, evidence assurance, and AI semantic reliability consulting.

Open-source proof: See QualityRag source code, PackSpec experiments, cross-format indexing and research limitations before engaging us.

Explore QualityRag on GitHub

Want to understand the research direction? Read our flagship article on semantic grammar, images, video and better information retrieval.

Your RAG Has the Information. Why Can't It Find the Answer?

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Experience the evidence failures before you book

Watch a narrated introduction to the QualityRag Evidence Engine and try the free 90-second RAG Failure Lab. It demonstrates three common failure modes with synthetic evidence, then shows how declared PackSpec requirements change the decision.

Try the free RAG Failure Lab