Proof of impact.
We don't trade in opinions. We trade in cost/conv, grounded quality, and time-to-answer. Browse our archive of AI recovery and system optimization results.
Offer ladder
Audit → Sprint → Retainer (baseline, ship, then prevent regressions).
Buyers do not need more slogans. They need concrete artifacts, clear scope, and an honest view of how delivery works.
Sample deliverable
Review the audit output format before you buy.
OpenAnonymized case studies
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NDA-friendly, redaction-ready, least-privilege workflows.
OpenArchive
Browse proof by problem type
Use the filters to inspect outcomes by retrieval, latency, cost, security, or reliability. Then move into the offer that matches your current constraint.
AI Production Audit: Why a Support Copilot Was Wrong, Slow, and Expensive
A support copilot was drawing complaints from every direction: wrong answers, slow responses, and rising spend. In five working days, we turned anecdotal pain into a measured baseline, isolated the dominant failure modes, and delivered a fix order the team could finally trust.
Hardening a Production RAG System Against Prompt Injection (Without Breaking UX)
A production RAG assistant blended untrusted user text, retrieved content, and tool capabilities inside one decision path. We rebuilt trust boundaries across prompt, retrieval, tool, and output layers with immutable policy separation, capability-scoped tools, citation-gated answers, isolated execution, and attack-suite validation. Representative injection and exfiltration paths were blocked without forcing normal users into brittle refusals.
Privacy-Safe LLM Observability: Debuggable Logs Without Storing PII
Logging for eval and debug meant storing PII—compliance blocked it. We built a redaction pipeline, hashing, sampling, access controls, retention policy, and synthetic replay sets for eval. Result: debuggable logs, no PII in storage, compliance review passed.
From 'Shut It Down' to Positive ROI: Unit Economics for an LLM Feature
Costs rose until leadership threatened to kill the feature. We rebuilt unit economics: cost per successful outcome, adoption, deflection rate. A clear payback narrative and evidence turned 'shut it down' into continued investment.
Reducing Inference Cost by 25–60% with Model Routing + Token Budgets (Quality Held Steady)
Context bloat, always-LLM-large, retries, no caching—costs spiraled. We implemented routing (small/large), context compression, tool-calling guardrails, and caching. Cost per task dropped; quality and p95 stayed stable.
Tracing an LLM Request End-to-End: How Observability Found the Real Bottleneck
P95 was bad—but where? We added distributed tracing across retrieval, embedding, rerank, and LLM. The trace waterfall revealed the real culprit. Fixing that span moved the needle. This case study shows before/after traces and how observability drives targeted fixes.
Cutting P95 Latency by 40–70% in a RAG Pipeline (No Quality Drop)
RAG pipeline P95 and timeouts were killing UX. We found rerank/embedding bottlenecks, cold starts, and queueing. Fixes: retrieval + rerank caching, streaming, batching, connection pooling, timeout budgets, fallback paths. P50/P95/P99 and throughput improved without quality loss.
Rebuilding Stakeholder Trust with Before/After Benchmarks: A Practical Exec Scorecard
Leadership had 'feelings' about quality—no evidence. We built a dashboard and weekly scorecard with quality composite, cost per successful task, p95 latency, and incident count. Trust returned; decisions became data-driven.
Shipping LLM Updates Without Regressions: Eval Suite + CI Gates in 3 Weeks
Every prompt or model change was a gamble—bugs slipped to prod. We built a golden set from logs, defined rubrics, calibrated judges, and added deterministic sampling with CI gating thresholds. Regression escape rate dropped; deployment frequency and quality stability improved.
From Hallucinations to Grounded Answers: Context Construction Fixes That Moved the Needle
RAG hallucinations weren't a model problem—they came from context that was too long, too noisy, and poorly filtered. We implemented context compaction, source filtering, citation gating, and refusal policies. Groundedness rate and citation coverage improved significantly.
Fixing Low Recall in Production RAG: +18–30pt Answer Accuracy Without Model Fine-tuning
A production RAG system suffered low answer accuracy, wrong citations, and user feedback 'not helpful.' We diagnosed chunking, embedding mismatch, and missing hybrid retrieval—then fixed it with BM25+vector, reranking, and query rewriting. Retrieval recall@k and grounded answer rate improved dramatically.
Case Study: Stopping Performance Regressions in a Fast-Moving Product Team
A fast-shipping B2B SaaS team was stable but slowly getting slower. We introduced flow-level performance budgets and regression guardrails—stabilizing tail latency without slowing delivery.
Case Study: Re-Organizing Observability for a Rapidly Scaling Multi-Service Platform
A mature product team had a full dev org and a stable system — but growing service sprawl made it harder to reason about incidents, performance, and scaling. We introduced flow-based observability, SLOs, and performance governance to restore control before the next growth wave.
Case Study: Stabilizing Flash-Sale Checkout for an Online Fashion Platform
A fashion commerce platform kept breaking during flash sales: unpredictable checkout latency, payment timeouts, and escalating cloud costs. We isolated the real constraints and delivered structural fixes—reducing checkout P99 by ~4.2×, cutting errors ~78%, and increasing sustainable throughput ~2.1×.
Case Study: Rescuing a Legacy E-Commerce Platform Before Peak Season
A long-running online store suffered severe lag and outages during sales. We rebuilt the stack, migrated the database with 100% data retention, and proved improvement with before/after distributions — turning peak traffic from chaos into confidence.
Baseline → Fix → Verify.
Every case study starts with an audit. We baseline cost/conv, quality, and TTFT—identify root causes—ship PRs—and prove impact with before/after benchmarks.
Next step
Want a case study with your own system in it?
The archive shows what good proof looks like. The cleanest way to create your own is still the audit: baseline the system, isolate the dominant failure, and define the proof method before shipping changes.