Your AI Is Failing. I Fix It¶
You're losing customer trust and revenue due to your AI's wrong answers and hallucinations. You’ve tried tweaking prompts, but unreliability remains. The problem isn't your AI model—it's the context you're feeding it.
Your AI Is Only As Good As Its Retrieval System¶
Your AI can only be as good as the information it retrieves. The wrong answers your customers see are a symptom. The disease almost always starts with a failure in your retrieval system. You can't generate a good answer from bad information.
While everyone has access to the same foundation models, the retrieval system and resulting context is where you create a durable competitive advantage.
The good news? It's a solvable engineering problem.
I fix the underlying retrieval systems that are the true cause of poor AI performance.
Better retrieval → Better context → Better AI → Less churn + Better conversions + More revenue
From Failing to Production-Grade¶
Legal Tech: 20% → 70% Accuracy in 30 Days¶
The Problem: A regulatory compliance AI couldn't retrieve from EU legal databases—20% accuracy made it unusable.
What I Did: Built a custom document processor and a specialized retrieval system for regulatory queries.
The Result: 70% accuracy in the first month (a 3.5x improvement), helping them secure their first enterprise contract.
carVertical: 128x Faster, 86% Cheaper¶
The Problem: Processing vehicle records took 10 seconds per match—impossible at their scale.
What I Did: Built a hybrid system that uses fast text matching for obvious cases and reserves AI for only the ambiguous ones.
The Result: 10s → 78ms response time, an 86% cost reduction, and 4x faster retraining.
HomeToGo: 10% More High-Value Bookings¶
The Problem: Their ranking system treated all bookings equally, missing that direct bookings had the highest lifetime value.
What I Did: Built a lightweight reranking layer that optimized for direct platform bookings without disrupting the core system.
The Result: A 10% increase in direct bookings with no negative impact on overall conversions.
My Process: Diagnose Before You Prescribe¶
I follow a three-step process designed to deliver measurable results and give you maximum ROI at every stage. We start with a low-risk engagement to prove value before you commit to a larger project.
Step 1: The Symptom-Level Diagnosis¶
A free, no-obligation 15-minute call to identify the primary symptoms of how your AI system fails. We'll get clarity on the business impact of your AI's errors and determine if I can genuinely help you solve it.
Step 2: The System-Level Diagnosis¶
A fixed-price, 1-2 week deep-dive engagement to find the root cause. I conduct a systematic error analysis on your production data and failure logs. The deliverable is not a report. It's a concrete, actionable roadmap to fix your top 3-5 failure categories.
Step 3: Full Implementation & Reliability Hardening¶
With a data-backed roadmap, we execute. This is a full engagement focused on addressing the highest-impact failure categories identified in step 2. This isn't just about fixing the current problem—it's about building for the future. I deliver a hardened system complete with custom evaluation frameworks and guardrails. Your team will own the system long after I'm gone, creating a flywheel of continuous improvement.
Who This Is For (And Not For)¶
This is a perfect fit if:
- Your AI's performance is directly impacting customer experience and revenue.
- You have a technical team ready to execute on a clear plan.
- You suspect your issues are deeper than just 'prompt engineering'.
- You believe in processes over tools and want a sustainable, in-house capability.
This is NOT a fit if:
- You are looking for a large, traditional consulting team.
- You need hourly work or staff augmentation.
- The problem is not an urgent, high-priority business issue.
Ready to Build an AI You Can Trust?¶
Book a 15-minute diagnostic call to get a clear path forward.
About Me¶
I'm Erikas Valinskas. With 7 years of experience building production retrieval and machine learning systems for companies like HomeToGo and carVertical, my focus is on making AI not just accurate, but fundamentally reliable. I now use that expertise to help companies like yours fix their failing AI systems in months, not years.