LLM Engineering
Why Your RAG System Is Hallucinating (And How to Fix It)
Most RAG failures aren't model problems -- they're retrieval problems. Here's our production checklist for building reliable retrieval pipelines.
Specializing in Computer Vision and Custom AI Engineering for startups that can't afford to fail.
Our background in complex spatial data and pixels allows us to solve LLM problems that others find impossible. AI that works in the real world, not just in a slide deck.
Production-grade visual quality control systems that catch defects humans miss, running 24/7 without fatigue.
Sub-200ms object detection and classification deployed on edge devices or cloud, tuned for your latency budget.
Transform raw visual and sensor data into actionable business intelligence, with full audit trails.
While we use AI to accelerate development, every line of the pipeline is human-audited for production-grade reliability. No black boxes. No surprises.
A 48-hour deep dive. Is your data ready? Is the tech possible? Stop guessing, start building.
A fixed-timeline sprint. We move from "Concept" to "Functional Prototype" in 10 days. High speed, low drag.
Long-term fractional AI Engineering. We don't just build the model; we integrate it into your production stack.
We don't pick models based on hype. We pick based on your unit economics.
If a $0.01 API call does the job, we use it. If you need data sovereignty and lower latency, we deploy custom-tuned Llama/HuggingFace models on your private cloud.
We guide the decision; you own the intellectual property.
The right choice depends on your data, budget, and scale targets. That's what the Audit determines.
Not just a logo wall. Here's what we actually built and the results it delivered.
Client had 1M+ product images per month requiring manual inspection, with a 12% defect miss rate causing costly recalls.
Custom CNN classifier with edge deployment, processing images in <50ms on factory-floor hardware.
Legal tech startup needed to extract structured data from 50+ document formats, with 99%+ accuracy required for compliance.
Multi-modal pipeline combining OCR, layout analysis, and fine-tuned LLM extraction with human-in-the-loop validation.
Retail chain needed real-time foot traffic analysis across 200+ cameras, with privacy-preserving people counting.
Privacy-first detection system using on-device inference with no PII storage, feeding a real-time analytics dashboard.
If your AI project doesn't pass these five checks, you need an Audit before writing a single line of code.
How does the model perform if the input data changes by 10% next month?
How does the system handle extreme lighting, occlusions, or low-resolution edge cases?
Can this run profitably at 100k users, or will the server costs eat the margin?
What is the maximum "Error Tolerance" for this business case?
Does the user need an answer in 200ms or 2 seconds?
Technical depth without the jargon. Articles designed for decision-makers building AI products.
Most RAG failures aren't model problems -- they're retrieval problems. Here's our production checklist for building reliable retrieval pipelines.
A camera is just a sensor. When does the cost of a CV pipeline outweigh a $5 IoT sensor? A framework for the decision.
Infrastructure, data labeling, and iteration cycles are the hidden 80%. How to budget for AI projects that actually ship.
Whether you need validation, a prototype, or a full production system, we'll find the right path forward.