AI integration services that do a job, not a demo
Search that understands what people mean, extraction that replaces manual data entry, assistants that answer from your data. Built on OpenAI and Claude, grounded in your systems, and measured against a baseline.
We built AI-powered search across more than 700,000 HVAC and plumbing SKUs for Blackhawk Supply, where a contractor types a half-remembered part description and needs the right item immediately. That project is a good picture of how we approach this work: the model is one component in a system that also includes clean product data, embeddings, ranking, fallbacks, and caching. The impressive part isn't the model call. It's that the answer is right and arrives fast.
We start by defining what "correct" means for your use case and building an evaluation set before writing feature code. That gives you a number to improve instead of a vibe. We also keep costs visible — prompt sizes, token spend per request, and where a cache or a plain database query does the job for a fraction of the price. Sometimes the honest recommendation is that you don't need a model at all, and we'd rather say that in week one than bill you for six.
- Semantic and hybrid search over your catalog, documents, or knowledge base, with ranking tuned to your data
- Retrieval-augmented assistants that answer from your content and cite the source, with clear behavior when they don't know
- Document and data extraction pipelines that turn PDFs, spreadsheets, and email into structured records
- An evaluation set and accuracy baseline, so every change is measured instead of guessed at
- Cost and latency controls: caching, model routing, prompt budgets, and per-request spend visibility
- Guardrails, logging, and human review paths for anything customer-facing
How we work
Pick one measurable use case
We choose the task with the clearest before-and-after, and define what a good answer looks like in writing.
Build the evaluation before the feature
A representative test set gives us a score to beat on day one and prevents silent regressions later.
Get the data right, then add the model
Cleaning, chunking, and indexing usually drive more accuracy gain than prompt tuning does.
Ship behind a flag and compare
We roll out to a slice of traffic, measure against the old path, and expand once the numbers hold.
Work we have shipped
Blackhawk Supply
A full e-commerce platform with AI search across 700,000+ HVAC and plumbing SKUs. Contractors type what they mean; the right part comes back instantly.
700,000 products indexed Read the case studyDraftEdge
Machine-learning player projections that keep pace with live injury news right up to lineup lock.
ML pipeline in production Read the case studyCommon questions
Not by default. We use enterprise API terms that exclude training on your data, keep your content in your infrastructure, and document exactly what leaves your systems on each request. If you have compliance requirements, bring them to the first call and we'll design around them.
Because you'll have a number. We build a labeled evaluation set from your real queries and score every version against it. You see accuracy, failure cases, and latency before launch, and we set a threshold together for what's good enough to put in front of customers.
It depends on volume and how much of your traffic actually needs a model. We estimate per-request cost during scoping and design for the cheap path first. Cached results and direct queries handle a large share of requests in most systems. You get spend monitoring so there are no surprise invoices.
Yes. We built AI-driven search across more than 700,000 HVAC and plumbing products for Blackhawk Supply. Catalog size mainly affects indexing strategy, embedding cost, and query latency, all of which are architecture decisions made up front.
Bring us the task your team does by hand every day. We'll tell you honestly whether AI should touch it.
One business day. From an engineer. No sales sequence.
or email customers@etlon.net