AI stack & practice
What we actually run.
Not a logo wall. The layers we build with, the rules we hold to, and an honest label on what is a default choice versus something we reach for only when the problem calls for it.
How we handle it
The rules come before the tools.
Models change every few months. These do not. They are the reason our work still holds up when the tooling underneath it moves.
Boring infrastructure, sharp models
The database, queue and deployment pipeline should be unremarkable. Model choice is where the interesting decisions live.
Evaluations before scale
If we cannot measure whether it is working, we do not put it in front of your customers.
A human where it counts
Judgment, money and anything customer-facing keep a person in the path until the evidence says otherwise.
Your data stays yours
We do not train on it, we scope exactly what reaches a model, and we can run the whole thing inside your cloud.
Cost and latency are design constraints
A feature that works but costs too much per user is not finished. We budget tokens the way we budget load time.
Reversible by default
Model routing, prompts and retrieval sit behind interfaces, so we can swap any of them without a rewrite.
The stack
Eight layers, one job each.
Every layer exists because something breaks without it. Open a category to see what we use and how firmly we commit to it.
Frontier LLMs
DefaultGeneral reasoning, drafting and extraction across the providers worth using.
Open-weight models
When it fitsSelf-hosted or private-cloud inference when data cannot leave your estate.
Embedding models
DefaultTurning your documents and records into something searchable by meaning.
Rerankers & small classifiers
DefaultCheap precision on top of retrieval, and clean routing before a big model is called.
Fine-tunes & distillation
When it fitsSmall task-specific models when latency or unit cost dominates the design.
Postgres with pgvector
DefaultOur default store. One database to run, back up, secure and reason about.
Managed vector databases
When it fitsWhen volume, isolation or throughput outgrow what Postgres should carry.
Hybrid search & chunking
DefaultPure vector search quietly fails on names, codes and numbers. We combine approaches.
Ingestion pipelines
DefaultDocuments cleaned, deduplicated, permissioned and versioned before they are indexed.
Warehouse connectors
When it fitsRead-only access to your systems of record instead of copying data around.
What this means for you
You are not locked in to our opinions.
Everything above sits behind interfaces we can swap. If a model, a vector store or a provider stops being the right answer, we change it without rebuilding your product.
How we build
Engineers first. AI where it does the most good.
We don't bolt AI onto a traditional process. We use it to deliver faster, and engineering judgement to make sure what ships survives production.
AI-accelerated delivery
Prototyping, coding, testing, documentation and research move dramatically faster. You see working software sooner.
Production judgement
Architecture, security, validation and human oversight. Speed doesn't cost you reliability. That's the whole point.
AI where it pays
Start with an AI Opportunity Audit to find the use cases with real ROI, and skip the ones that just look impressive.
Vibe-code rescue
Already built something fast with AI? We harden it for real users, security, tests, data integrity, without a rewrite.
Start with what pays off
The stack is the easy part once you know where AI is worth applying. That is what the audit answers, in two weeks.