You see it sooner
The first pass is generated, so you have something to react to in days instead of waiting out a design phase you paid for in the dark.
AI
AI that earns
AI that earns
its place in the product
We build AI-native by default. The model takes the first pass, our senior team reviews it, decides and owns the result. Start with an independent strategy read, or go straight to a build.
Trusted by:
Where we start
Eight ways in, depending on whether you have a product to extend, a process to automate, a team to reinforce, or just a question about whether AI is worth it.
AI-native development
How we build by default: AI on the first pass and the repetitive parts, seniors on the calls.
AI product integrations
Assistants, semantic search and agentic workflows inside the product you already have.
Custom LLM apps
Chatbots, copilots and assistants built on your own content and your own rules.
RAG & knowledge systems
Answers from your own documents, databases and tickets, with citations and access control.
AI automation
Triage, data entry, reporting and approvals, with a person kept in the loop where it matters.
Computer vision
Inspection, detection, counting, document capture and video analytics, at the edge or in the cloud.
AI strategy & consulting
An independent read on where AI pays off and where it doesn’t. No obligation to build with us.
Forward deployed engineers
Senior AI engineers embedded in your team, shipping in your codebase from week one.
How an AI engagement runs
Evaluation comes before the demo. If a feature can’t be measured, we’ll say so instead of shipping it on vibes.
1
Find the case worth doing
A week looking at where your time and money actually go. Most of the value is in ruling things out. The shortlist usually gets shorter.
2
Build the evaluation set first
Real inputs with known correct answers, agreed with the people doing the work today. Without it, nobody can tell an improvement from a regression.
3
Prototype against it
A working version, measured on your own data rather than a benchmark. You see the score, the failure cases and the running cost before you commit.
4
Harden it
Guardrails, fallbacks, rate limits, audit logging and a cost ceiling. It’s the part that separates a demo from something you can put in front of customers.
5
Ship with a person in the loop
Start with someone reviewing the output, then relax that as the numbers earn your trust. Nothing goes fully autonomous on day one.
What AI-native actually changes
Not what you get. How fast you get it, and what it costs.
Seniors still decide
Every generated line is reviewed by the engineer whose name is on it. The model drafts. A person is accountable.
Measured, not claimed
Every AI feature ships with an evaluation set and a cost ceiling, so “it got better” is a number instead of an opinion.
Your data stays yours
Gateways, retention controls and access rules, all agreed before the first call to a model provider.
The engineering behind it
A straight answer: our AI projects are recent and mostly under NDA, so the case studies below are engineering work rather than AI work. They’re what the AI practice sits on. The data, integration and platform problems that decide whether an AI feature is even possible.
7Ways into
the AI work
the AI work
31Projects
shipped
shipped
38Client reviews
on Clutch
on Clutch
4ISO
certifications
certifications
The choices that decide the outcome
AI projects fail in fairly predictable ways. These four decisions settle most of it.
Build or buy
An off-the-shelf tool is often the right answer.If a product already does 80% of it for a monthly fee, we’ll tell you to buy it. We’d rather integrate a tool that exists than sell you a build you didn’t need.
Model choice
Frontier, small, open-weight or self-hosted.It comes down to latency, cost per call and where your data is allowed to go, not which model is in the news that week. Most production systems end up mixing two.
Retrieval
Keyword, vector, hybrid or re-ranked.For anything answering from your own content, retrieval quality matters far more than the model does. Hybrid search plus re-ranking is where most of the gain sits.
Autonomy
Suggest, review, or act.How much the system is allowed to do on its own. We start at suggest, and move up only when the numbers justify it.
Got an idea? Let’s make it real.
Tell us the short version
This could be the first step towards a new and successful collaboration. A one-line idea and a finished spec are both fine — tell us the problem, the deadline you’re working to and what’s in your way.
Frequently asked questions
If a product already does 80% of it for a monthly fee, we will tell you to buy it and do the integration work around it instead of selling you a build you did not need.
It depends on latency, cost per call and where your data is allowed to go. Frontier, small, open-weight or self-hosted are all on the table. Most production systems end up combining two.
Every engagement starts by building an evaluation set of real inputs with known correct answers, agreed with the people doing the work today. Improvements are measured against it rather than demonstrated.
Only under rules agreed before the first call. Gateways, retention controls and access rules are set up during the build, and self-hosted options exist where the data cannot leave.
Not on day one. We start with a person reviewing the output and relax that only as the evaluation numbers justify it.