AI Development Services
Ship faster without sacrificing engineering discipline.
AI-native engineering
We put AI to work in two places: inside your business — across your data, workflows, and decisions — and inside your product, where your customers feel it. We design it, build it, ship it fast, and track what it moves.
12+
years of expertise
120+
projects delivered
25+
vetted senior engineers
98%
customer satisfaction
Teams already shipping with us
The divide
AI is splitting the winners from everyone else.
Almost every company now uses AI. Very few have made it count. A small group has wired it into how they actually operate, and they pull further ahead every quarter. The dividing line isn’t using AI — it’s integrating it.
74%
of AI’s economic value is captured by just 20% of companies
PwC, 2026
7.2×
how far AI leaders are pulling ahead of their peers
PwC, 2026
7%
of companies have actually scaled AI beyond pilots
McKinsey
We close that gap — and prove it in your numbers.
Recent work
Agentic AI · Clone platformUgenticAi
$1M in sales at launch
A first-of-its-kind agentic AI clone platform, in 5 weeks
UgenticAi, slide 1 of 5
What we do
How we close the gap
We handle the whole build — the AI products you ship to customers, the AI agents that run your operations, and the data work behind better decisions. One team, from first idea to live system.
Tuned Pods
A Tuned Pod is a small team of senior engineers steering a proprietary in-house harness of AI agents — built on Claude, Codex, and Gemini. The agents do the building; our engineers do the steering — the architecture, the orchestration, and the judgment calls that decide what ships. You see it land as working software, in front of you every week.
Outcomes
- 6 weeks
- to your first release
- 70%
- of a complex workflow, automated end to end
- 10×
- faster decisions from your data
How we work
From first call to first release in 6 weeks
A Pod embeds in your stack, narrows the work to the metric that matters, and ships the smallest system that moves it.
01
Scope the outcome
We pin down the metric that matters and what “done” means — before any code.
Metric locked
Before any code
manual review
handoff lag
error rate
Locked
The one metric
+ a definition of done
02
Stand up a Pod
A small team and its agents, embedded in your stack within days.
Pod online
Live in days
Tuned Pod
Embedded team
Engineer
Build
QA
Verify
Agent harness
Automate
Your stack
Integrate
03
Build & ship
A working system in front of real users — fast.
Release path
In front of real users
API
UI
Tests
Deploy
04
Measure & improve
We track the numbers it moves and sharpen it as your data and the models change.
Live signal
Metric moving in production
Signal
Live
Tracked & improving
as your data and models change
Bring us the outcome. We’ll map the route.
What can ship now, what needs proof first, and what it takes to keep it reliable.
Book a callHow we build
AI-native engineers and agents on a modern, battle-tested stack.
The stack is how we build. The outcome is the point.
TypeScriptPythonNext.jsReactFastAPIPostgresRedispgvectorGCPAWSAzureDockerOpenAIAnthropicLangGraphLlamaIndexHugging FacePyTorch
AWSAzureDockerOpenAIAnthropicLangGraphLlamaIndexHugging FacePyTorchTypeScriptPythonNext.jsReactFastAPIPostgresRedispgvectorGCP
“Outstanding, wide-ranging expertise. A true full-stack development team with capabilities beyond what we were initially looking for.”
Alex Jeffers · CFO, Motus InsuranceRecognition
Awards & certifications
- Clutch5.0 · Top rated
- Upwork5.0 · Top Rated Plus
- Google5.0 · Verified reviews
- ISO 27001Information Security
- ISO 9001:2015Quality Management
- LaravelCertified Company
Insights
Notes from the build
What we've learned shipping AI that holds up — what works, what breaks, and what we'd do differently.
AI agents vs chatbots: what’s actually different
A chatbot responds to a prompt and stops. An AI agent plans a goal, uses tools to carry it out, checks the result, and keeps going until the task is done. The dividing line is autonomy.
Read the article
What is RAG (retrieval-augmented generation), and how does it work?
RAG makes an LLM answer from your data instead of only its training. Before the model writes, a retrieval step finds the most relevant passages and adds them to the prompt — so the answer is grounded in real, current facts.
Read the article
RAG vs fine-tuning: which one does your problem need?
Use RAG when the problem is missing knowledge — facts that change or that the model never saw. Use fine-tuning when the problem is behavior — a tone, format, or decision pattern. They solve different problems, and the best systems use both.
Read the article
Why most AI pilots never reach production
Almost every company is running AI pilots. Very few have put one into production. The gap is not the model — it is everything around it.
Read the article
How to evaluate a RAG system: the metrics that matter
Evaluate a RAG system in two halves: did retrieval fetch the right context, and did the model answer faithfully from it? Measure retrieval with context precision and recall, generation with faithfulness and answer relevancy — against a fixed set of test cases.
Read the article
How to reduce LLM hallucinations in production
You cannot fully eliminate hallucinations, but you can drive them down with layers: ground the model in retrieved facts, constrain it with low temperature and structured output, validate with guardrails and an LLM judge, and measure the rate with evals.
Read the article
Shipping a grounded RAG assistant in two weeks
A field report on scoping, retrieval quality, and the evals that let us put a retrieval-grounded assistant in front of real users — fast.
Read the article
The Tuned Pod: a senior team amplified by agents
How a small, senior team using AI agents ships what used to take a team three to four times its size — and keeps it running.
Read the article
Let’s talk
Tell us the metric you need to move.
Book a 30-minute call. We’ll map the highest-impact system to build first — and what moving that number is worth.