>40%
of agentic AI projects predicted to be cancelled by 2027
Source: Gartner
AI Product Studio · Global
Custom AI products,designed and built end‑to‑end.
Web applications, AI agents, copilots, internal tools — we design, engineer, and operate them with the rigor of real software. The studio for teams who need AI to actually work.
Custom-engineered
Built for your stack and your users — never from a template.
Production-grade
Evals, telemetry, and guardrails wired up from day one.
Async + global
Working with teams across continents and timezones.
The standard
Every product we build ships to real users.
No pilots, no proofs-of-concept, no slide decks gathering dust. That's the bar.
- Production · not pilot
- Shipped · not staged
- Real users · not demos
How we engage · 04 waysSee packages
01
1 wk · Strategy
Discovery
02
2–3 wks · Prototype
Spark
03
8–12 wks · Production
Build
04
Monthly · Retainer
Embed
End to end
Strategy → ship.
Working globally
Web · Mobile · Agents
Copilots · Internal tools
Copilots · Internal tools
LangGraph
Claude Sonnet 4.6
OpenAI o-series
Supabase
pgvector
Langfuse
Braintrust
Arize Phoenix
Next.js 16
Vercel AI SDK
MCP
Gemini 2.5
Cohere Rerank
Pinecone
Postgres
LangGraph
Claude Sonnet 4.6
OpenAI o-series
Supabase
pgvector
Langfuse
Braintrust
Arize Phoenix
Next.js 16
Vercel AI SDK
MCP
Gemini 2.5
Cohere Rerank
Pinecone
Postgres
Selected work · 04 of 05
AI products
shipped to real users.
GenRasi
2026GenRasi — a brand-native generative AI product, end to end.
Brand identity, product UX, and a custom generative AI experience launched on a single Next.js + Supabase stack.
Generative AIBrand + ProductEnd-to-end
ExpatLife.Ai
2025Your guide to moving abroad — for real this time.
An AI relocation assistant covering 122+ countries, with country-specific guardrails and audit trails.
RAGRegulated
Spotfic
2025Fortune-500 location intelligence — for the corner store.
AI + Google Maps location analytics that delivers competitor analysis, demographics, and SWOT in seconds.
Multi-agentGeo-AI
AEO Agent
2025Optimize your site for the engines that answer, not just the ones that rank.
A free AI tool that helps brands win citations in ChatGPT, Perplexity, and Google AI Overviews.
Multi-agentGEO/AEO
The state of AI · 2026
Big market, real risk.
Engineering decides who ships.
Enterprise demand for AI is at an all-time high. So is the cancellation risk for projects that don't get production fundamentals right. We focus on the second half.
$1.4T
enterprise AI spend forecast by 2027
Source: IDC + McKinsey
31%
of enterprises run AI in production today
Production rate, Q1 2026
Principles · 04
How we build,
ship, and operate.
Four engineering disciplines we apply to every product we ship — independent of model, framework, or industry.
01 /04
Evals before launch.
Every AI product ships with a graded eval suite — unit tests on key behaviors, LLM-as-judge regression scoring, and human review samples. If it can't pass eval, it doesn't ship.
02 /04
Telemetry from day one.
Traces, latency budgets, token costs, error rates — wired up before the first user touches the product. You can't fix what you can't see.
03 /04
Guardrails as architecture.
Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after the first incident.
04 /04
Boring stack on the edges.
Cutting-edge model in the middle. Reliable, well-understood infrastructure around it. Novelty where it earns its place; stability everywhere else.
How we engage · 04 ways
Four ways to ship
an AI product.
Productized engagements with explicit scope and timelines. Pricing on request — every quote returns within 24 hours.
1 week
Outcome → Go / no-go decision
Discovery
Use-case validation, system architecture, eval strategy, and a clear build estimate. The cheapest way to find out if your idea ships.
Get a quote2–3 weeks
Outcome → Working prototype
Spark
A working AI prototype delivered to your stakeholders — with eval harness, architecture diagram, and a go-to-production roadmap.
Get a quote8–12 weeks
Outcome → Production product shipped
Build
Custom AI product — application, agent, copilot, or internal tool — fully integrated, with eval suite, observability, and guardrails. Launched to real users.
Get a quoteMonthly retainer
Outcome → AI engineering on tap
Embed
We become your AI team. Eval & incident monitoring, continuous capability shipping, quarterly architecture reviews.
Get a quoteFrom the lab · Field notes
Long-form
shipping notes.
⊕FeaturedPatterns·11 min·May 30, 2026
RAG vs agents vs fine-tuning: when each one wins.
Three techniques. Three different problems. Most teams reach for the wrong one because they're picking based on hype, not problem shape. Here's the honest decision framework.
Read essay
Opinion·8 min
How much does it cost to build an AI agent in 2026?
Pricing for AI work is opaque. Here's the honest breakdown — what a prototype costs, what production costs, what operations costs, and what makes the numbers move.
Field notes·13 min
LangSmith vs Langfuse vs Arize vs Braintrust: comparing AI observability platforms.
Four platforms, four philosophies. We've shipped on all of them. Here's the honest comparison — what each does well, what each doesn't, and how to pick.
Engineering·10 min
How to write your first AI eval suite without a framework.
You don't need LangSmith, Braintrust, or any platform to ship your first eval suite. Most production-grade evals start as 100 prompts in a JSON file and a script. Here's the playbook.
Start a project
Have an AI product
that needs to ship?
Tell us where you are — early concept, broken prototype, or scaling something that already works. We'll come back within 24 hours with a take and a quote.
FAQ · Common questions
Questions teams ask
before they email.
Don't see yours? Email us — we read every inquiry and reply within 24 hours.
01What kinds of AI products do you build?
What kinds of AI products do you build?
Web and mobile applications, AI agents, copilots, internal tools, RAG systems, and custom AI features inside existing products. Some are consumer-facing, some are internal — the common thread is they all run in production with real users.
02How long does a typical project take?
How long does a typical project take?
A Discovery sprint is one week. A Spark prototype runs 2–3 weeks. A full Build engagement is typically 8–12 weeks from kickoff to a launched product. Embed retainers are open-ended. We give a precise timeline and milestones at quote stage.
03Do you work with startups or only enterprises?
Do you work with startups or only enterprises?
Both. We work with funded founders shipping their first AI product, with product teams inside scaling SaaS companies, and with established organizations adding AI to existing software. The bar isn't size — it's whether the work needs to actually ship.
04Can you take over an existing AI project?
Can you take over an existing AI project?
Yes. A common engagement is taking on a stalled prototype that needs to reach production — wiring up evals, observability, and guardrails, fixing the reliability problems, and shipping it to real users. We'll review what's there and tell you honestly what stays and what we'd rebuild.
05What does pricing look like?
What does pricing look like?
We don't publish standard rates because every engagement is scoped to outcomes, not hours. Discovery starts at a fixed fee in the low four figures. Spark and Build engagements are scoped per project. Embed retainers are monthly. Every quote returns within 24 hours of an inquiry.
06Where are you based and where do you work?
Where are you based and where do you work?
We work globally and async-first. Calls are scheduled to your timezone. We've shipped products for clients across North America, Europe, the Middle East, and Asia.
07What happens after launch?
What happens after launch?
Every Build engagement ends with a clean handoff: docs, eval suites, observability dashboards, and a runbook your team can operate. Many clients then move to an Embed retainer so we keep iterating, monitoring, and shipping new capabilities. Some don't — both paths are fine.