A
Founders scaling a startup
You have traction and a growing engineering team, and nobody senior enough to own the AI roadmap and the technical calls.
AI engineering studio · Singapore · Bali · Istanbul
We ship AI systems into production.
We design AI products and the architecture under them, and build them from first roadmap to release, inside your account. We also lead engineering as a fractional CTO.
Before coderoadmap, architecture, evals
First releaseevals, traces, rollback
Runs inyour own account
Answer · with its sourcesevidence attached
You
What did we decide about the vendor for the new cluster, and who approved it?
nokta
The team chose Vendor B for the new cluster on 4 Nov 2025 . The decision is recorded in Cluster vendor decision on Confluence. The cost comparison ran in #eng-platform on Slack, and the platform lead proposed it. The CTO approved it in the Q4 plan on Notion.
Sources
Cluster vendor decision2025-11-04
#eng-platform thread2025-11-03
Q4 plan2025-11-06
No evidence found for: vendor benchmark, option C
Every answer carries its sources. When the evidence is not there, it says so.
Worked with
Connects to what your team already uses
- Slack
- Notion
- Confluence
Jira
SharePoint
Google Drive
Gmail
Outlook
Zoom
GitHub
Linear
Teams
Best fit
Start where a wrong answer is expensive.
B
Engineering and R&D leaders
You need an agent or a knowledge graph in production, with the evals and traces to prove it behaves.
C
Security and compliance owners
You need proof trails, refusal on weak evidence, and the whole system inside your own environment.
Pick the review, launch or audit you cannot afford to get wrong. That is the first project.
What we do
Four engagements. Architecture, agents, graphs, and the leadership to ship them.
01
AI product and solution architecture
Decide what to build, what to buy and how to test it, before anyone writes code you will have to throw away.
A roadmap, the build-vs-buy calls, the system design, and an eval strategy your team can run without us.
- roadmap
- build vs buy
- system design
- eval strategy
02
Agents you can leave running
Automate a workflow your team owns, with the evidence to show why it can run unattended.
The agent, an eval suite it must pass before each release, traces for every run, and a rollback path.
- evals
- traces
- rollback
- first release
03
Knowledge graphs that cite
Ask what the company already established and get the paragraph it came from, or a statement that no source says so.
A graph over your wiki, documents, chat and meetings, every relationship linked to the line that supports it.
- citations
- refusal gate
- bi-temporal
- your sources
04
Fractional CTO
AI strategy for an established company, or technical leadership for a startup scaling faster than its team. We did the second at CopyCopter.
A few days a month, or embedded for a quarter. Hiring, architecture calls, vendor decisions and the roadmap, with one name on each.
- AI strategy
- days per month
- embedded quarter
- CopyCopter
Same work for teams with an air gap or an ITAR clause. Not a fit if you want a strategy deck and no system behind it.
Process
Discover, build, deliver. Every engagement ends with something your team can run.
01 · DiscoverWeeks 1 to 2
Read the sources, write the spec
We sit with the team that owns the problem, read their code and sources, and write the spec or the roadmap with them.
02 · BuildSix weeks
One system, in your repo
Built in your repo and your account, and released only once it passes its evals. As fractional CTO, your team builds and we lead.
03 · DeliverHandover
Running, documented, still staffed
The system or the roadmap handed over, a decision log with every architecture call and why, and us in your standup for the first weeks.
Security
Nothing leaves your environment.
Unpublished results, partnerships under NDA and internal assessments are what a company most needs to search and can least afford to send to somebody else's endpoint. So we build inside your cloud or on-premise, and the models, index and evidence run in the same account as the data.
Your cloud or on-premise
No third-party inference in the answer path
No shared tenancy
Every answer traceable to its source
Work
All workWhat we have shipped for clients.
Quantum computing · QuEra Computing
A knowledge graph that cites its sources, running inside QuEra's own cloud
Eighteen months building and running a private knowledge graph for a quantum computing company. Every answer cites the line it came from, the system declines when the evidence is missing, and nothing leaves the client's AWS account.
"He is very professional, knowledgeable and great to work with."Hugh Kelly, Fractional CMO at QuEra ComputingRead the case study
Consumer iOS · Ailifestory - our own product
Ailifestory: from an AI that writes a book to an app a family opens on Sunday
Not client work: our own product, built native and shipping. A voice-interview app that turns an elder's stories into a private family feed and a weekly podcast-style episode - rebuilt from a web tool into native SwiftUI.
Read the case studyConsumer AI · CopyCopter
Scaling CopyCopter from launch to paid plans
Technical leadership for a consumer AI video product, from its Product Hunt launch to paid plans.
Story comingWriting
All articlesNotes on agents and knowledge graphs after the demo.
Products
Most of our work is behind a firewall. These are not.
Built, funded and run by us.
The same people who build the client systems. We pay the bills and answer the support mail. No download counts, no ratings: only what can be checked.