AI Development Partner
We build AI agents, company-knowledge chatbots, custom AI models and the web services around them. One team takes it from planning to operations, and every two weeks you get something you can actually use.
You hear a lot about what AI can do, but nobody tells you concretely how it fits your own work.
The chatbot that looked great in the demo gives odd answers to real customer questions and real data.
When the contract ends, the code, the documents and the know-how stay with the agency.
You get a single number, with no way to see why it costs that or what dropping a feature would save.
We build a small version on your real documents, calls and data, and only the features that prove useful go into full development.
You see progress as something you can click through, not as a status report.
You see what each feature costs, so you can drop what you do not need and pay only for what you do.
We build chatbots that answer from your documents, manuals and experts. RAG means the AI looks up the relevant documents before it answers; add a knowledge graph (GraphRAG) and it can follow the connections between scattered pieces of knowledge.
Related workFarm AI consulting
We build systems where several AIs split up the roles of researching, analysing, writing and reviewing, and take over work that used to cost a person days. Important decisions stay with a human.
Related workExport-buyer reports · marketing automation
When general-purpose AI is not accurate enough, we train models on your own data: speech recognition, automated scoring, document understanding, small language models.
Related workFarm speech recognition · English-test AI scoring
We measure how accurate the AI you already use is on real data, and build guards that catch wrong answers before users see them. We also deliver reports comparing a new model with your current service on the same standard.
Related workAnswer-verification SDK · speech-recognition benchmark
We build the whole service that puts the AI to use: user app, admin console, backend, data pipelines, cloud deployment and operations.
Related workLesson-material AI · automated stock trading
We find which of your processes gain the most from AI, test it with a small pilot, then scale up.
Related workFarm consulting · export-buyer research automation
Together we look at the problem and the data you already have. Call recordings, documents, databases: anything works.
Hours and cost per feature, plus milestones, in one document a CEO can read and decide on.
Every two weeks you get something to use, and your feedback goes into the next two weeks.
We verify performance with measured numbers and hand over all code, documents and runbooks. We stay on after launch.
Every feature is broken into the hours it takes and shown in a table, so you can see what each line costs.
Core, then extend, then refine. You choose how far to go.
A smaller budget does not mean lower quality. We cut scope instead, and design it so the rest can be added later.
| Package | Feature | Hours |
|---|---|---|
| 1 · Core | Document collection & cleaning pipeline | 24h |
| 1 · Core | Chatbot that cites its sources | 40h |
| 2 · Extend | Admin console & answer-quality dashboard | 32h |
| 3 · Refine | KakaoTalk & app integration | 24h |
Find out in two weeks, on your own data, whether the idea actually works, before you commit to a big budget.
Agree on the goal and success criteria, and gather the data.
Get one core feature actually working.
Run it on real data, measure, and fix.
Review the results together and settle the scope and quote for full development.
What changed for each client, in plain words rather than jargon.
That is the user count of an ed-tech service that scores English essays and speaking with AI. We built its scoring and correction engine.
Our AI crop consulting runs inside the app of a farming company with over $500M in yearly revenue, and has been rolling out to all its farms since May 2026.
For example, on farm phone calls a commercial speech API got 16 of every 100 characters wrong. Ours gets 10.
A first version running on your own data, ready for you to try within two weeks.
An AI that builds lesson slides for elementary teachers in their school’s own template.
An ed-tech client raised this with our scoring model as its core technology. The model was shown to be state of the art in a published paper.
There are more projects than the ones shown here.
Client details and fuller case studies are shared in a consultation. If you have a similar problem, just ask.
No split between sales and engineering. The CEO, an AI engineer with eight years of experience, is responsible from the first call through design, build and operation.
Code, trained models, design documents and runbooks all become your assets.
Instead of "it works well", we report performance as numbers measured on the same data, by the same standard.
Monitoring, incident response, retraining and new features: we stay on after launch.
With AI projects, a lot is unknown until you actually use the thing. Seeing something that works every two weeks lets us correct course early, which means less wasted money.
Yes. Source code, trained models, data-processing scripts and design and operations documents are all handed over, and what is handed over is written into the contract.
Monitoring, incident response, retraining and new features can continue under a follow-on contract, as much as you need.
We check it with numbers measured on real data. For speech recognition, for example, we ran the same 2,354 call sentences through our model and a commercial service and compared the errors side by side.
That’s fine. We start by turning scattered documents, recordings and spreadsheets into something usable.
Book a free consultation to talk through direction and cost.
twoweeks.binbin@gmail.com