AI Agents
Autonomous workers for support, research and back-office operations. They read your data, take real actions in your tools, and escalate to a human at the boundary you set rather than guessing.
Tool useMemoryHuman handoff
AI systems studioTaipeiEst. 2026
Agents that do the work. Not the demo.
We build AI agents and automations for teams that need them running every day. Scoped in weeks, shipped into your own environment, handed over with the source, the runbook and the numbers.
- First working system
- 14–21 days
- Handover
- Source + runbook + evals
- Commitment
- No retainer lock-in
The method
Four stages, in order. Nothing skipped because it is slow.
Most AI work fails between the prototype and the tenth thousandth run. This sequence exists to close that gap, and each stage has to finish before the next one starts.
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01
Bloom
Map it before we build it
We sit with the people who do the work, trace the workflow end to end, and write down what “working” means as a number. If AI is the wrong tool for it, you hear that here, before you spend on a build.
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02
Pour
Build the smallest system that works
One agent or one pipeline, wired into the tools you already pay for. It runs in your environment, on your accounts, inside three weeks. You get a working system to argue with, not a slide deck.
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03
Extract
Tune against real traffic
We run it on your real inputs, catalogue where it fails, and fix the failures in order of cost. Every change is scored against a fixed eval set, so quality only moves one direction and you can see it move.
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04
Cup
Hand it over properly
Source, prompts, eval suite, infrastructure config and a walkthrough with whoever owns it next. You can run it without us. Clients who stay, stay for the next system, not for access to this one.
Services
Four things, built to keep running.
AI Automation
Pipelines that connect software you already run. Intake, enrichment, routing, reconciliation, reporting — the work nobody should do twice, moved off your team’s week and into a system that logs itself.
WorkflowIntegrationsNative APIs
Voice Agents
Inbound and outbound calls that qualify, book and write back to your CRM. Sub-second response, your script, your escalation rules, and a transcript on every record so nothing depends on memory.
InboundOutboundCRM sync
AI Infrastructure
Retrieval, evaluation, observability and cost control. The unglamorous layer that stops a system drifting three months after launch, and the reason we can tell you what it costs per run.
RetrievalEvalsMonitoring
Engagement
Start small. Leave whenever it stops paying.
Diagnostic
A written map of where AI actually pays inside your operation, with a build plan, a cost estimate per run, and the parts we recommend you do not automate. Credited against a build if you continue.
- Workflow trace and bottleneck costing
- Model and tooling recommendation
- Build plan with fixed scope and price
Build
One system, scoped, built, tuned against your traffic and handed over. Weekly demos on the real thing. If scope changes, we re-quote before we write the code, not after.
- Working system in your environment
- Eval suite and regression checks
- Source, runbook and team walkthrough
Steady state
We keep the systems tuned as your inputs change, watch the evals and the bill, and pick up the next build when you are ready for it. No minimum term and no exit fee.
- Eval monitoring and drift response
- Model and cost review each month
- Priority queue for the next system
Questions
The five we get every time.
Do we own what you build?
Yes. Source code, prompts, eval sets and infrastructure config are yours at handover. There is no black box and no licence that stops working when you stop paying us.
Which models do you use?
Whichever fits the job, your latency budget and your data constraints. We benchmark candidates on your own data before committing, and we build behind an abstraction so a model can be swapped without a rewrite.
Does our data train anything?
No. We use zero-retention API tiers where the provider offers them, keep your data inside your own accounts wherever the architecture allows, and never use client data to train or fine-tune models for anyone else. The specifics are in our privacy policy.
How fast can we start?
Diagnostics usually begin within two weeks of the first call. Builds start once the diagnostic is signed off, and the first working version lands two to three weeks after that.
What if AI is not the answer?
Then the diagnostic says so and tells you what to do instead. A rules-based script and a clean spreadsheet beat an agent more often than this market admits, and telling you that is cheaper for both of us than a build that quietly fails in month four.
Contact
Tell us what’s slow.
Send us the workflow that eats your team’s week. You will get a straight answer on whether it is worth automating, roughly what it takes, and what it would cost to run.
Replies within one business day, Taipei time.