Frontier AI, put to work.
MCP for your APIs
We train your team on frontier AI, redesign how the work happens, then build it and hand it over running. Global projects, headquartered in Bangkok, with a focus on APAC.
Three practices. One firm.
Most organisations now have capable models and no way to put them to work. The gap is people who can use the tools, software that agents can actually operate, and a clear decision about what the work should look like afterwards. We are organised around all three.
The three practices
Enablement.
Your team learns to use frontier AI on the work they already do, and keeps a private portal that shows what actually stuck.
Preview build
Build.
We make your software something an agent can operate, we give your agents governed access to every model at a price you see before the call runs, and we build the systems around both.
{ "model": "the routed model", "estimate_usd": { "floor": 0.0041, "expected": 0.0087, "ceiling": 0.0192 }, "basis": { "input_tokens": 1842, "max_output_tokens": 4096 } }SPECIMEN, NOT LIVE Brand & Intelligence.
We turn what your business knows into something it can act on, and produce the work that carries it to market.
Sources
Your APIs were built for developers. Agents need more.
You already have an API. It documents endpoints, it authenticates a developer, and it assumes a human read the docs first. An agent has done none of those things.
We wrap software you already own in a Model Context Protocol server: authentication that works for a delegated actor rather than a logged in user, tools scoped so an agent cannot do damage, planning structure that tells the model which tool to reach for and when, and error semantics it can recover from rather than fail on.
The result is that your product becomes usable inside Claude and other agent platforms, and your customers get agentic use cases without you rebuilding anything.
And the same problem, pointing the other way.
Glhip Model Factory.
An MCP server lets an agent reach your software. Model Factory lets your software reach every model, through one endpoint, at the best available rate for the task.
One integration instead of one per provider. When a better model ships, it appears behind the same endpoint rather than in your next sprint.
The part nobody else puts in front of you: every request returns an estimated cost in USD before it is processed. Your agent sees the price while it still has the option not to pay it. A classification job routes to something small, an escalation routes upward on purpose, and finance sees the cost of an AI feature per unit of work rather than at the end of the month.
Senior leads. You will meet all of them.
This is the entire delivery team. Every person here runs their own firm, holds one layer of the work, and stays on your engagement from scope to handover.
- Architecture and agent design. Solution architecture, agent design, data models, and enablement.
- Platform and integration. Cloud architecture and migration, ingest connectors and pipelines, security and PDPA aligned data protection, ERP and digital workplace implementation.
- Commercial strategy. Peer and benchmark models, commercial logic, and KPI and ROI design.
- Behavioural design. Why teams do not comply and what makes them act. Nudges, game loops, and a full change playbook.
- Agentic systems engineering. Software that decides and acts rather than waiting to be told, built end to end.
- Governance and risk. AI governance charters, multi jurisdiction privacy including PDPA and GDPR, contracts and IP transfer, and procurement.
Decades of knowledge inside hotel systems.
Hospitality is where our work goes furthest down the stack, and it is the clearest illustration of how we approach any sector: start in the systems of record, not in the strategy deck.
Commercial AI in a hotel does not fail on the model. It fails on the integration layer, and on whether the revenue team changes what they do on a Tuesday. So we work at system level, and we name the systems.
We know which of these write back, which only read, and which integration is called supported and is not. The same discipline applies wherever we work: name the systems, find where the data is actually dirty, and design for the person who has to use it.
Global projects, run from Bangkok.
Glhip Co., Ltd is the operating company in Bangkok. Glhip Inc, in the United States, is the parent. Principals are Bangkok based, which means a client in this region gets people in the room the same afternoon a decision needs making, and a client outside it gets a team that works while their office is closed.
The group works across seven languages: English, Thai, Spanish, French, Bengali, Hindi and Hebrew. Thai, Spanish and French do real delivery work. The rest are breadth rather than market coverage, and we will say so.
Engagements have run across APAC, and the practice is built to travel. Travel and onsite presence outside Thailand are scoped per engagement.
Training your team on frontier AI is no longer optional.
The front door. Hands on up skilling days for non technical leaders, run monthly across APAC, plus a private portal your cohort keeps: role based paths, installable Glhip skills, a Hire Your Agent step, and progress tracking so you see adoption as evidence rather than attendance.
Tell us what you want AI to do. We will plan it, build it, and hand it over running.
info@glhip.com · Glhip Co., Ltd, 548 One City Centre, Ploenchit Road, Lumpini, Pathum Wan, Bangkok 10330