AI infrastructure & transformation
Become an AI-native company, on infrastructure you own
We design, build and run the layer between your business systems and modern AI models: MCP tool servers, agent-to-agent workflows, retrieval over your own knowledge, and the evaluation that keeps it honest. In the cloud, on your own hardware, or fully air-gapped.
- MCP & A2A engineering
- On-premise & air-gapped
- Thai and English delivery teams
What we build
The plumbing that makes AI useful inside a company
The layer between your business systems and modern models. Built as real infrastructure — versioned, tested, documented and handed over.
MCP tool servers for your systems
Your ERP, POS, CRM, booking system and internal APIs, exposed to any model as governed tools over the Model Context Protocol.
- One MCP server per system, with typed tools instead of scraped screens
- Per-tenant scoping, authentication and a full audit trail on every call
- Reusable by Claude, OpenAI, local models, your own agents and ours
Agent-to-agent (A2A) systems
Specialist agents that discover each other and hand work across teams and vendors, instead of one prompt trying to do everything.
- A capability registry so an agent can find the right counterpart
- Delegation contracts with timeouts, retries and idempotent replays
- Human approval gates on the actions that move money or data
Agent runtime and orchestration
The unglamorous layer that makes agents survive production: durable queues, isolated execution and turns that can run for minutes without dropping work.
- Durable job queues with checkpointing and crash recovery
- Sandboxed execution per turn, with an egress policy you approve
- Budget, rate and capability limits enforced before the model is called
Retrieval over your own knowledge
Documents, policies, product data and past conversations turned into retrieval your staff can actually trust — in Thai and English.
- Ingestion pipelines for PDFs, spreadsheets and scanned Thai documents
- Permission-aware retrieval: an answer never crosses a role boundary
- Freshness and ownership rules so stale content is retired, not repeated
Evaluation and observability
The difference between a demo and a system you can bet operations on is measurement, and measurement is part of every build we ship.
- Golden test sets built from your real conversations and documents
- Replay harnesses that score a model or prompt change before it ships
- Per-turn traces with cost, latency and failure dashboards your team owns
Becoming AI-native, not AI-curious
Infrastructure only pays off when the organisation changes around it, so delivery includes the people side of the work.
- A prioritised roadmap tied to measurable operational outcomes
- Enablement for your engineers: architecture, runbooks and handover
- Workshops for the teams whose daily work the system changes
Sovereign AI
Local inference, on-premise and air-gapped
For teams whose data cannot leave the building — regulated industries, government, defence-adjacent work, and anyone who would simply rather own the stack.
Local inference stack
Model selection, GPU sizing and a serving stack tuned to your latency and throughput targets — behind an OpenAI-compatible endpoint, so your applications do not need to know where the model runs.
Air-gapped deployment
For environments with no outbound network at all: an offline supply chain for models and images, signed artefact transfer, internal package mirrors, and a network and hardware posture your auditors can follow.
Full local deployment
The whole stack — models, vector store, queue, orchestrator, observability — on your own hardware or private cloud, deployed as infrastructure-as-code that stays in your repository.
Data residency and governance
PDPA-aligned handling with tenant isolation, retention and redaction policy, key management, and audit trails that answer who asked what, and what the system did about it.
We specify the hardware and you buy it directly. No markup, no proprietary runtime, no lock-in to us.
How we engage
Assess, design, pilot, run
Every engagement starts with an assessment, because a roadmap built on guesses is the most expensive thing in AI.
Four stages, each one useful on its own
- 01
Assess
We map your processes, data and systems, then rank the opportunities by value and by how hard they are to do properly.
- 02
Design
A target architecture with an explicit deployment posture — cloud, on-premise or air-gapped — and the security model that comes with it.
- 03
Build & pilot
One workflow into production with real users, real integrations and the evaluation set that proves it is working.
- 04
Deploy & run
Roll out across teams, hand over to your engineers, and keep the stack current as models and hardware move.
AI-native assessment
A short, evidence-based read on where AI belongs in your operations — and where it does not.
From
฿180,000per engagement
Timeline: 2–3 weeks
What you get
- Process, data and systems audit
- Opportunity map with expected impact per use case
- Target architecture and deployment posture
- Prioritised roadmap with build estimates
Build & pilot
The first production system: MCP servers for your key platforms and one agent workflow your team relies on.
From
฿650,000per engagement
Timeline: 6–10 weeks
What you get
- MCP tool servers for your priority systems
- One agent or A2A workflow live in production
- Evaluation sets, tracing and cost dashboards
- Architecture documentation and engineer handover
Sovereign deployment & run
Your own inference stack, installed on your hardware — including fully air-gapped sites — and kept running.
From
฿15,000per month
Timeline: ongoing
What you get
- Local inference stack on hardware you own
- Air-gapped installation, runbooks and upgrade procedure
- Model upgrades, capacity planning and tuning
- Support in Thai and English with an agreed response time
Indicative figures for planning. Final scope and price are quoted after the assessment, and hardware is quoted separately at cost.
Questions we get
Before you book a call
No. We deploy the same architecture in three postures: managed cloud, on-premise in your own data centre, or fully air-gapped with no outbound network. The posture is a decision we make with you during the assessment, before anything is built.
Start here
Start with an assessment, not a proof of concept
Two to three weeks from now you can have a ranked roadmap, a target architecture and a straight answer on what belongs on your own hardware.