Connect Claude API to your ERP / Intranet / Helpdesk / Document Management + configure SSO/SAML + design RAG & workflow + establish data governance aligned with PDPA — by a team that has implemented enterprise systems for 25 years
Pick one or combine — we recommend starting with a small pilot scope, then expanding. Below these are 13 advanced capabilities that take knowing the model, your back-office systems, and the rules together.
Connect Claude to ERP (including Saeree ERP), CRM, Accounting, HR via Claude API + custom workflows aligned with your business process
Build an MCP Server so Claude can query and act on your internal systems directly — ERP, procurement, inventory, HR, document management — under each user's existing permissions, with no data copied outside the system
Design RAG architecture so Claude can answer questions about your organization-specific knowledge — SOPs, HR policy, product catalog — without fine-tuning
Configure Claude Enterprise to integrate with your identity provider — Azure AD / Okta / Google Workspace + SCIM auto-provisioning
Establish data governance aligned with Thailand's PDPA Section 40 + Anthropic DPA + SCC + AWS Bedrock APAC per regulator requirements
A monthly or annual care contract after go-live — because an integration on day one is not the same as the same integration six months later: processes change, new models ship, and spend needs controlling
The six services above are the backbone of an integration — connect it, and people can use it. This group is another level. Each item needs three things at once: how the model actually behaves, the back-office systems holding the real data, and the rules a Thai auditor will hold you to. Miss any one of the three and the work stalls halfway.
We turn your working standards into skills Claude applies the same way for everyone — your ministry's official letter format, the structure your executives expect in a proposal, the costing method you actually use, the checklist that runs before a document goes out
Work too long for a single pass gets split into sub-tasks that run at the same time and are merged at the end — reviewing bids from several vendors simultaneously, or checking a contract section by section and reporting the clauses to watch
Setting your in-house developers up to use Claude Code on real code with real boundaries — permission rules for which files it may touch, hooks that stop it before anything prohibited, MCP access to internal systems, plus work on legacy code and migrations
Designing exactly where a person must sign off before the AI writes anything into a live system, with a screen that shows the approver what will change, on what grounds, and citing which source document
We place a checkpoint between your internal systems and Claude that inspects every payload before it leaves. When it finds something your organization has ruled out of bounds — national ID numbers, bank account numbers, individual salaries, classified documents, patient records — it either masks just that portion or refuses the request outright, tells the user why, and logs the event for your administrators.
A tool exposed to Claude does not expose the whole table. Access is narrowed to which rows this specific user may see — their own department, the fiscal years they are responsible for — and which columns stay hidden
We build a set of real questions from your work with known-correct answers, then re-run it every time a prompt changes, a model version changes, or documents are added — so you can compare scores against the previous round instead of guessing
A document, email, or web page the AI reads may contain text written specifically to make it do something it should not. We separate incoming content from instructions, keep tool scope narrow, and test the system the way an attacker would before it goes live
We agree at design time what gets produced the day an auditor asks — who asked what and when, which data the model saw, which document an answer cited, and who approved writing it into the system
The data you need usually sits in a system with no way in — written years ago, its original developers long gone, or offering screens and nothing behind them. We build the intermediate layer that does the job
AI cost tracks the volume of data moving in and out, and that volume comes down substantially through design rather than through using the system less
Most of the work in a document project happens before the model ever sees anything — scans with no text layer, government forms built as tables, seals and signatures mixed into the body text
Executives who approved the budget always ask the same question — who is actually using this, and is it worth it. We build the dashboard that answers it
Our support team uses an MCP Server we wrote ourselves, connected to the databases of the systems we maintain, for three real jobs every day — answering usage questions customers raise, checking how the system's business rules behave, and investigating reported bugs. Our developers use Claude Code against Saeree ERP's own source in the same way.
The reason we did it in that order is that wrong data in an ERP is hard to undo, and customers should not be the ones discovering our problems for us. We are past that stage now and ready to take this on for other organizations. What that period gave us is knowing which parts can be trusted and which parts need a person watching — and that is not something you can read in documentation.
Not every task deserves AI. The ones that pay off are the tasks where someone opens five screens to answer one question, or where the answer already sits in an internal document nobody can find
Search regulations and TOR conditions · check document completeness before submission · compare multiple vendor quotations side by side in one table
Ask about stock balances and issue history in plain language instead of paging through reports · surface mismatched items before a physical count
Summarize remaining budget by project and funding source · flag unusual entries for review before period close, with people still making the call
Answer questions on leave rules, benefits, and internal procedures from your own documents — not from the model's general knowledge
Draft letters in your organization's format · summarize lengthy submissions · find prior cases related to the one on your desk
Answer product usage questions from manuals and past cases · verify system business rules — this is the category our own support team uses daily
The first question from IT and legal in almost every organization is whether data leaves the country. The answer depends on the route you pick, and that choice belongs at design time, not at go-live
| Route | Where inference runs | What gets stored | Fits |
|---|---|---|---|
| Claude API direct from Anthropic |
Routes globally by default · can be pinned to US-only infrastructure via the inference_geo parameter, billed at 1.1x the standard rate |
Conversation content is not retained by default · a Zero Data Retention arrangement can be requested from Anthropic · workspace-level storage is currently US-only | Organizations comfortable with offshore processing that want new models and features the day they ship |
| Amazon Bedrock Thailand region (ap-southeast-7) |
Callable from the Thailand region since February 2026 · but via Global cross-Region inference, so processing may run in other AWS regions worldwide | Data at rest — logs, knowledge bases, stored configuration — stays in the source region · AWS, not Anthropic, is the contractual data processor | Organizations already on AWS, or bound by a requirement that stored data remain in-country |
| MCP Server + existing on-premise systems |
Your ERP and databases stay in-house; Claude never connects to the database directly, only through the tools you expose | The model sees only what a tool returns under that specific user's permissions — never the whole database · every call is logged for audit | Organizations that must keep the data surface as narrow as possible and be able to say who asked what, when |
No route keeps data entirely inside your organization. Claude is a cloud service; whichever path you take, the question and the material used to answer it are sent to the model. What you can genuinely design is what leaves, where and how long it is stored, and how far back you can audit
That is a different story from Saeree ERP, which runs on your own servers with data that never leaves. The two cannot be described in the same sentence — anyone claiming an AI integration keeps your data 100% in-house is leaving something out
Following the ISO/IEC 29110 standard that Grand Linux has been certified for 10 consecutive years — transparent, with documentation delivered to the customer at every phase
Understand use case, existing systems, constraints
Architecture + data flow + security design
Integration code + test + UAT
Train users + admins, deliver manuals
Production cut-over + monitoring + tuning
Because Claude Integration isn't just "calling an API" — you need to understand business processes, compliance, and change management
Eligible for government procurement contracts · renewed continuously 3 times (2018, 2021, 2024)
4 consecutive certificates (2015–2027) · Project Management + Software Implementation
Deep understanding of Thai business workflows — government, state enterprise, private sector
Deep understanding of business systems — not a new AI reseller
The questions that come up in every Discovery — answered from verifiable facts as of September 2026
On commercial plans — Team, Enterprise, and the API — the default is that your data is not used for training, per Anthropic's published terms
One exception worth telling your users about: if someone gives thumbs-up or thumbs-down feedback on an answer, that entire conversation can be retained for up to five years and used to improve the model. Put this in your internal usage policy from day one
On consumer plans (Free / Pro / Max) the setting sits with each individual user and cannot be enforced centrally, which is why those plans do not belong anywhere near real organizational data
No. The Claude model itself is not available to install on your own servers — every route calls a cloud service
What can stay in-house is the MCP Server, the business systems, the databases, and the whole permission layer, which is what lets you control exactly which data leaves — see the route comparison above
Yes — either an API key for integration work, or Team / Enterprise seats for general users across the organization
We can handle procurement: Thai-language quotation, PO, tax invoice, and withholding tax under Thai regulations — see Claude procurement · Or, if you already hold the licenses, we can do the integration work alone
Yes, provided the system offers a way in — a REST API, a readable database, or at minimum an export file that can be produced consistently
What we cannot answer in advance is how deep a given system will let us go; that needs a look at the real thing during Discovery. If it turns out not to be worth doing, we say so then rather than carrying it through to a quotation
There is no off-the-shelf rate — scopes differ too much. But the cost always breaks into three parts, and we itemize all three before anything is signed
(1) License or API usage fees, which belong to Anthropic and vary with actual consumption · (2) the integration work, quoted as a project against the agreed scope · (3) ongoing care after go-live, billed monthly or annually
Better to design for it than to catch it later — answers must cite the source document or record, every data call is logged for audit, and anything hard to reverse is restricted to read-only
Work that writes into the real system always goes through human approval. We do not design AI that writes into an ERP unsupervised
Yes — provided it is enforced at the tool layer the data actually passes through, not at the model. We place a checkpoint ahead of every call to Claude; when it finds something your organization has ruled out of bounds, it masks that portion or refuses the request entirely, and logs the event for administrators to review.
What has to be clear up front is that this checkpoint works from rules written in advance. It catches what you have defined a pattern for — it is not a machine that infers intent. So the first step of this work is sitting down with you to decide what must never leave, then converting that into rules the system can enforce — see the sensitive-data checkpoint.
No, and you should not try — most organizations start with the two or three that match their actual risk and workload.
For organizations holding a lot of sensitive data, we recommend starting with the data checkpoint and row/column permissions. For those already running for a while whose answers have started drifting, start with the answer-quality test set. During Discovery we tell you which ones to do first, and which are not yet warranted at your current scope.
We recommend exactly that — a pilot on one measurable use case in a single department, then expand
You get to see whether people genuinely change how they work before committing a larger budget. Change management usually decides the outcome more than the technology does
It can, and we will tell you upfront what happens next. The integration you ship today is not the same thing six months later — internal processes change, new models ship and behave differently, and growing usage shifts the cost profile
With nobody watching, what we typically see is a system that still runs while answer quality drifts quietly, until people stop using it on their own
Book a Discovery session with the Grand Linux team — free, no obligation · we deliver scope + budget + timeline within a week
