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Nadeem Shaikh

Off-the-Shelf AI vs Custom AI: How to Decide What to Buy and What to Build
 Off-the-Shelf AI vs Custom AI: How to Decide What to Buy and What to Build 150 150 Nadeem Shaikh
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Off-the-shelf AI vs custom AI comes down to one question: is the work you want AI to do the same at your company as everywhere else, or does it depend on your own data, rules and systems? If it’s the same everywhere, buy it. If it depends on what makes your business different, that’s the part worth building, usually on top of the AI tools you already pay for.

This week made the choice more pressing. On October 6, SAP began rolling out Joule Work, an AI interface that works across SAP and third-party data, and on October 8 Google Cloud announced the Gemini agent, a universal agent for work. In the same week, Infor published a survey of 2,111 business decision-makers in seven markets in which 68% said off-the-shelf AI doesn’t adequately address their industry’s needs. Infor sells industry-specific AI, so read that number with its source in mind. Still, the gap it describes is one most operations teams will recognize.

What the big platforms now give you out of the box

Built-in AI is getting genuinely useful. SAP says its Knowledge Graph maps more than 7 million data fields to ground Joule in business context, and that Joule Studio lets customers build their own agents while choosing from more than 70 AI models. Google says its Gemini agent plans the work, connects to customers’ business systems, chooses the best model for the job and has built-in cost controls. If you run those platforms, you get a lot without writing code.

The catch is in the word “platform”. Built-in AI works best on the data inside that vendor’s product, and most businesses don’t run on one vendor. Orders sit in one system, contracts in a shared drive, pricing rules in a sales spreadsheet, and the know-how for exceptions with a few experienced people.

When off-the-shelf AI is the right call

  • The task is generic: drafting emails, summarizing meetings, searching files, writing first drafts.
  • The data already lives in the vendor’s product, and the AI feature comes from the same vendor that holds it.
  • Being a little wrong is cheap, because a person reviews the output before it matters.
  • You need value this quarter and nobody on your team could own a custom system yet.

Here, building your own is usually wasted effort. Turn on what you have, train people to use it, and measure whether it saves time.

When custom AI pays off

  • The work depends on your rules: how you price, what you accept, which exceptions need a manager’s sign-off.
  • It crosses systems: a quote that needs your ERP, your shipping carrier and your customer history at the same time.
  • The documents are yours: supplier invoices, purchase orders, inspection reports or contracts in formats no generic tool has seen.
  • Mistakes are expensive, so you need to test against your own past cases and log every answer.
  • Data has to stay in your own cloud account for security or compliance reasons. In Infor’s survey, 33% of leaders named data security, sovereignty or compliance as their single greatest barrier to advancing their AI strategy.

The same survey suggests the fit problem grows with operational complexity: 73% of manufacturing respondents and 76% of distribution respondents said off-the-shelf AI doesn’t fit their needs. If you run a plant or a warehouse, expect generic tools to handle the easy part of the job and miss the part that matters most.

The middle path: buy the platform, build what makes you different

The choice is rarely all or nothing. A practical setup looks like this:

1. Buy the general-purpose layer

Use the assistant that comes with the suite your team already works in for everyday writing, search and summaries.

2. Build the business-specific layer

Build the small pieces that teach AI about your business: a private knowledge base over your own documents, document extraction tuned to your forms, and connections to your systems that respect your existing permissions.

3. Keep the custom pieces portable

AI products change fast, so build custom parts that more than one AI tool can use. SAP, for example, says Joule can connect with third-party AI and agents through the Agent2Agent protocol. Open connections like this, or a custom MCP server in front of your own systems, let you change the front end later without rebuilding everything behind it.

4. Own the evaluation

Whatever you buy or build, keep your own set of real past cases with known right answers and check the AI against it. A vendor demo is not proof that a tool works on your data.

A quick test before you sign or start building

  1. Write down the exact task and who does it today.
  2. List every system and document the task touches.
  3. Run the off-the-shelf option on real past examples.
  4. Note where it fails. If the failures come from missing business context, that context is what you build.
  5. Name the person who owns the result. In Infor’s survey, 15% said no one person has primary responsibility for AI, or that it’s unclear who does.

FAQ: off-the-shelf AI vs custom AI

What is the difference between off-the-shelf AI and custom AI?

Off-the-shelf AI is a ready-made product, such as an assistant built into your ERP, office suite or CRM, that works the same way for every customer. Custom AI is built around one company’s own data, rules and systems, for example an agent that reads that company’s supplier invoices and posts them to its ERP using its own approval rules.

Is it cheaper to buy AI or build it?

Buying is usually cheaper to start because the vendor carries the development cost. Building costs more up front but can pay off when the work is specific to your business, runs at high volume, or would otherwise need heavy manual checking of a generic tool’s output. Compare both on a real task with real data.

Can a business combine off-the-shelf AI with custom AI?

Yes, and for many businesses that is the practical answer. A common pattern is to use a vendor’s assistant as the everyday interface and build custom pieces behind it, such as a private knowledge base, document extraction or connections to in-house systems.

Get a clear buy-or-build answer

GrtLabs is an AI and software development company in Houston that helps businesses decide which AI to buy and which to build. Our AI Discovery engagement reviews your data, systems and processes and ends with clear build / no-build recommendations. When building makes sense, our AI Factory team designs and ships it, including on AWS or Azure inside your own cloud account. Talk to us about the task you’re weighing.

Custom MCP Server: When Your Business Needs One and How to Build It Safely
 Custom MCP Server: When Your Business Needs One and How to Build It Safely 150 150 Nadeem Shaikh
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A custom MCP server is a small, controlled service that lets AI assistants and agents read from and act on your own systems, such as your ERP, CRM, database or document store, through one standard connection instead of a new integration for every AI tool. If your team already uses AI assistants and keeps copying data into them by hand, an MCP server is usually the next thing worth building.

MCP stands for Model Context Protocol. It is an open standard that tools like Claude, ChatGPT and Cursor use to connect to outside data and actions. This week shows how fast it is becoming the default: Atlassian rebuilt its MCP server for Jira, Confluence, Loom and Bitbucket and says it now handles more than 15 million tool calls a day, Google moved its MCP Toolbox Java SDK to a stable 1.0, and several data platforms announced MCP access to enterprise data. The big vendors are wiring their products for AI agents. The question for most businesses is how to do the same for the systems that are unique to them.

What an MCP server actually does

An MCP server sits between an AI assistant and one of your systems. It publishes a short list of tools, for example “look up an order”, “list open invoices for a customer” or “create a support ticket”, and describes each one in plain language so the AI knows when to use it.

When someone asks the assistant a question, the assistant picks a tool, the MCP server checks whether that user is allowed to run it, calls your system, and returns only the data the user is allowed to see. Your system never hands its database password to the AI. The MCP server holds that access and decides what goes through.

When off-the-shelf MCP servers are enough

If the data lives in a popular SaaS product, check for an official MCP server first. Atlassian, GitHub, Slack, many CRMs and most major clouds now publish one. Using it is faster and cheaper than building your own, and the vendor keeps it updated.

You need a custom MCP server when:

  • The data lives in a system you built or heavily customized, such as an in-house ERP, a legacy SQL database or a line-of-business app.
  • You want the AI to follow your business rules, for example “never quote below list price” or “only show a customer’s own orders”.
  • You need one tool that pulls from several systems at once, like order status that combines your ERP, shipping provider and support history.
  • Security or compliance means data must stay inside your own cloud or network.

How to build a custom MCP server that is safe to put in production

1. Start with three to five tools, not your whole database

Pick the questions your team asks most often and build a tool for each. Narrow tools with clear names work better than one tool that runs any query. The AI chooses more accurately, and you can see exactly what it is allowed to do.

2. Make reads and writes separate

Read-only tools are low risk and a good first release. Tools that change data, such as creating orders, updating records or sending anything to a customer, should come later and usually ask a person to confirm before they run.

3. Pass the user’s identity through

The MCP server should act as the person asking, not as an all-powerful service account. That way a sales rep sees their accounts, a manager sees their team, and your existing permissions still apply.

4. Log every call

Record who asked, which tool ran, what went in and what came back. When an answer looks wrong, the log tells you whether the data, the tool or the AI was the problem.

5. Keep responses small

Return the fields the AI needs, not whole records. Smaller responses are cheaper to run, faster, and less likely to leak data nobody asked for.

6. Test with real questions before rollout

Write down 20 or 30 questions your team actually asks and check that the assistant picks the right tool and gets the right answer. Run that list again every time you add or change a tool.

MCP server vs. RAG: which one do you need?

They solve different problems and often work together. RAG (retrieval-augmented generation) helps an AI answer questions from documents such as manuals, contracts and policies by finding the right passages first. An MCP server gives the AI live access to systems and actions, such as today’s order status, current stock or creating a ticket.

A simple rule: if the answer is in a document, start with RAG. If the answer is in a system, or the AI needs to do something, you need an MCP server. Many useful assistants use both, with RAG as one of the tools the MCP server exposes.

What to have ready before you start

  • A list of the systems you want the AI to reach, and who owns each one.
  • The top questions or tasks your team would hand to an assistant.
  • How users sign in today (Microsoft, Google or your own login), so permissions can carry through.
  • Which AI tools your team already uses, so the server is tested against them.
  • Where it should run: your AWS or Azure account, or on-premises.

Get help building your MCP server

GrtLabs builds custom AI integrations, agentic AI workflows and AI assistants that connect to the systems your business already runs. If you want your team’s AI tools to work with your own data safely, talk to us about your systems and we’ll help you pick the first tools worth building.

AI Agent Governance: How to Let AI Agents Do Real Work Without Losing Control
 AI Agent Governance: How to Let AI Agents Do Real Work Without Losing Control 150 150 Nadeem Shaikh
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AI agent governance means deciding, before an agent goes live, what it may do on its own, what needs a human yes, and how every action gets logged and reversed. If you can’t answer those three questions for an agent, it isn’t ready to touch real orders, invoices or customer records.

This week the big platforms made the same point. SAP, ServiceNow, IBM and SAS all announced agent features built around audit trails, agent inventories and controls on how much autonomy an agent gets. The tools are new, but the rules behind them are simple enough for any business to apply, whatever stack you run.

Why AI agents need governance and chatbots didn’t

A chatbot answers a question. An agent takes an action: it updates a CRM record, approves a refund, routes a purchase order or sends a file to a vendor. When an answer is wrong, someone reads it and moves on. When an action is wrong, money moves, data changes or a customer gets the wrong message.

That’s the whole reason governance matters. The more an agent can do, the more you need to know what it did, why, and how to undo it.

The five controls every AI agent should have

1. A written scope

List the systems the agent can read, the systems it can write to, and the actions it is allowed to take. Anything not on the list is off limits. Keep it to one page so the business owner, not only the developer, can sign it.

2. Its own identity and least-privilege access

Give the agent its own account instead of borrowing a person’s login or an admin API key. Grant only the permissions its scope needs. If the agent is compromised or misbehaves, you can switch off that one identity without locking out a team.

3. Autonomy levels by action

Not every action carries the same risk. A practical split:

  • Act on its own: reading data, drafting replies, tagging and routing tickets.
  • Act, then report: low-value, easily reversed updates, such as correcting a field in a CRM record.
  • Ask first: anything that spends money, changes a contract, deletes data or reaches a customer.

Start strict and loosen a rule only after the agent has a clean track record on it.

4. A full audit trail

Log every input, every tool the agent called, every decision and every result, with a timestamp. When someone asks why an order was changed, you should be able to answer in minutes, not after a week of digging.

5. An off switch and a rollback plan

Someone on your team should be able to pause the agent in one step. For each write action, know how you would reverse it, whether that’s a database revision, a credit note or a follow-up message.

Keep an inventory of every agent you run

Agents multiply fast. One team builds an email triage agent, another turns on an assistant inside their ERP, a third wires up an automation through a no-code tool. Within a year nobody knows how many there are or what they can touch.

A simple inventory fixes that. For each agent, record its owner, its purpose, the systems it touches, its autonomy level, the AI model it uses and the date it was last reviewed. A shared spreadsheet is fine to start.

Test agents before and after they go live

Before launch, run the agent against a set of real past cases where you already know the right outcome, and check what it gets wrong. After launch, keep sampling its work. Models change, data changes and prompts drift, so an agent that was accurate in month one can quietly get worse by month six.

Where to start if you already have agents running

  1. List every agent and automation that can write to a business system.
  2. For each one, check whether it has its own identity and a log of its actions.
  3. Move every action that spends money or contacts customers to “ask first” until you’ve reviewed it.
  4. Name one owner per agent who is responsible for its results.

None of this needs a new platform. It needs clear rules, applied consistently.

Build agents that are governed from day one

GrtLabs designs and builds AI agents with scope, permissions, approval steps and logging built in, inside your own AWS or Azure account. See how we approach it on our Agentic AI page, or contact us to talk through the agent you want to put to work.

The Benefits of Snowflake and New Generative AI: Making Data Management More Powerful
 The Benefits of Snowflake and New Generative AI: Making Data Management More Powerful 1024 576 Nadeem Shaikh
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The Benefits of Snowflake and New Generative AI: Making Data Management More Powerful

In the evolving landscape of data management and analytics, Snowflake and new generative AI technologies are transforming how organizations handle and leverage their data. These advancements are not just incremental improvements; they represent significant leaps forward in efficiency, scalability, and innovation. This article explores the benefits of Snowflake and how new generative AI technologies are enhancing its capabilities, making it a powerhouse for modern data needs.

Snowflake: A Game-Changer in Data Warehousing

Snowflake has revolutionized data warehousing with its unique architecture and cloud-based solutions. Here are some of the key benefits that make Snowflake a preferred choice for organizations worldwide:

  1. Scalability and Performance
  • Snowflake’s architecture separates storage and compute resources, allowing for independent scaling. This means that businesses can handle massive amounts of data and numerous concurrent queries without a drop in performance.
  • The platform automatically optimizes data storage and retrieval, ensuring fast query performance regardless of the data volume.
  1. Seamless Data Integration
  • Snowflake supports a wide range of data formats and easily integrates with various data sources, from structured to semi-structured data like JSON and Avro.
  • It offers robust data sharing capabilities, enabling secure and efficient sharing of live data across different departments and organizations without the need for data duplication.
  1. Cost Efficiency
  • With its pay-as-you-go pricing model, Snowflake ensures that organizations only pay for the resources they use. This model provides significant cost savings, especially for businesses with variable workloads.
  • Automated scaling and resource management further optimize costs by eliminating the need for over-provisioning.
  1. Data Security and Governance
  • Snowflake provides comprehensive security features, including end-to-end encryption, robust access controls, and compliance with industry standards like GDPR and HIPAA.
  • It also offers powerful data governance tools that help organizations maintain data quality and compliance, ensuring that data is reliable and secure.

Enhancing Snowflake with Generative AI

Generative AI, a subset of artificial intelligence that can generate new content, ideas, and solutions, is making Snowflake even more powerful. Here are some ways in which generative AI is enhancing Snowflake’s capabilities:

  1. Advanced Analytics and Insights
  • Generative AI models can analyze vast amounts of data to uncover hidden patterns and insights that traditional methods might miss. This capability allows businesses to make more informed decisions and gain a competitive edge.
  • By integrating generative AI with Snowflake, organizations can automate complex data analyses, reducing the time and expertise required to extract valuable insights.
  1. Automated Data Cleaning and Preparation
  • One of the most time-consuming tasks in data management is cleaning and preparing data for analysis. Generative AI can automate these processes, identifying and correcting errors, filling gaps, and standardizing formats with minimal human intervention.
  • This automation improves data quality and accelerates the time-to-insight, allowing analysts to focus on higher-value tasks.
  1. Enhanced Data Personalization
  • Generative AI enables hyper-personalization of data-driven applications, from marketing campaigns to customer service solutions. By leveraging AI-generated insights, businesses can create highly personalized experiences for their customers, increasing engagement and satisfaction.
  • In combination with Snowflake’s data sharing and integration capabilities, personalized data can be seamlessly and securely delivered to the right stakeholders.
  1. Predictive Maintenance and Optimization
  • Generative AI can predict future trends and potential issues by analyzing historical data. This predictive capability is invaluable for maintenance, risk management, and operational optimization.
  • When integrated with Snowflake, these AI-driven predictions can be immediately acted upon, ensuring that businesses stay ahead of potential challenges and opportunities.

Conclusion

Snowflake and generative AI are at the forefront of a data revolution, providing unparalleled capabilities for data management, analysis, and utilization. By combining Snowflake’s robust data warehousing solutions with the innovative power of generative AI, organizations can achieve greater efficiency, deeper insights, and more strategic decision-making.

For businesses looking to harness the full potential of these technologies, expert guidance is crucial. Contact GRTLABS experts, who possess advanced knowledge and skills in Snowflake and generative AI, to help you navigate this transformative landscape and achieve your data-driven goals.