AI Integration & MCP

Q: What is MCP (Model Context Protocol) and why does it matter for my business?

MCP is an open standard that lets AI assistants securely connect to external tools, data and systems. Instead of building a separate integration for every AI tool your team adopts, one MCP server gives any MCP-compatible assistant the same secure, approved way to reach your systems.

Q: What is function calling and why does it matter for AI integrations?

Function calling lets an LLM invoke a predefined function with structured parameters instead of just generating text. It's how an agent moves from suggesting an action to actually performing it through your APIs.

Q: What is AI sprawl and why is it a problem?

AI sprawl is when an organization ends up with multiple disconnected AI tools built by different teams, each with its own data access, governance, and maintenance burden. It creates duplicated work, inconsistent security, and no single view of what AI is actually doing.

Q: Can Akantik integrate AI agents with legacy or non-REST APIs?

Yes, we've integrated agents against SOAP, GraphQL, and legacy XML-based APIs that were never designed for this kind of access, wrapping them behind a cleaner interface the agent can use reliably.

A model that can only generate text is a demo. A model that can call your APIs, update your CRM, or reach your internal tools through a properly scoped MCP server is a system that does work. We connect AI to the systems you already run, and to each other, without handing it more access than it needs.

MCP Development & Integration • AI + ERP/CRM • AI + APIs • Enterprise AI Integration

Scoped access, not wide-open : Every integration exposes exactly what's needed and nothing more.
Works with what you already run : Dynamics 365, Salesforce, SAP, and legacy APIs, no forced platform migration.
Auditable by design : Every call is authenticated and logged, so you can trace exactly what an agent did.

MCP Integration Architecture
Scoped, Authenticated, Auditable

MCP Development & Integration

The Model Context Protocol (MCP) is an open standard that lets an AI assistant securely reach outside of its own training into your approved tools, data and systems, instead of you building a one-off integration for every AI tool your team adopts. One MCP server gives every MCP-compatible assistant, Claude, ChatGPT, Copilot, the same secure way in.

Custom MCP servers for your systems : We expose your internal APIs, databases, and business tools to approved AI assistants, so an agent can look up real data and take real action.
Scoped tool exposure : We define exactly which functions, queries, and actions an MCP server exposes, a narrow, deliberate surface instead of a wide-open API.
Custom servers for proprietary systems : Off-the-shelf connectors don't exist for most internal tools, so we wrap your proprietary databases and legacy systems directly.
Authentication and access boundaries enforced : Every tool call is scoped to what that specific integration is allowed to do, and every request is logged for audit.
Extension of existing MCP servers : Already running an open-source or third-party MCP server? We extend and adapt it to your data model and workflow.
MCP integration into your own product : Building a SaaS platform or internal tool? We add MCP connectivity so your users can plug their AI assistant directly in.

AI + ERP/CRM

Your ERP and CRM already hold the data, deals, orders, inventory, customer history. The problem usually isn't the data, it's that nobody has time to dig through it before a decision needs making. Instead of replacing Dynamics 365, Salesforce, or SAP, we layer AI on top of what's already there.

Lead and opportunity scoring : Historical deal data trains a model that ranks leads by likelihood to close.
Demand forecasting : Sales history and pipeline feed a model that flags a demand shift early, before it shows up as a stockout.
Automated data entry : Order confirmations, invoices, and intake forms get parsed and written into your ERP/CRM automatically.
Natural-language reporting : Your team asks a question in plain English and gets an answer pulled straight from your CRM data.
Integration without a platform rebuild : We build against the APIs your existing platform already exposes.

AI + APIs

We wire agents into your internal and third-party APIs with function-calling patterns that define exactly what an agent can invoke, with what parameters, and under what conditions, scoped to the task instead of exposing everything by default.

Defined function-calling contracts : Every action is a specific function with typed parameters, not open-ended API access the model has to guess how to use.
Rate limiting and retry logic : Backoff, retry, and circuit-breaker logic so a bad response doesn't cascade into an outage.
Error handling an agent can actually use : API failures are surfaced in a form the agent can reason about, so it can retry or hand off instead of silently failing.
Cross-system orchestration : For tasks touching multiple systems, we sequence the calls so partial failures don't leave data inconsistent.
Scoped credentials per integration : Each API connection runs on credentials scoped to exactly what that integration needs.

Enterprise AI Integration

Most companies don't have an AI problem, they have an AI sprawl problem. A chatbot here, a copilot there, an automation script somewhere else, none of them sharing context or governed the same way. We connect what you've already built into one coherent, governable layer.

A shared context layer : AI features across your organization draw from the same underlying data instead of each tool maintaining its own disconnected view.
One governance model, not five : Access controls, audit logging, and data handling policies are defined once and applied consistently.
Consolidation of overlapping tools : When two departments have quietly built the same capability twice, we consolidate onto one system.
A central integration point for new AI features : Future capabilities plug into the same layer instead of becoming the next disconnected tool.
Visibility into what AI is actually doing : Centralized logging and monitoring give you one place to see what every AI system is doing.

Have systems an AI agent should be able to reach securely, or AI tools scattered across your organization with no shared governance? Let's connect them.

Connect My Systems to AI

What is MCP (Model Context Protocol) and why does it matter for my business?

MCP is an open standard that lets AI assistants securely connect to external tools, data and systems through one server instead of a separate integration per tool.

Why not just give an AI agent direct database or API access?

Direct access means an agent can do anything the underlying credentials allow. Scoped integration narrows that down to specific, reviewed actions, and logs every call.

Do we need to replace our current ERP or CRM to add AI features?

No. Akantik builds AI capabilities that integrate with the ERP or CRM platform you already run, using their existing APIs, rather than requiring a platform migration.

What is function calling and why does it matter for AI integrations?

Function calling lets an LLM invoke a predefined function with structured parameters instead of just generating text, moving from suggesting an action to performing it.

What is AI sprawl and why is it a problem?

AI sprawl is multiple disconnected AI tools built by different teams, each with its own governance, creating duplicated work and inconsistent security.

Can Akantik integrate AI agents with legacy or non-REST APIs?

Yes, we've integrated agents against SOAP, GraphQL, and legacy XML-based APIs, wrapping them behind a cleaner interface the agent can use reliably.