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Stories

These are all of the stories currently in motion as part of Naftiko go-to-market. Once published stories move into the archives, keeping this list an active representation of the work currently on the table, but managed across multiple platforms, with multiple community stakeholders.

1 - API Reusability - Naftiko Blog

This is blog post on the AI Context use case concerned with encouraging the discoverability and reuse of existing APIs, leveraging existing API infrastructure to quantify what API, schema, and tooling reuse looks like, and incentivizing reuse of APIs and schema across the software development lifecycle—reducing API sprawl and hardening the APIs that already exist.

Title

  • API Reusability

Tagline

  • Embracing your legacy and meeting the demands of AI integration using your existing API investments is how you will get the work done.

Description

This use case is concerned with encouraging the discoverability and reuse of existing APIs, leveraging existing API infrastructure to quantify what API, schema, and tooling reuse looks like, and incentivizing reuse of APIs and schema across the software development lifecycle—reducing API sprawl and hardening the APIs that already exist.

Teams need to be able to easily use existing API paths and operations as part of new integrations and automation, ensuring that paths, operations, and schema are available within IDE and copilot tooling—meeting developers where they already work. API reusability enables developers while also informing leadership regarding what API reuse looks like and where opportunities to refine exist.

Benefits

  • Unlock access to legacy data
  • Right-size & unify APIs
  • Foundation for AI initiatives

Pain

  • Build on existing internal APIs
  • Reuse 3rd-party APIs already used
  • Need to leverage existing OpenAPIs
  • We do not understand what API reuse is
  • We aren’t able to communicate API reuse

Gains##

  • Leverage existing internal API catalog
  • Establish API catalog for 3rd-party APIs
  • Extend existing OpenAPI for MCP delivery
  • We are able to communicate reuse to leadership
  • We are able to meet developers where they work

Connects

  • Internal APIs
  • Infrastructure APIs
  • SaaS APIs
  • Partner APIs
  • Paths
  • Schema

Adapters

  • HTTP
  • MCP
  • OpenAPI

Tags

  • API Management
  • API Reuse
  • Developer Tooling
  • Developer Enablement
  • Developer Experience
  • API Governance
  • AI Governance

2 - Getting On The MCP Bullet Train Without Leaving Governance Waiting At The Platform

As organisations rush headlong into the AI revolution, a familiar pattern is emerging. For integrations, we’ve seen it before with APIs, with microservices, and now with the Model Context Protocol (MCP). The technology arrives, excitement builds, adoption accelerates—and then the cracks begin to show. Today, many enterprises find themselves in precisely this position with MCP, caught between ambitious AI investments and the sobering realisation that their governance practices are failing to keep pace.

3 - Going from being on top of public APIs to feeling the way with MCPs

If you’re running an API product business—one where APIs are your primary revenue stream—you’re likely grappling with a critical question right now. What should our strategy be for MCP (Model Context Protocol) in the age of agentic AI? This has moved on from being a theoretical concern. The signals are everywhere. Your established API business is humming along nicely—you’ve got sophisticated infrastructure, mature governance practices, strong customer onboarding, and the revenue numbers to prove it all works. But then the landscape shifts. Agentic AI arrives. Suddenly, you’re fielding questions about how agents will discover and use your APIs. You’re watching competitors experiment with MCP servers. You’re wondering if your current approach is future-proof.

4 - Pivoting AI-enabled integration to what customers really want

For software solution providers in the SaaS space, the journey towards artificial intelligence integration has become increasingly complex. Many organisations have invested heavily in building their own branded AI experiences—co-pilots, assistants, and intelligent features designed to showcase their deep industry expertise. However, a quiet revolution is taking place in how AI agents interact with services, and it’s forcing even the most API-mature companies to reconsider their strategic priorities.

5 - AI Orchestration Use Case - Naftiko Blog

This is blog post on the AI Orchestration use case, focusing on he data, skills, and capabilities that artificial intelligence agents used internally can use to automate and orchestrate tasks while discovering and negotiating with other agents to accomplish specific goals. This use case employs the open-source Agent-2-Agent specification to securely and confidently enable agentic activity across operations.

Title

  • AI Orchestration

Tagline

  • Planning ahead for teams when it comes to discovery, security, and other properties of the Agent-2-Agent specification helps steer the fleet in the same direction.

Description

This use case provides the data, skills, and capabilities that artificial intelligence agents used internally can use to automate and orchestrate tasks while discovering and negotiating with other agents to accomplish specific goals. This use case employs the open-source Agent-2-Agent specification to securely and confidently enable agentic activity across operations.

As teams focus on responding to this AI moment and deploying MCP servers on top of existing APIs and other tooling, they need to begin understanding how to implement agentic automation and orchestration on top of MCP servers. Teams need structure and guidance when it comes to authentication and authorization, discovery, governance, and all the standardization required to deploy agents at scale.

Tags

  • Automation
  • Agentic
  • Agent-2-Agent
  • Compliance

Benefits

  • Discover skills & capabilities
  • Internal & external agents
  • Implement A2A protocol
  • Apply policy-driven governance

Pains

  • High complexity in standardizing message formats and handshakes.
  • Difficult to cap liability; risk of hallucinated agreements.
  • Requires vetting external agents; security risks.
  • High debugging difficulty; “Black Box” interactions.

Gains

  • Universal connectivity; “Write once, talk to many.”
  • Removal of human bottlenecks in approval chains.
  • Access to dynamic markets and real-time supply chains.
  • Modular system; easy to swap out underperforming agents.

Connects

  • Internal APIs
  • Infrastructure APIs
  • SaaS APIs
  • Partner APIs
  • MCP Servers

Adapters

  • HTTP
  • OpenAPI
  • MCP
  • A2A

6 - Capabilities - API Evangelist

This is blog post on capabilities meant for the API Evangelist blog, providing an opionated look at what capabilities are and why is the time we need them to align engineering with business outcomes, but also provide the much needed context for powering AI copilots and agents, help increase we will achieve the outcomes we desire.

7 - Cost - Naftiko Blog

This is blog post on managing costs when it comes to integrations and automation using a capabilities-driven approach, focusing on the cost, spend, and budget manage aspects of integrating primarily across 3rd-party services, but also possibly with internal APIs, helping bring more attention to the cost of operation integrations and automation.

8 - Data Sovereignty Use Case - Naftiko Blog

This is blog post on the AI Context use case, focusing on empowering companies to take control of their data that resides across the third-party SaaS solutions they use regularly. The data sovereignty movement is centered on establishing more control over the data generated across the different services you depend on, ensuring data is integrated, migrated, and synced to data and object stores where a company has full control and access.

Title

  • Data Sovereignty

Tagline

  • Govern, encrypt, and audit how data moves through your entire stack.
  • APIs, SaaS tools, and AI all touch sensitive data but governance rarely keeps up. Shadow IT, unencrypted transfers, and compliance risk abound.

Description

This use case focuses on empowering companies to take control of their data that resides across the third-party SaaS solutions they use regularly. The data sovereignty movement is centered on establishing more control over the data generated across the different services you depend on, ensuring data is integrated, migrated, and synced to data and object stores where a company has full control and access.

Data sovereignty enables teams to localize data and train local AI models using the data they produce across third-party platforms. This use case may be aligned with country or regional regulations, or it may simply be part of enterprise compliance programs. Data sovereignty investments have increased as part of the growth of AI integrations and the need for context across third-party systems, as well as the increasing value of data itself.

Tags

  • Data
  • Regulation
  • Compliance
  • Sovereignty
  • Control

Benefits

  • Aggregate 3rd-party SaaS data
  • Increase visibility of SaaS data
  • Allow for more SaaS data discovery
  • Encourage the reusability of SaaS data
  • Enable ETL/ELT access to SaaS data

Pain

  • Difficulty in Accessing 3rd-Party Data Sources
  • Regulatory Mandate for Control Over All Data
  • GraphQL Was Difficult to Adopt Across Teams
  • Lack of Data Available for AI Pilot Projects

Gains

  • Provide SQL Access Across 3rd-Party Data Sources
  • Satisfy Government Regulatory Compliance Requirements
  • Speak SQL Across All Data Sources For Any Teams
  • Universal Access to Data for Use in AI Projects

Connects##

  • Infrastructure APIs
  • SaaS APIs
  • Partner APIs

Adapters

  • HTTP
  • OpenAPI

9 - Innovation - Naftiko Blog

This is blog post focus on innovation, helping shine a light on how managing cost, velocity, and risk can help lead to more innovation, helping enterprises achieve an agreed upon around what innovation looks like by focusing on capabilities that consistently drive conversations around cost, velocity, and risk of integration and automation.

10 - Naftiko Signals - API Evangelist

This is blog post announcing the Nafitko Signals progam on the API Evangelist blog, providing a different perspective on how the program came to be and why it is turning into something more, helping provide a behind the scenes snapshot on what is going on with the research, but also the conversations we are having with design, service, and market partnrs.

11 - Naftiko Signals White Paper - Naftiko Blog

This is a blog post about the Naftiko Signals white paper that was published in December, providing an overview of the paper and the program behind, and why Signals provides an important way of looking at the enterprise system whether you are inside or outside of that system, helping generate more leads using the white paper.

12 - Risk - Naftiko Blog

This is a business outcomes blog post focused on managing risk when it comes to integrations and automation, helping demonstrate how a capabilities-driven approach can help with security, privacy, compliance, and other common approaches to managing risk across enterprise operations, from the dimensions business care about.

13 - SQL Data Access Use Case - Naftiko Blog

This is blog post on the SQL Data Access use case, focusing on consistently unlocking the data companies currently depend upon across multiple third-party SaaS providers and a variety of existing database connections via JDBC and ODBC to ensure AI integrations have the data they require. Data today is spread across many internal and external systems, and making it consistently available as part of AI integrations has significantly slowed the delivery of new products and features.

Title

  • SQL Data Access

Tagline

  • Data lives in silos. Teams want insights now but every new API means another custom connector.

Body

This use case seeks to consistently unlock the data companies currently depend upon across multiple third-party SaaS providers and a variety of existing database connections via JDBC and ODBC to ensure AI integrations have the data they require. Data today is spread across many internal and external systems, and making it consistently available as part of AI integrations has significantly slowed the delivery of new products and features.

Teams benefit from consistent SQL access to data sources via ODBC/JDBC interfaces, and expanding this access to third-party SaaS will help teams provide the context, resources, tooling, and data needed to deliver AI integrations across the enterprise. The capability and resulting engine deployment for this use case provides a unified, consolidated, and simplified approach to providing the data needed to power individual AI integrations within specific business domains.

Tags

  • SQL
  • Data
  • SaaS
  • Dashboards
  • Analytics
  • Copilots

Benefits

  • Unlock SaaS data
  • JDBC / ODBC drivers
  • Federated SQL processing

Pain

  • Limited or No Access to SaaS Data for Analytics Teams
  • No Access to Data Sources Across MCP-Enabled AI Integration
  • Demand for 3rd-Party Data for Business Intelligence in Dashboards
  • Demand for 3rd-Party Data by Data Science for ML Engineering

Gains

  • Access to SaaS Data via SQL
  • Access to Internal APIs via SQL
  • Easy Connections to Existing Dashboards
  • Plug-and-Play Connectors for Data Science

Connects

  • Internal APIs
  • Infrastructure APIs
  • SaaS APIs
  • Partner APIs
  • Legacy APIs

Adapters

  • HTTP
  • OpenAPI
  • ODBC
  • JDBC
  • MCP

14 - Velocity - Naftiko Blog

This is business outcomes blog post focued on velocity associated with integrations and automation, helping shine a light on moving the right velocity when it comes to the 3rd-party and internal systems we use, helping ensure we have a solid map of the services and the domains in which they are used to help teams move faster when using.