I’ve spent fifteen years watching the API industry from the outside, reading analyst reports, attending conferences, and talking to practitioners. The gap between what vendors say the market is doing and what engineering teams are actually building has always been wide. With AI, that gap has become a canyon. Everyone claims they’re “AI-first.” Everyone says they’re adopting agents. Everyone has an MCP strategy. But when you look at the engineering signals — the job postings, the open-source contributions, the SaaS portfolios, the standards participation — the reality is far more nuanced and far more interesting.
That’s why we built Naftiko Signals.
What Signals Does
Signals is an open intelligence platform that tracks technology investment across 311 enterprise companies spanning 49 industries. It’s available at signals.naftiko.io and the entire dataset is open source on GitHub.
For each company, we profile investment across four dimensions:
Areas of Technology. The technical domains a company actively invests in. Not what they talk about at conferences — what they’re actually hiring for, contributing to, and building around. AI, APIs, cloud infrastructure, containers, platform engineering, security, observability, governance. Forty-four signal groups in total, each measuring a specific dimension of enterprise investment.
SaaS Portfolio. The services and platforms a company buys, builds, or integrates. This is one of the most revealing dimensions because it shows where a company trusts the ecosystem versus where they go it alone. An enterprise running fourteen different integration platforms tells a very different story than one that has consolidated around two.
Standards. Participation in open standards and specifications — OpenAPI, AsyncAPI, JSON Schema, MCP, A2A. Standards adoption is a strong signal of interoperability commitment. Companies that invest in standards are building for composability. Companies that don’t are building silos, regardless of what their architecture diagrams say.
Tooling. The developer tools and open-source projects a company adopts or contributes to. Engineering culture is visible in tooling choices. A company contributing to CNCF projects has a fundamentally different engineering culture than one that builds everything internally.
Why 44 Signal Groups
The number of signal groups isn’t arbitrary. We started by mapping the dimensions that matter for understanding enterprise readiness for AI-driven integration — which is Naftiko’s core domain. But it quickly became clear that AI readiness can’t be assessed in isolation. You need to understand the cloud posture, the API maturity, the integration strategy, the data infrastructure, the governance model, and the organizational design before you can say anything meaningful about whether a company is ready for agentic AI.
So the signal groups span the full stack: Artificial Intelligence, Cloud, Open-Source, Languages, Data, Databases, Virtualization, Specifications, Context Engineering, Data Pipelines, Containers, Platform, API, Integrations, Event-Driven, Observability, Governance, Security, Automation, ROI & Business Metrics, Regulatory Posture, AI FinOps, Provider Strategy, Talent & Organizational Design, and more.
Each signal group includes a description of what we measure and how we interpret it. “Measuring the AI investment” doesn’t mean counting press releases. It means evaluating ChatGPT usage patterns, MCP adoption, agentic automation investment, and the depth of organizational understanding.
The Analysis Layers
Raw signals are useful but not sufficient. The interesting part is what happens when you look across companies and industries for patterns.
AI Waves. We identify recurring waves of AI adoption — patterns that emerge as technology matures and spreads. Large Language Models, Coding Assistants, Context Engineering, Retrieval-Augmented Generation, Model Routing, Agent-to-Agent protocols. Each wave has a lifecycle, and companies are at different points on each one. Knowing where a company is on the Context Engineering wave versus the RAG wave tells you something specific about their AI maturity that a generic “AI adoption score” never could.
Roles. Each wave reshapes the talent landscape. We map which roles are growing, which are shrinking, and which entirely new ones are emerging. The appearance of “AI Platform Engineer” and “Context Engineer” in job postings is itself a signal — one that predicts organizational investment more reliably than strategy announcements.
Impact. We assess the downstream impact of each wave on markets, workflows, and the cost/velocity/risk balance inside enterprises. This is where signals become actionable. If you can see that companies in your industry are uniformly investing in context engineering while your organization is still debating whether to adopt coding assistants, that’s a strategic data point.
The Radar
One of the most useful views in Signals is the Naftiko Radar — a visual map of every technology, service, tool, and standard we track, plotted across four adoption rings: Adopting, Optimizing, Evaluating, and Watching.
The Radar gives you a single view of where the industry is now and where it’s heading. It’s updated as our signal data changes, so it reflects the current state of enterprise investment rather than a quarterly snapshot that was already stale when it was published.
Open by Design
This is deliberate and important: Signals is fully open source. The entire platform is a Jekyll site hosted on GitHub Pages. Every data point — companies, industries, signals, services, tools, standards, waves — is a structured YAML file in a Git repository. You can inspect it, fork it, contribute to it, or build on top of it.
We made this choice because intelligence about enterprise technology investment should be as accessible as the open-source projects it tracks. The analyst model of charging six figures for a PDF that’s outdated before the ink dries is broken. The engineering community deserves better signal sources, and the best way to build trust in data is to make it transparent.
Why This Matters for Naftiko
Signals isn’t separate from Naftiko’s core mission — it’s foundational to it. Naftiko builds governed, spec-driven integration capabilities that connect enterprise APIs to AI agents. To do that well, we need to understand what APIs enterprises are actually using, what integration patterns they’ve adopted, what standards they participate in, and where they are on the AI adoption curve.
Signals gives us — and gives every technology leader — that understanding. When we profile a company’s SaaS portfolio and see fourteen integration platforms, that’s not just a data point. That’s a signal about integration complexity, governance gaps, and the opportunity for a capability-driven approach. When we see a company investing heavily in MCP and context engineering, that’s a signal about readiness for the kind of spec-driven integration Naftiko provides.
The signals inform the capabilities. The capabilities reduce the complexity that the signals reveal. That’s the loop.
Get Started
Browse the platform at signals.naftiko.io. Start with the industries view if you want to see how your sector is investing. Start with the company view if you want to benchmark against specific organizations. Start with the radar if you want the broadest view of where enterprise technology is heading.
The data is open. The signals are clear. What you do with them is up to you.
