While collaborating with a Data & AI podcast series Let's Talk About Data!, we discovered that hundreds of niche, high-quality Data & AI tools exist across the globe — yet most enterprises are only aware of a handful.

This raised a critical question:

Why are all those strong Data & AI tools invisible to the customer base?

35+

Countries with vendors and increasing

100s

Of niche tools tracked globally

The Visibility Problem

Why startups and smaller vendors rarely qualify for analyst recognition — even when they excel at solving real business problems.

Gartner Magic Quadrant for Data Science and Machine Learning Platforms
Forrester Wave - Data Management For Analytics Platforms

How Gartner Shows

How Forrester Shows

Why?

Gartner & Forrester applies strict eligibility criteria — vendors often need:

  • • Minimum enterprise implementations across multiple geographies
  • • Revenue thresholds and funding stability
  • • Recognition in analyst or customer surveys
  • • Broad feature sets, not just single capabilities

This means startups and smaller vendors rarely qualify — even if they excel at solving a real business problem.

What we will make visible!

Startups SME

These companies don't meet Gartner or Forrester eligibility criteria but are often single-feature specialists that solve problems faster and cheaper, staying invisible to enterprise buyers despite being enterprise-ready.

Why Analysts Miss Them

Gartner & Forrester apply strict eligibility criteria:

  • Minimum enterprise implementations across multiple geographies
  • Revenue thresholds and funding stability
  • Broad feature sets, not just single capabilities
  • Recognition in analyst or customer surveys

The Consequence

  • Customers default to "Leaders"

    And pay for large, complex platforms

  • Only 30-40% of features ever used

    But enterprises pay for 100%

  • Smaller vendors with laser-focused solutions

    Could solve the pain point at a fraction of the cost — but never make it onto the radar

Our Role

We extend the horizon by making niche, high-quality startups and SME vendors visible:

Connecting niche tools to enterprises
Pre-vetting for enterprise readiness
Direct access to evaluation

Pain Points & Resolutions

You're Not Seeing the Full Market — And It's Costing You

Enterprises continue to face three recurring challenges:

High R&D Budgets

Wasted on long discovery cycles

Resolution

Reduce R&D time by rapidly identifying the most relevant tools

Blind Spots

80-90% of niche tools never reach evaluation

Resolution

Provide visibility into the most suitable niche and high-quality tools globally

Overpayments

60-70% extra for underutilized platforms

Resolution

Enable access to tools that deliver high value without unnecessary cost

By bridging this gap, we reduce R&D time, expand visibility into global innovation, and enable access to tools that deliver higher value at lower cost.

Cost of Inaction

When the visibility gap remains unaddressed, enterprises face compounding risks:

Multi-year contracts & proprietary architectures

Restrict flexibility and delay change

Vendor Lock-In

Competitors adopt agile, niche tools

Move faster and innovate ahead while you fall behind

Innovation Lag / Lost Competitive Advantage

Outdated or misfit tools

Create workarounds, manual processes, and costly integrations

Growing Technical Debt

Overpaying for mega-vendor solutions

Overbuilt, underutilized, or misaligned with needs

Higher Total Cost of Ownership

Closing this gap isn't optional — it's the difference between keeping pace and falling permanently behind.

The Next Wave of Data & AI Products

Enterprise transformations will not be led by legacy vendors like Oracle, IBM, Microsoft, Informatica, etc.

Rather, they will be driven by Specialized, Agile Technologies

The New Life of a Data Product

Traditional vs New Era — From Months, sometimes, into Minutes/Seconds!

Phase Traditional New Era
Ideation & Discovery Business identifies a need, months of research AI agents autonomously surface needs from patterns — instant inception
Design & Assembly Data models, pipelines, frameworks — weeks to months Auto-generated pipelines, schema, docs. Assembly drops to hours or minutes
Execution & Consumption Data product goes live — lifespan measured in years Data product may serve a single query, then retire. Lifespan can be minutes
Sunset or Evolution Decommissioning or upgrades every 1-3 years Automated retirement or mutation into a new product within seconds

Why Listen to Us?

Collaboration with podcast series Let's Talk About Data! brings unique, vast knowledge and value from across the globe.

We're not guessing the gap — we've mapped it!

Global Reach

Host of Let's Talk About Data!

80+ episodes, 35+ countries, and growing...

Tool Discovery

Mapped the Market

Tracked 100s of niche & high-quality Data & AI tools globally

Community Influence

Massive Reach

50,000+ direct and 3+ million indirect reach to Data & AI communities

Engage us for a curated discovery pilot

We'd love the opportunity to present a detailed look at what we do — and how we make it happen.

Get in Touch