Teqblaze - from manual monetisation platform to AI-assisted optimisation

Find out more about Teqblaze, the ad tech expert which is building upon its considerable experience in programmatic platforms to pioneer a new 'AI-assisted' optimisation service, which treats "monetisation as an infrastructure problem, not a growth hack".

PocketGamer.biz spoke to CEO, Anastasia Nikita Bansal to find out more about the company's foundation, evolution, her views on the challenges facing the global games market and how Teqblaze plans to help publishers build more predictable, sustainable monetisation - without sacrificing transparency or control.

PocketGamer.biz: The mobile ad tech space is very competitive. What was the gap in the market or the specific unmet need for developers that Teqblaze was founded to solve?

Anastasia Nikita Bansal: When we started TeqBlaze, the mobile ad tech market was already crowded, yet many app publishers still struggled with a very basic issue: they couldn’t clearly see or control how their monetisation decisions translated into real outcomes.

Platforms tended to optimise for scale or automation in isolation, while publishers were left balancing revenue, quality, and operational complexity on their own.

We saw a gap for a sell-side platform that treats monetisation as an infrastructure problem, not a growth hack. One where publishers can understand their supply paths, make deliberate choices about demand, and adjust strategy based on data rather than guesswork.

Another important part was automation that actually supports teams. Not replacing people, but reducing manual overhead and helping operators focus on higher-value decisions.

At its core, TeqBlaze was built to help publishers build more predictable, sustainable monetisation over time, without sacrificing transparency or control as they scale.

Given the complexity of programmatic advertising, how have your experience and previous leadership roles informed Teqblaze's approach to product development and client service?

Programmatic is messy, and my experience has mostly come from dealing with that mess in real life. At the point when we separated and became an independent business, it wasn’t about adding more technology.

We had to take a set of separate solutions and turn them into a single system that people could rely on every day. That experience shaped a very clear principle for us: even in a complex programmatic environment, the product has to stay manageable and scalable.

This is where our focus on a unified white-label ecosystem came from. SSP, Ad Exchange, DSP, and SDK shouldn’t feel like disconnected tools. They need to work together, with consistent logic, so teams don’t have to constantly switch contexts or rebuild processes as they grow.

That same experience also changed how we think about automation. In ad tech, it’s easy to keep adding features, but that often increases manual work instead of reducing it. For us, AI and machine learning are not experiments. They’re practical tools to simplify traffic management, supply path optimisation, and pricing, especially in areas that used to require constant manual control.

The way we work with clients has evolved as well. Our partners come from very different markets and have very different levels of ad tech experience, so a standard support model simply doesn’t work.

That’s why we put a lot of emphasis on tailored onboarding, training, and a consultative way of working, so clients don’t just launch a platform, but actually understand how to operate it. Internally, product, account management, and technical teams work closely together, and client feedback feeds directly into product priorities.

For me, the real challenge in programmatic isn’t complexity itself, but making that complexity usable without losing control, flexibility, or alignment with industry standards.

Beyond standard mobile platforms, where do you see the next significant ad monetisation opportunities emerging? Are you tracking playable ads in CTV or ad placements in emerging sectors, such as spatial computing (VR/AR)?

When people talk about next-gen platforms, I think the biggest shift isn’t really about brand-new formats, but about programmatic gradually moving beyond traditional mobile. We’re already seeing more activity in areas like CTV and DOOH, where automation and programmatic logic are still developing and not as standardised as they are in mobile or web.

Interactive formats, including things like playable ads, are interesting, but they bring very practical challenges. Measurement, latency, and quality control often matter more than the format itself. The same applies to emerging environments like VR or AR. There’s clear long-term potential, but right now these spaces are still fragmented, with limited scale and few common standards.

So in the near-term, the most realistic opportunities tend to come from applying existing programmatic approaches to new types of inventory, rather than chasing entirely new platforms before the ecosystem is ready.

As programmatic expands and becomes more complex across environments and formats, what do you think the industry underestimated most in terms of how this complexity would need to be managed?

I think the industry underestimated how hard it would be to manage complexity as a system, not just as a set of tools. For a long time, there was an assumption that adding more technology, algorithms, or optimisation layers would naturally solve new problems as programmatic expanded. In reality, each new format, channel, or integration added new dependencies across the stack, not just another feature to manage.

What was missing was a clear, shared approach to ownership and decision-making. Pricing, demand access, formats, and quality evolved in parallel, but without a unifying logic that ties those decisions together.

The operational impact was also underestimated. As stacks grew, teams spent more time maintaining integrations and troubleshooting behaviour they couldn’t fully explain, rather than improving monetisation itself.

What is the most common structural mistake you see publishers make when building or scaling their ad stack, especially as they add more partners and tools?

The most common structural mistake I see is scaling the ad stack without designing it as a system. As publishers grow, they tend to add partners and tools to solve very specific problems: more demand here, better pricing there, higher fill somewhere else. Each addition makes sense on its own, but over time, the stack becomes a collection of disconnected decisions.

Every partner optimises for their own metric, but no one really owns how the stack behaves as a whole. When performance shifts, it’s hard to tell what actually caused it, because the logic is spread across too many layers.

Another issue is giving up control over key decisions too early. Pricing rules, traffic routing, and access to demand are often delegated to external systems without clear internal ownership. That can work at a small scale, but once volume grows, it creates operational friction and risk.

The mistake isn’t adding partners. It’s postponing architecture until complexity has already become a problem.

Looking ahead to 2026, what is the single biggest technological or infrastructure challenge you believe the mobile gaming industry needs to solve collectively to unlock the next level of profitability?

When I look ahead to 2026, the biggest challenge for mobile gaming isn’t about new formats or better algorithms. It’s architectural.

Over the years, the industry has built very sophisticated ad stacks, but not necessarily manageable ones. Decisions about pricing, demand access, formats, and traffic behaviour are spread across different systems, each acting on its own logic. No single layer really owns how the stack behaves as a whole.

That lack of ownership creates friction. When revenue shifts or player experience changes, it’s hard to understand why. The system produces outcomes, but not explanations. And as scale grows, that gap turns into real cost, operationally and financially.

Unlocking the next level of profitability requires the industry to rethink ad infrastructure as something that needs to be owned and directed, not just connected. When publishers can clearly see and control how monetisation decisions are made, complexity stops being a risk and starts becoming an advantage.

That shift, from assembling tools to designing systems, is what I believe will matter most over the next few years.

What is the next major feature or key market expansion planned for Teqblaze over the next 12 to 18 months?

Over the next 12 to 18 months, our main focus is on expanding TeqBlaze as an AI-assisted monetisation platform that helps publishers move faster and make better decisions with less manual effort.

One of the key areas we are investing in is TeqMate AI as a practical automation and decision-support layer. Publishers today spend a huge amount of time on routine operational tasks: setting up configurations, monitoring performance changes, and reacting to anomalies. Our goal is to automate as much of this work as possible, so teams can focus on optimisation and strategy rather than constant manual control.

Another major direction is AI-powered automated A/B testing, especially for in-app monetisation. A/B testing is often slow, complex, and underused because it requires manual setup, long evaluation cycles, and statistical expertise.

We are building AI-driven testing that allows publishers to launch A/B and multivariate tests with minimal configuration. The system evaluates statistical significance, explains performance changes, and recommends next steps: whether to scale, stop, or iterate. This enables continuous optimisation instead of occasional experiments.

We are also evolving our data and analytics experience. Publishers already have access to large amounts of data, but turning it into clear, actionable insights is still a challenge. Our focus is on faster, more intuitive analytics where AI highlights what changed, why it matters, and what should be optimised next, rather than relying on static dashboards.

Overall, these developments move TeqBlaze from a manual monetisation platform to an AI-assisted optimisation system. The real value for publishers is speed, clarity, and control: faster testing, smarter decisions, and continuous performance improvement at scale without growing operational overhead.