Marketing Architects and Media Consulting Group (MCG) are partnering to combine AI-driven media buying with upstream Connected TV supply optimization, creating a more automated approach to evaluating, shaping and purchasing streaming inventory for performance-focused advertisers.

Connected TV advertising has moved from a primarily brand-oriented channel into an increasingly measurable part of the performance marketing stack. As advertisers shift more budgets toward streaming, the challenge is no longer simply finding available inventory. It is determining which supply can deliver the right audiences, economics and measurable outcomes before a campaign begins.

Media Consulting Group and TV agency Marketing Architects are targeting that problem through a new partnership that combines supply-side intelligence with AI-powered media buying.

MCG optimizes streaming inventory across the United States and Canada, while Marketing Architects uses Annika, its proprietary AI media-buying system, to purchase against the resulting supply. The two companies say the combined approach is designed to improve how premium CTV and OTT inventory is evaluated and activated.

The key difference is where optimization occurs.

Traditional media buying typically begins with available inventory and then applies targeting, bidding and optimization strategies during the buying process. MCG is attempting to move part of that intelligence upstream, evaluating available supply before the bid and organizing it around an advertiser's performance objectives.

The result is intended to give an AI buying system a more relevant supply environment to operate within.

MCG says its AI engineers analyze the broader streaming supply landscape and organize inventory around each buyer's proprietary marketing objectives in near real time. Once that supply has been optimized, Marketing Architects' Annika AI can buy against it.

That creates a two-layer model: MCG focuses on the quality and structure of the supply, while Marketing Architects' AI focuses on buying and optimization.

The distinction is increasingly relevant as CTV becomes more programmatic. Streaming television combines characteristics of traditional television with digital advertising infrastructure, creating a complex environment involving publishers, streaming platforms, ad exchanges, supply-side platforms, demand-side platforms and measurement providers.

Advertisers therefore face a growing supply-path challenge. The same audience can potentially be reached through multiple intermediaries, with differences in fees, inventory quality, transparency and performance.

Supply-path optimization, or SPO, has become a major focus across programmatic advertising because advertisers want greater control over how their media dollars move through the ecosystem. The MCG-Marketing Architects partnership extends that concept into performance-oriented CTV by applying optimization before the media is purchased.

Daniel Elad, Co-Founder and CRO at MCG, said the partnership combines MCG's supply intelligence with Marketing Architects' buying intelligence.

Marketing Architects brings a different piece of the equation. Its Annika platform is designed to continuously learn from campaigns and identify more efficient media opportunities. The company positions the technology as an AI-driven buying system rather than a conventional optimization layer.

By feeding Annika with supply that has already been evaluated against performance objectives, the partnership aims to reduce the amount of inefficient inventory that reaches the buying process in the first place.

That is potentially important for performance TV because advertisers increasingly expect CTV campaigns to demonstrate measurable business outcomes rather than simply reach or completed views.

Full-funnel measurement is therefore another component of the partnership. Marketing Architects says it applies measurement across the customer journey, allowing campaign performance to be evaluated beyond basic delivery metrics.

The competitive landscape includes major programmatic platforms and CTV specialists that already use machine learning for audience targeting, bidding, optimization and measurement. Google, Amazon and other large advertising ecosystems have invested heavily in automated CTV and streaming advertising capabilities, while independent DSPs and supply-side platforms compete around transparency, inventory access and optimization.

The MCG and Marketing Architects approach differs by separating upstream supply intelligence from downstream buying intelligence.

Whether that produces a material performance advantage will depend on how accurately MCG can evaluate supply and how effectively Annika can translate those signals into buying decisions. The partnership also raises a broader question for the CTV market: whether AI optimization should begin before an impression enters an auction rather than only after it becomes available to a buyer.

That could become an important direction as CTV inventory becomes more fragmented.

Streaming platforms continue to add advertising-supported tiers, while traditional broadcasters are expanding digital distribution. More inventory creates more choice, but it also increases complexity for marketers attempting to identify efficient paths to audiences.

AI can help manage that complexity, but automation alone does not guarantee better media economics. The data and inventory available to the algorithms remain critical.

The MCG-Marketing Architects model effectively treats supply quality as an input to AI performance. Instead of giving an automated buying system access to as much inventory as possible, the objective is to improve the quality of the environment in which the AI makes decisions.

For advertisers, that could translate into greater attention to supply-path quality, inventory transparency and pre-bid intelligence as CTV evolves from a reach-focused channel into a performance medium.

Market Landscape

CTV and OTT advertising are becoming increasingly automated as advertisers demand the targeting, measurement and optimization capabilities associated with digital advertising.

The expansion of streaming services has created more inventory, but fragmentation has also made it harder for advertisers to evaluate supply quality. Programmatic CTV can involve multiple intermediaries between the advertiser and publisher, increasing the importance of supply-path optimization and inventory transparency.

Major technology ecosystems including Google and Amazon are competing alongside independent advertising platforms to control CTV buying and measurement infrastructure.

The MCG-Marketing Architects partnership represents another approach: optimize the supply before the AI buying engine makes its decision.

Strategic Outlook

The next phase of CTV advertising may involve increasingly agentic systems that make decisions across the media supply chain rather than simply automating individual bidding or targeting tasks.

If AI can evaluate supply, select efficient paths, execute buying and continuously learn from full-funnel outcomes, media buying could become substantially more autonomous.

But the quality of those decisions will depend on the quality of the underlying supply intelligence. For performance advertisers, that makes upstream optimization an increasingly important part of the CTV technology stack.

Top Insights

• MCG and Marketing Architects are combining upstream CTV supply optimization with AI-powered buying to create a more performance-focused media activation model.

• MCG evaluates streaming inventory before bidding, giving Marketing Architects' Annika AI a supply environment structured around each advertiser's performance objectives.

• The partnership reflects growing advertiser interest in supply-path optimization as fragmented CTV ecosystems make inventory quality and transparency increasingly important.

• Marketing Architects applies full-funnel measurement to AI-driven TV buying, shifting performance TV evaluation beyond reach, impressions and traditional television metrics.

• The combined model points toward more agentic CTV advertising, where AI systems could increasingly evaluate supply, buy media and optimize outcomes with less manual intervention.

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