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The orchestration layer that replaces human media coordination—entirely.
One mandate in. Optimised, learning, always-on execution out. No planning docs. No strategy rounds. No briefing cycles. Just a goal and an AI that knows how to hit it.
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The Problem

Media buying is broken.

Brands pay agencies 15–20% of spend to coordinate campaigns across platforms. The process is manual, slow, and reactive. Signals are missed. Budgets are wasted. Nobody learns from what happened last time.
15–20%
Agency Fee
Percentage of media spend charged for coordination that AI can do better, faster, and cheaper.
Days
Signal Lag
By the time a trend report reaches a planner, the window has closed. The system sees it in hours.
Zero
Learning Loop
Every campaign starts from scratch. No institutional memory. No compounding intelligence.
The Pipeline

We don’t store data.
We store meaning.

Raw signals are processed and discarded. Only distilled intelligence is kept. Every stage compresses data into a denser, more useful form. Seven stages. Three LLM calls. Everything else is code.
01
Raw Ingest
GDELT, Google Trends, Reddit, YouTube, platform APIs, financial filings. Processed in memory. Never stored.
Raw data discarded
02
Signal Triage
Scored by reliability, velocity, persistence, category relevance. Below threshold: discarded. Corroborated: confirmed.
Unconfirmed discarded
03
Briefing Functions
First LLM call. Confirmed signals + brand context in, structured JSON briefing out. Compact. Queryable.
Stored in vector store
04
Payload Assembler
No LLM. Pure code. Queries vector store by semantic similarity. Selects briefings, injects skill and tolerances.
Deterministic
05
Recommendation Bot
Second LLM call. Multi-pass reasoning. Returns structured action with confidence, tolerance check, and escalation flag.
Stored
06
MCP Execution
Approved instruction sent to platform via MCP adapter. Platform executes. Returns current state. Duplex.
Action logged
07
Analysis Bot
Third LLM call. Action taken vs outcome. Produces annotation, score, and verdict: REPEAT, ADAPT, or STOP.
Stored in vector store
The Intelligence

This is what a briefing looks like.

Structured JSON. Not prose. Every field queryable by the Payload Assembler. This is one daily Fast Track briefing for a beauty brand at 07:00.
Briefing Output — Fast Track Daily · Beauty Brand · 07:00
Structured JSON. Compact. Every field queryable by the Payload Assembler. Not prose—precision.
Signals (confirmed, ranked)
purple_lip_trend confidence 0.87 · peak T−4 days
berry_aesthetic confidence 0.72 · peak T−11 days
competitor_A_dark spend drop 34% · 3 days
CPM_beauty_UK trending down 8% · 48hrs
discarded_signals 847 below threshold
Brand Context Retrieved
matching_products Luxe Violet Lip · Berry Gloss
brand_permission trend_reactive TRUE
staged_creative 4 assets ready · violet theme
rules_triggered 2 of 47 retrieved
mandate_progress 1.61:1 · target 2:1
Recommended Attention
priority_1 Amazon DSP bid amplification — pre-peak window open
priority_2 Competitor dark — reallocate SOV budget
prior_pattern REPEAT · pre-peak bid scored 9.1/10 · 4/4
daily_envelope £4,200 · band ±£840
The Execution

From intelligence to platform action in one step.

A briefing becomes an instruction. The instruction reaches the platform via MCP. The platform confirms. The system records the outcome. All within the tolerance band.
Briefing (vector retrieved)
purple_lip_trend · 0.87 · peak T−4
prior_pattern: REPEAT pre-peak bid
competitor_dark · CPM falling
mandate: 1.61:1 · needs acceleration
→
Skill: amazon_dsp_optimise_v2
You are optimising an Amazon DSP campaign.
Daily envelope: £4,200. Band: ±20%.
Return mandate: 2:1. Current: 1.61:1.
CPA floor: £4.20. Output: JSON only.
→
Brain Output (JSON)
action: increase_bid_violet_lip_18%
reallocate: awareness → conversion £620
pause: creative_3 (frequency exceeded)
confidence: 0.91 · within_band: true
→
MCP → Amazon DSP
bid_adjustment: Luxe Violet Lip +18%
budget_shift: line_item_003 +£620
creative_pause: ad_id_cr3
↩ confirmed · CTR +22% vs yesterday
The Learning Loop

Every execution makes the next one smarter.

Every action produces an annotation. Every annotation refines a rule. Every refined rule makes the next campaign more precise. This is how institutional knowledge compounds.
Annotation Record · Amazon DSP · 14:32
Repeat — Score 9.4 / 10
Action taken: Bid +18% on Luxe Violet Lip · Reallocated £620 to conversion
Signal basis: Pre-peak window T−4 · Competitor dark · CPM falling
Outcome: CPA £4.80 → £3.90 · CTR +22% · ROAS 2.4:1 on segment
vs expectation: Outperformed · Mandate progress +0.29 in 6hrs
Pattern: Pre-peak bid amplification during competitor dark period compounds efficiency. Effect stronger when CPM also falling.
Learned Rule Update · Written to knowledge store
Autonomous — apply without sign-off
Rule ID: opt_prepeakbid_003
Condition: Horizon signal T−3 to T−5 AND competitor spend declining AND CPM falling
Action: Increase bid 15–20% · Shift budget from awareness to conversion
Evidence: 5/5 successful · Avg CPA improvement 27% · Avg ROAS uplift 0.6
Confidence: HIGH · Apply autonomously within Fast Track band
Exceptions: Do not apply if no_trend_reactive set · Do not apply if CPA floor at risk
The Architecture

Why not just ask an LLM
to run your ads?

You could. But intelligence without infrastructure is just conversation. This is what turns an LLM into an operating system—a swarm of dedicated agents, a vector-indexed knowledge store encoding decades of proprietary advertising strategy across 1,536 semantic dimensions, and a learning loop that compounds with every campaign it runs. No two recommendations are ever the same. They can’t be.
8
Autonomous Agents
Working through the night. Signal triage, performance analysis, tactic matching, budget execution.
1,536
Semantic Dimensions
Every signal, tactic, and insight embedded in vector space. Semantic retrieval, not keyword matching.
4-Pass
Intelligence Pipeline
Situation assessment, tactic search, knowledge retrieval, contextual generation. Nothing is generic.
∞
Compounding Memory
Every campaign outcome feeds back into the knowledge store. The system gets smarter with every pound spent.
Connects To
2,000+
Knowledge Records Indexed
1,536
Semantic Dimensions
18B+
Decision Permutations / Year
1,624
Live Signal Pathways
The Moat
The compounding moat: Every campaign the system runs produces annotated action records. Every annotation refines a rule. Every refined rule makes the next campaign smarter. After 12 months of operation, the system carries institutional knowledge about a brand’s category, competitors, and optimal patterns that took years of human experience to accumulate—and that no competitor can replicate without running the same campaigns. The moat is not the algorithm. It is the annotated history.
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AIAD Intelligence is currently in closed beta. We are onboarding select brands and agencies.
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