Reduce wasted ad spend. Our models forecast campaign performance with confidence intervals and shift budget toward stronger channels — inside spend caps, floors and guardrails you set, or in advisory mode only.
All Algorithmic Marketing →
Most marketing teams operate on rearview-mirror analytics — they know what happened last month, but they're essentially guessing about tomorrow. Predictive marketing analytics inverts that paradigm entirely. We build statistical and machine-learning models that ingest your historical ad spend, conversion events and customer lifecycle signals to estimate CPA, LTV and campaign ROI before you commit budget to a new campaign. These are forecasts with error bars, not certainties — and reporting the error bar is the point.
The predictive modeling pipeline begins with data unification. Our systems connect to Google Ads, Meta Ads, LinkedIn Campaign Manager, TikTok Ads, and your CRM through native API integrations, aggregating every impression, click, conversion, and downstream revenue event into a unified data warehouse. From this foundation, we train models on your specific business context rather than generic industry benchmarks; the right model class depends on your data volume, and at typical account sizes that is usually gradient-boosted trees rather than a deep network. The CPA model analyses many feature dimensions: creative type, audience segment, day-of-week patterns, seasonal trends, competitive auction density, and landing page performance to generate probabilistic forecasts for every campaign configuration you're considering.
LTV modeling is where the strategic advantage compounds. Traditional marketers optimise for cost-per-lead or cost-per-acquisition, but these metrics are fundamentally incomplete — a cheap lead that never converts costs far more than a premium acquisition that generates five years of revenue. Our LTV models analyse cohort behaviour patterns, purchase frequency distributions and churn probability curves to estimate the lifetime value of each customer segment early in its life. This enables you to bid aggressively on high-value audiences that your competitors undervalue because they're only measuring surface-level conversion metrics.
The real-time budget optimisation engine operates continuously. As campaign data flows in, the system recalculates predicted ROI across every active channel and audience segment, autonomously shifting budget allocation toward combinations that maximise marginal return. Underperforming creatives are paused before they waste significant spend. Emerging high-performers are scaled instantly. It does not wait for weekly reports or monthly reviews: it makes many small adjustments a day, inside your caps and floors, compounding modest efficiency gains at a cadence a person cannot sustain by hand.
Native API connectors pull every impression, click and revenue event from your ad platforms and CRM into a single warehouse.
CPA and LTV models are trained on your business context, validated against holdout sets and shipped with confidence intervals.
The engine recalculates predicted ROI around the clock, shifting budget, pausing losers and scaling winners within your guardrails.
Forecast error is reported per model with confidence intervals, on your own data, and we agree an acceptance threshold with you before go-live — a single headline accuracy percentage would be meaningless here, because 'accuracy' is not even well defined for a continuous forecast like CPA or LTV. What we commit to is method: every model is validated against a holdout set, every forecast ships with its confidence band, and error is tracked against actuals after launch so you can see when a model degrades.
At minimum, we need 6-12 months of historical ad spend data with associated conversion events. The more data dimensions available — CRM revenue data, customer cohort information, multi-touch attribution paths — the more powerful the predictive models become. We connect via native API integrations to Google Ads, Meta, LinkedIn, TikTok, and major CRM platforms, so data ingestion is automated.
The system integrates directly through each platform's API — reading performance data in real-time and writing budget adjustments, bid modifications, and creative pausing commands back to the platforms. It works alongside existing campaign structures, not replacing them. You maintain full control and visibility while the AI handles the continuous optimisation layer that human media buyers cannot sustain at machine speed.
Yes. The budget optimisation engine recalculates allocation recommendations every 15-60 minutes based on incoming performance data. It can autonomously shift spend between campaigns, ad sets, and channels — or operate in advisory mode where it surfaces recommendations for your team to approve. The system respects configurable guardrails: minimum spend floors, maximum daily shifts, and channel allocation constraints you define.
Cross-channel attribution determines which combination of marketing touchpoints actually caused a conversion, rather than crediting only the last click. Our Markov Chain models simulate removing each touchpoint from real conversion paths to measure its true incremental contribution — so budget flows to the channels that genuinely drive revenue, not the ones that merely capture credit.
It works, with adjusted expectations. Smaller accounts generate fewer conversion events, so models take longer to stabilise and carry wider confidence intervals at first. The honest test is arithmetic rather than a threshold we can quote: model the saving you would need to cover the build against your current wasted spend, and if it does not clear comfortably the answer is to wait until volume supports it. We will run that calculation with you before you commit.
Replace manual media buying with forecasts that carry error bars and guardrails you control.
30-minute technical call · no deck
What we built, what stage it is at, and a link to the live thing wherever there is one.
yourOS is an operating system with an AI at its heart. You talk to it like a person, out loud or typed. It makes your calls, plans your days and works on your projects with a crew of agents, on your own private server. Our own product, built from the first commit; the waitlist is open.
Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.
Brand, website, prototype, apps on five platforms, the AI backend and the algorithm-intelligence pipeline for Podswap, a social growth engine that runs without a human in the loop.
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.
Live at domainbooth.com for DomainBooth, a premium domain brokerage: a new animated mark, every page redesigned and rebuilt with designed mobile layouts, and a custom CMS for its sales record, featured names, press and enquiries.
Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
