Teenva AI & Digital Ventures builds ad optimization AI for performance marketing teams, agencies, ecommerce brands, and travel advertisers from Bangalore, India. Teenva ingests Google Ads, Meta Ads, and analytics conversion data—then recommends or applies bid adjustments, budget pacing, creative test winners, and anomaly alerts against your ROAS, CPA, or lead-cost targets—with spend caps, human approval, and audit logs so automation never silently burns budget.
Platform “Smart” campaigns are opaque; manual spreadsheet bid changes lag intraday auctions. Teenva delivers explainable recommendations, multi-armed bandit creative tests, and optional API write-back—paired with AI content generation for new variants and ecommerce conversion tracking on your store.
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Ad optimization AI—bid and budget ML, creative tests, and spend alerts with approval gates.
Hub: AI solutions · Domain: Marketing
What we optimize
| Lever | AI role | Human gate (default) |
|---|---|---|
| Keyword / ad group bids | Intraday or daily bid multipliers from conversion model | Approve changes > X% |
| Campaign budgets | Pacing to hit monthly cap without front-loading | Alert + suggest |
| Audience exclusions | Flag low-quality segments, overlap waste | Review list |
| Creative rotation | Bandit test headlines/images; promote winner | Auto on test campaigns only (scoped) |
| Dayparting (scoped) | Hour-of-week bid curves from historical CPA | Suggest schedule |
| Geo / device (scoped) | Bid modifiers by converting regions | Threshold rules |
| Landing page mismatch (scoped) | Alert when ad promise ≠ landing page bounce | Alert when ad promise ≠ landing page bounce |
Optimization uses structured ML on spend and conversion time series—LLMs optional for creative text variants only.
Data we connect
| Source | Signals |
|---|---|
| Google Ads API (scoped) | Impressions, clicks, cost, conversions, quality score |
| Meta Marketing API (scoped) | Spend, CPA, ROAS, creative IDs |
| GA4 / server-side (scoped) | Assisted conversions, revenue, funnel steps |
| CRM / offline (scoped) | SQL or won deal attributed to campaign |
| Ecommerce platform (scoped) | Order value from Shopify or custom checkout |
| First-party pixel | Events via API integration services |
Features we implement
Recommendation feed
“Raise bid 12% on Brand-Exact—CPA 18% below target”Auto-apply mode (scoped)
Within min/max bounds you set per campaignBudget pacing model
Forecast month-end spend vs planCreative experiment engine
Multivariate or bandit; sync winners to platformCopy variant hook
Pull drafts from content generationAnomaly detection
Spend spike, CTR collapse, conversion tracking breakSlack / email alerts
With deep link to ad platform (scoped)Holdout reporting
AI-managed vs human-only campaign cohortsMulti-account (scoped)
Agency rollup dashboardsAudit trail
Every bid/budget change: who, when, model version
Copy variants from AI content generation.
Architecture
Ad platform APIs + analytics + ecommerce orders
ETL to warehouse (hourly / daily)
Feature store (lagged conversions, seasonality, promo calendar)
Optimization models → bid/budget/creative recommendations
Policy engine (caps, brand campaigns frozen, min spend)
Approval UI OR auto-apply within bounds
Write-back to Google/Meta APIs + log outcomes
Deployment — Your cloud; ad tokens never sent to public LLM for bidding decisions
Use cases by advertiser type
| Advertiser | Optimization focus |
|---|---|
| D2C ecommerce | ROAS by SKU; product recommendation landing pages |
| B2B SaaS | Cost per SQL; sync with lead scoring |
| Travel / OTA (scoped) | Route seasonality; travel technology campaigns |
| Local services (India) | Geo radius, call conversions, Hindi ad variants |
| Agency (scoped) | Multi-client guardrails and white-label reporting |
| App install (scoped) | CPI/CPA with in-app event optimization |
Guardrails & governance
| Control | Why |
|---|---|
| Spend ceiling | Hard stop if daily spend > N% above plan |
| Brand campaign lock | No auto bid on trademark campaigns |
| Learning period respect | Don't churn bids during platform learning phase (scoped rules) |
| Conversion lag window | Wait for attributed conversions before judging CPA |
| Manual override | Media buyer always wins; model pauses on conflict |
| Documentation | Change log for finance and agency clients |
Automation without caps is a budget fire.
Every auto-apply path ships with ceilings, approval thresholds, and anomaly kill switches.
Ad optimization vs sibling marketing AI
| Product | Focus |
|---|---|
| Ad optimization AI | Paid media bids, budgets, creative tests |
| AI content generation | Write copy assets |
| Email personalization AI | Owned email 1:1 fields |
| Social media AI assistant | Organic social cadence and replies |
| AI campaign manager | CRM nurture sequences—not auction bidding |
Why Teenva AI
ML-first paid media
Not a chat UI pasted on Google AdsFull funnel
Web, ecommerce checkout, and ads in one teamIndia + global
INR budgets, festival seasonality, export brandsTransparent math
Recommendations show why (CPA vs target, volume)Creative loop
Connect testing to content studio
Delivery process
Discovery
Platforms, KPI (ROAS/CPA/lead), monthly spend, risk tolerance
Tracking audit
Conversion pixels, offline imports, revenue match
Data pipeline
Warehouse + daily/hourly sync
Baseline report
Current waste segments and creative fatigue
Recommendation-only pilot
2–4 weeks human applies suggestions
Guardrailed auto-apply (scoped)
Bounded bid/budget changes
Creative tests
Bandit with content generation feed
Holdout analysis
Lift vs control accounts
Runbook
Escalation when anomalies fire
Frequently asked questions
No—augments buyers with data and guardrailed automation; strategy stays human.
Google Ads and Meta most common; others via API (scoped).
Default recommend-only; auto-apply within bounds you configure (scoped).
Rough guide: ₹2–5L+/month or equivalent—below that, rules-based may suffice.
No for bidding—ML on metrics; LLM only for creative text variants (optional).
- Festival and route seasonality for travel advertisers (scoped).
- Offline SQL conversion import improves B2B CPA models via lead scoring (scoped).
Multi-tenant dashboards and client guardrails (scoped).
8–12 weeks—tracking audit + recommend pilot; auto-apply adds 2–4 weeks.
Explore
Build your ad optimization system
Build your AI solution with us—bid and budget ML, creative tests, and spend alerts with approval gates.
Build your AI solution with us
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