Parker AI
Parker AI — AI-Native Marketing Intelligence
Multi-agent platform that turns TikTok, Instagram, and Meta signal into shippable creative for high-spend DTC brands.
Timeline
06/2023 – Present
Stack size
12 technologies
Role
Founding AI Product Engineer
Company
Parker AI
Overview
Overview
Parker AI is an AI-native creative-strategy platform for brands spending up to $2M/month on Meta ads. It ingests signal from TikTok, Instagram, Facebook, Reddit, competitor sites, reviews, and Meta ad performance, embeds and serialises it for hybrid retrieval, and powers agents that generate hooks, scripts, angles, and full creative briefs.
- The product is Slack-first: strategists, brand teams, and operators get proactive reports, ad-performance digests, and creative recommendations where they already work. The dashboard on Next.js gives deeper drill-downs and the reusable idea bank.
Problem
Problem
High-spend Meta advertisers run out of creative ideas faster than they can ship them. Existing tools either summarise the past or generate generic content. Parker connects what brands and creators are doing now to what should be tested next.
Architecture
Architecture
AI & Orchestration
Data
Application
Infra
Integrations
Features
Features
- Multi-source ingestion pipeline (TikTok / Instagram / Facebook / Reddit / competitor sites / reviews) running on Temporal with retries and dead-letter queues.
- Hybrid retrieval across vector (Qdrant) and relational (Supabase Postgres) stores with semantic chunking and query-aware re-ranking.
- Agentic ideation system built on Mastra that proposes hooks, scripts, angles, and full briefs.
- Reusable idea bank with status tracking, tagging, and reuse across campaigns.
- Meta Marketing API integration with ads_management and pages_read_user_content, token rotation, and rate-limit-aware fetchers.
- Slack-first delivery: proactive alerts, weekly strategist reports, ad-performance digests.
- Eval harness with Langfuse — every prompt change ships with traces, cost, and quality metrics.
- Cost-aware model orchestration with prompt budgets, cache-friendly retrieval paths, and guardrails that keep AI quality stable while controlling spend.
AI systems
AI systems
- Mastra agents with planning, memory, tool-use, and human-in-the-loop checkpoints.
- Custom MCP-style tools for ads APIs, internal data, competitor research, and creative review.
- Embedding pipelines with serialisation conventions tuned for long-form social content.
- Proactive improvement systems — agents continuously monitor performance and surface recommendations.
Outcomes
Outcomes
- Powering creative strategy for brands at up to $2M/month Meta ad spend.
- Idea turnaround compressed from days to minutes.
- Improved token and workflow efficiency through eval-informed prompt tuning and caching decisions.
- Foundation for the rest of the engineering team to build on.
Technologies
Technologies
FAQ
FAQ
How does Parker AI ingest TikTok, Instagram, and Meta data?
Temporal workflows fan out platform-specific workers that authenticate, paginate, normalise, embed, and persist to both Postgres and Qdrant. Failures retry with backoff and dead-letter for review.
How are agents kept safe in production?
Tool calls are typed and traced via Langfuse, agents have budget caps and explicit memory scopes, and high-impact actions (like emails to clients) sit behind human-in-the-loop checkpoints.
More work