Fig AI SystemsAgentic AI systems for real business workflows from prototype to production
We build end-to-end AI systems with agentic chat, orchestration, vector databases, clustering, and closed-loop evaluation for teams that need production outcomes.
Specialties we bring to complex AI builds
We focus on the system architecture around the model: data foundations, agent coordination, evaluation loops, and production workflows that keep improving after launch.
Vector databases and data clustering
We design retrieval, semantic search, and clustering systems that organize messy business data into usable context for agents, analysts, and customer-facing products.
Agentic chat and orchestration
We build agentic chat experiences and multi-agent workflows that plan, use tools, route work, and coordinate across APIs, documents, users, and internal systems.
Closed-loop testing and self-improvement
We create agentic test harnesses where agents test agents, simulate realistic user behavior, identify failures, and feed those results back into systems that improve over time.
Proof of outcomes
Examples of agentic systems, custom AI development, and workflow automation with measurable business impact.
Marketplace
Build-a-Dress
Launched a custom artificial intelligence web application for made-to-order fashion that helps shoppers design in minutes and routes orders to verified manufacturers, supporting over 2,000 monthly active users.
Outcomes
- Agentic chat assistant for conversational dress design with image generation.
- Agentic AI workflow automation for quoting that classifies dress complexity, weight, and other pricing signals.
- Manufacturer verification system and payout setup.
- Stripe and Wise integration for a global payment gateway.
- Active business with 2,000+ monthly active users.
View business
Education
Report Grader
Built a custom domain AI analysis system for education teams: a grading assistant that reviews school reports against national and international standards, reducing manual correction time while improving consistency and feedback quality.
Outcomes
- Large-scale agentic AI workflow automation with over 1,000 token-efficient LLM calls triggered per report analysis.
- Skill-enabled agents mapped directly to a domain-specific corpus for precise grading grounded in school review criteria.
- Evaluation patterns that close the loop between model output, review criteria, and system improvement.
- DOCX and PDF parsing with segmentation to channel parallel LLM calls for high concurrency.
- Human-in-the-loop report grading that enables inspector input where needed.
Tell us what your AI system needs to do
Share the workflow, data, agents, or evaluation loop you want to build. We will follow up with a practical next-step recommendation for an end-to-end AI system.
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