AI Agent Execution Platform · ROI in 8–12 weeks

The AI Agent Execution Platform for Enterprise Operations

Move beyond failed pilots. LlamaSee gives Chief AI Officers the infrastructure to deploy a digital workforce, orchestrate agentic workflows, and govern AI in production — with measurable ROI from day one.

8–12
weeks to first ROI
99%
transaction cost reduction
1000x
throughput increase
The enterprise AI gap

AI investment is surging.
ROI is not.

The challenge isn't access to models or budget. It's the absence of an execution layer that can run AI in production at enterprise scale.

80%

of Fortune 500 companies have dedicated Chief AI Officers — the mandate to transform is real and funded.

5%

of enterprise AI projects yield positive ROI. The gap isn't investment or models — it's the execution layer.

"Just like Salesforce became the system of record for Chief Marketing Officers, and Workday for Chief HR Officers — LlamaSee is becoming the system of record for Chief AI Officers."
The platform

Built for production.
Not prototypes.

LlamaSee helps enterprises create AI agents, run them as a governed digital workforce, monitor outcomes, explain decisions, and audit every step.

🤖

Agent workforce

Create task-specific AI workers for operations, analytics, review, and workflow execution. Manage them with roles, responsibilities, versions, status, and review history.

⚙️

Decision intelligence

Turn business signals into explanations, root-cause analysis, recommended actions, and decision-ready operating reviews.

🔀

Governed execution

Monitor agent activity, cost, quality, privacy checks, errors, human escalations, and audit evidence from one operating layer.

📡

Audit backbone

Trace recommendations back to data, signal, business context, assumptions, execution stages, provider choices, model versions, and outcomes.

📈

Fast integration

Developers and business analysts connect workflows quickly with an SDK and business context layer while governance stays centralized.

🏗️

Separated roles

AI administrators own governance and reviews. Developers own integrations and extensions. Business analysts contribute operating context.

Platform trailer

How LlamaSee
runs AI work.

A public-safe tour of the four core motions: build AI workers, monitor their outcomes, explain decisions, and govern execution.

Real results

Not pilots.
Production at scale.

99%
transaction cost reduction in 2 days of agent iteration
1000%
throughput increase — from thousands to 800K products
100K+
agent calls in production from first design partner
Live
marketing control tower guiding weekly business reviews
Process Automation Catalog Intelligence

Catalog classification agent: $2.00 → $0.02

Manual catalog operations cost $2 per transaction and took 5 minutes each — prohibiting growth at scale. LlamaSee deployed an AI catalog agent and iterated it over 2 days across multiple LLM models.

Result: 99% cost reduction, 1,000% throughput increase, and 800,000 products now classified continuously in production.

Line chart showing transaction cost per 1K dropping from $2,000 (manual baseline) to $2 (optimized output structure) across 5 iterations in 2 days
Two core accelerators

Purpose-built for operational AI

Both products have super-user configuration tools to expedite implementation — no heavy engineering required.

Product 02

Operations control tower

Connect data signals, business strategy, and AI reasoning into one decision system. Give executives visibility from signal to action.

  • Data → signal → business strategy traceability
  • LLM-powered root cause analysis
  • KPI decomposition and portfolio analysis
  • Cross-functional execution visibility
  • Weekly business review automation
For partners

Build an AI practice
on LlamaSee

LlamaSee changes the consulting engagement model. Business strategy consultants focus on transformation — LlamaSee handles the AI execution layer. SI partners and consulting firms can build repeatable, scalable AI practices.

SI partner model

Team up with LlamaSee in Phase I engagements. Your team drives business strategy and process mapping — LlamaSee provides AI infrastructure, model orchestration, and governance. Revenue share on subscriptions.

Revenue share Co-sell Joint delivery

Consulting practice builder

From a single AI use case to a scalable industry practice. Start with one repetitive task (ROI in 8–12 weeks), expand to workflow templates and knowledge bases, then scale a full vertical practice with reusable solutions.

Industry accelerators Reusable templates Faster deployments
Phase I is free
We conduct a free 1-day AI strategy workshop to identify use cases, define KPIs, and build your AI roadmap — at no cost.
Partner with us
Private by design

Privacy is part of
the system design.

LlamaSee is designed for production environments where business context, prompts, outputs, and decisions must remain governed, reviewable, and protected.

Audit is the backbone of agent operations.

We position agents as controlled digital workers, not black boxes. Teams can create different task agents, monitor their work, and review evidence before high-impact actions move forward.

Customer data stays protected Public demos use sanitized, non-real data. Production deployments are scoped to customer-approved data sources, access controls, and environments.
Policy gates before action Agent workflows can include PII, security, compliance, and domain rules before recommendations become operational decisions.
Traceable audit backbone Agent outputs are designed to be reviewable with evidence, confidence, assumptions, cost, quality, and escalation paths instead of opaque automation.
Separate admin and developer roles AI administrators manage agents and governance. Developers connect systems through the SDK without taking over operational policy.
Leadership & trust

Built by operators
who scaled AI

TJ
Tommy Jiang
CEO & Founder
Former GM, AWS Supply Chain. Former VP Data, AI & Robotics, Moderna. Founder of Supply Chain OS (successful exit). 30 years in enterprise technology.
MM
Mark Mao
CTO & Co-founder
Entrepreneur, Founder of Whim-Tech. Expert in business application and DevOps. Full-stack and AI systems architect for secure, scalable enterprise platforms.
OB
Dr. Oliver Ban
Chief Business Dev.
PhD, MBA. Strategic advisor supporting enterprise AI transformation and operational scaling across global markets.
SH
Dr. Su Huang
Principal Scientist
Deep technical and business leadership experience in AI and enterprise operations systems.
Advisory team — proven enterprise leaders
Jim Miller
ex-CTO, Wayfair · ex-VP, Google
Sr. Advisor BCG. Active board director for multiple public companies.
Steve Block
Public Sector leader
AWS and Amazon public sector enterprise.
Start here

Ready to see ROI
in 8–12 weeks?

Start with a free Phase I assessment to identify where agents and workflows can create immediate enterprise value.

  • Free 1-day AI strategy workshop — no cost, no commitment
  • Identify your highest-ROI automation candidates
  • Define KPIs and prioritize use cases
  • Fixed-cost 8–12 week implementation with your team
  • SI partners welcome — co-delivery model available
Start your free assessment
We'll reach out within one business day to schedule your workshop.