✓ Build at 10x speed with Claude or ChatGPT
✓ Maximize runtime AI reliability
Why enterprise AI automation stalls
Enterprises run many agentic AI pilots, but few reach production. There are two reasons, and Thunk.AI solves both.
01
AI reliability
LLMs and agents are probabilistic. “Usually right” isn’t good enough for business-critical work, and better models alone don’t fix it. Thunk.AI’s application model and runtime make agentic workflows reliable.
02
Human reliability
Builders are scarce, and designing, testing, deploying and monitoring each automation is slow, manual work. So enterprises rent armies of expensive forward-deployed engineers. Thunk.AI’s Forward Deployed Agent does that work instead.
In our benchmarks, the same models score higher and closer together on Thunk.AI than on their own.
99%
AI Reliability for Drug Safety Case Intake
See how three AI models reached a 97.9% to 99.8% Weighted Score on Thunk.AI, and required every expedited regulatory report, in our October 2026 drug safety benchmark.
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AI Reliability for IT Service Management
See how we achieved an industry-leading 99% AI Reliability score in our February 2026 benchmark implementation.
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AI Reliability for Enterprise Document Workflows
A closer look at our September 2025 benchmark implementation, achieving an industry-leading 97.3% AI Fidelity score.
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Meet your "Forward Deployed Agent"
Process owners describe the work. The Forward Deployed Agent, running in Claude or ChatGPT, builds it. The platform runs it reliably. Each request below is weeks of specialist work, done in minutes.
Build
“Here is a SOP document. Create the thunks needed to automate it, then test and validate them.”
Benchmark
“Build a benchmark set of thunks from this SOP. Generate the test data, compare three frontier LLMs, and write a full report.”
Operate
“Every morning, troubleshoot the last five errors on thunk X and tell me what you fixed.”
Evolve
“Here is user feedback on thunk X. Produce a new version that addresses it, and explain what you did for each issue.”
Optimize
“Cut the token cost of thunk X significantly. Show me the result on a copy.”
See how Thunk.AI’s reliable, no-code, no-FDE platform compares to other approaches to enterprise automation.
vs FDE-led deployments →
Your process owners and a Forward Deployed Agent build the workflows, instead of a vendor’s engineers.
vs Custom development →
Reliable AI workflows in weeks, built by your process owners and the agent instead of a development team.
vs Microsoft Copilot →
Reliable, auditable workflow automation, not just AI chat assistance.
vs OpenAI Workspace Agents →
Enterprise reliability, human-in-the-loop control, and no-code authoring by process owners.
vs LangGraph →
No-code agentic workflows with reliability built in, and no framework code to maintain.
vs n8n →
AI-native agents that reason and check their own work, beyond rule-based automation steps.