Flev DevOps pilot
Best first offer: diagnose one failing CI, deploy, Kubernetes, or incident path and return evidence plus a reusable runbook.
Discuss a pilot
Buyer View
Choose a paid pilot, not a broad platform conversation.
The buyer should immediately understand what they send, what they receive, how success is judged, and what becomes repeatable if the pilot works.
Benchmark proof sprint
Compare the same model across DeepAgents and Flev control modes so buyers see measured lift before a bigger rollout.
Discuss a pilotSample output
Preview the diagnosis brief, evidence table, runbook patch, and approval boundary before sending a real failure.
Discuss a pilotAfter the pilot
If the first workflow proves useful, package the repeatable pattern into a recurring Flev workspace or customer-facing workflow.
Discuss a pilot
Product Names
The names describe the boundary each product owns.
Better tool calls: validate, repair, or stop tool actions before they become workflow failures.
A stable harness for agents: keep sessions, approvals, evidence, and operator control attached to the run.
Flow Evolution: turn one useful workflow into a repeatable product surface that can keep evolving.
Flev Proof Surfaces
The agent should produce work people can inspect, remember, measure, and improve.
Flev gives operators places to see evidence, approvals, governed memory, benchmark comparisons, context changes, and reusable artifacts during and after a run.
CLI run
Start a real workspace task from one command and persist the run.
Review Tree
Review steps, checks, approvals, and artifacts without reading a long transcript.
Governed Memory
Review, approve, scope, and delete reusable long-term context instead of letting hidden state accumulate.
Benchmark Studio
Compare the same model across DeepAgents and Flev control modes with durable BFCL or BCFL reports.
Engineering Detail
Inspect the lower-level path when engineering needs to debug exactly what happened.
Chat and Embed
Turn the workflow into an experience customers or internal teams can use.
Model Cost And Privacy
Use frontier models only where they matter.
Flev workflows can route routine, structured steps to local or private small models while keeping stronger models available for complex reasoning.
Routine steps
Classification, extraction, validation, routing, and repair do not always need the most expensive model in the stack.
Reviewable routing
Teams should be able to see which model handled which step, why fallback exists, and who can approve changes.
Small-model ready
Better Call evidence shows tool-call accuracy improving from 73.4% to 83.8% on 3,625 granite4.1:3b BFCL v4 cases.
Business Outcomes
The goal is not an agent demo. The goal is a measurable operating capability.
Acquire better leads
Use a sharper offer architecture, workflow proof, and pilot package so prospects understand exactly what they can buy.
Shorten sales cycles
Show side-by-side benchmark reports, actual workflow traces, tool calls, risk controls, and approvals instead of broad AI claims.
Protect margin
Route routine steps to local or private small models where appropriate, and reserve expensive models for higher-value reasoning.
Reduce launch risk
Use benchmark comparisons and evidence trails to prove which runtime controls improve pass rate before a buyer commits to scale.
Engagement Models
Start the intake
Have one failing CI, deploy, Kubernetes, or incident path worth fixing first?
Send the failing path and what a useful diagnosis would change. We will help scope the smallest credible Flev DevOps pilot before expanding into broader workflow products.
User Guides
