Routine steps
Classification, extraction, validation, routing, and repair do not always need the most expensive model in the stack.
Flev Offers
Buy a workflow outcome first. Inspect the platform after the offer is clear.
The first purchasable offer is Flev DevOps: send one failing CI, deployment, Kubernetes, or incident path and get back a diagnosis, evidence trail, runbook, and productization path. Other Flev workflows should follow the same narrow, evidence-heavy pattern.
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.
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.
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.
Flev DevOps pilot
Best first offer: diagnose one failing CI, deploy, Kubernetes, or incident path and return evidence plus a reusable runbook.
Discuss a pilotBenchmark 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
Engineering Proof
Why the offer can be trusted after the buyer understands it.
Flev
The user-facing workspace: run the workflow, inspect evidence, review context, embed the experience, and package what should repeat.
Stable Harness
The operating boundary: sessions, approvals, evidence, memory lifecycle, protocol access, and delivery context stay attached to the run.
Better Call
The execution guard: malformed or unsafe tool actions are validated, repaired only when allowed, or blocked before users see failure.
Model routing
The cost and privacy boundary: routine steps can run on local, private, or smaller models while complex reasoning keeps access to frontier models.
CLI run, Studio tree, raw trace, memory review, chat, embed, and workspace delivery surfaces.
Session, evidence, approval, provider, memory, and protocol boundaries stay attached to one run.
Same-model comparisons show how repair, review, memory, HITL, and runtime controls change pass rate, tool-call validity, and latency.
Local, private, OpenAI-compatible, or frontier models can be assigned by workflow step instead of hidden in code.
BFCL v4 evidence: tool-call accuracy moved from 73.4% to 83.8% on 3,625 granite4.1:3b cases.
What The Buyer Should See
Make the proof concrete before asking for a bigger platform conversation.
Each offer should leave artifacts a buyer can forward to an operator, engineer, or budget owner.
Evidence table
What was checked, what was confirmed, what remains unknown, and which source supports each claim.
Benchmark report
Same-model runtime comparison showing pass rate, valid tool calls, repair success, latency, and which control mode produced lift.
Runbook or next-run rule
What the team should do next time the same failure or workflow appears.
Approval boundary
Which actions were read-only, which actions required review, and which actions should never run automatically.
Productization path
Whether the workflow should become a recurring Flev workspace, customer-facing feature, or one-off consulting output.