Most AI teams are spending 20-40% more than they need to on inference.
And when compliance asks "who approved that model decision?" — silence.
That's what we fix at https://t.co/lyXbKhrBfp.
Own, govern, and compound your AI. Without runaway cost or compliance risk.
30 minutes to measurable ROI. Or your first 5 workloads are free for 12 months.
→ https://t.co/lyXbKhrBfp
@fh7ww@jointhebridge Teams go POC → production and agent costs come in 3-5x over model. Gateways see requests, tracing sees traces, neither sees the run where retries happen.
I built the layer that does. Runs compound back into routing and fine-tuning, Hyderabad, 18yrs in enterprise
@sridharfyi Building https://t.co/TC5sl4mVM9 - Agent Operating Layer for Mid Enterprises so they can own and compound their AI usage, without need for a AI team. MVP built, 2 early pilots with design partners and one LOI from a SI partner. Pre-revenue.
@rajeshchitupe "When innovation meets the invoice" is the perfect framing. Cloud got FinOps; agents need the equivalent — spend attributed per agent and workflow, not a lump-sum token bill. Most enterprises are about to find they have zero visibility into where the money actually goes.
@Olivier__OG The green-dashboard problem is real: token usage looks healthy while nobody can tie a single agent run to a dollar of value or cost. ROI proof starts with attribution — per-workflow cost and outcome — not aggregate usage charts.
@doronkatz Reliability is an observability problem wearing a trench coat. You can't make an agent reliable if you can't see why it failed — which step, which tool call, which token blew the budget. The teams shipping reliable agents instrument first, then iterate.