AI that keeps working. Long after launch day.
AI Ops is the operate half of every Nexera engagement — the production-grade layer that comes with every Floki deployment, run by the same team that built it. Observability, evals in prod, guardrails, and cost engineering on infrastructure we own. Agents don't rot on our watch; they get measurably better each month.
What AI Ops is
The operate
half of the lifecycle.
Four disciplines that turn a shipped agent into a system that keeps earning its keep — the part most vendors treat as someone else's problem once the invoice clears.
Observability that speaks English
Traces, live scoring, drift detection, and dashboards that non-engineers can actually read. If a workflow gets slower, sloppier, or more expensive, you see it before runaway costs or quality issues start to hurt you.
Evals in production, not just staging
Evals run continuously against real agent traces — not a stale test set from launch week. Drift, regressions, and silent quality drops show up as failing scores before they hurt your business.
Cost & latency engineering
Model routing, caching, batching, and prompt discipline applied as an ongoing practice. Quality goes up, token usage goes down, and you get the receipts.
Guardrails that hold under load
Input validation, output policy, jailbreak defense, PII handling, and human-in-the-loop where it matters — tuned continuously as your traffic and threat surface evolve.
How we operate
The loop
that keeps
agents honest.
A tight loop from production reality back into the system — so agents get sharper with time, not sloppier.
Most AI systems don't fail loudly. They quietly drift — a few more retries, a few more hallucinations, a few more edge cases handled the wrong way. Our ops discipline is designed to catch that drift early, diagnose it precisely, and correct it before it compounds.
01
Instrument
We capture only the telemetry that matters — structured traces, not a blanket recording of everything. Instrumentation is auditable by default and scoped to making the system better.
02
Observe
Live evals, drift detection, cost and latency watchtowers. The system tells us when something is off, instead of us finding out from a customer.
03
Diagnose
Traces and eval failures point to the exact prompt, tool, or edge case that broke — so fixes target the real cause, not the symptom.
04
Improve
Fixes ship as new evals, prompt updates, tool changes, or fine-tunes — then re-verified end-to-end before rollout. Improvement is a loop, not a heroic push.
Why our ops is different
Four things
most vendors skip.
The industry default is a dashboard, a Slack channel, and a hope that quality holds. Our default is a team, a loop, and a proprietary framework unavailable anywhere else.
- 01
Built on Floki, not bolted on
Observability, evals, and safety are framework primitives on Floki — not third-party overlays trying to peer inside a black box. Our ops layer understands how your agents actually work because we designed the runtime it runs on.
- 02
Senior operators
The same team that built your agent runs it. No handoff to a rotating support desk, no ticket queue where context goes to die. The people diagnosing an issue are the ones who designed and built the system.
- 03
Isolated infrastructure
Your ops runs on infrastructure we own and operate. No noisy neighbors, no shared model quotas, no vendor incident report as your explanation for why the agent went sideways at 3pm on a Tuesday.
- 04
Continuous improvement
We're not paid to just keep the lights on — we're paid to make the Floki framework and your Floki fleet better every month. AI Ops that doesn't continuously improve isn't worth having.
The unit economics
AI that gets cheaper as it gets better.
Most AI systems drift upward in cost over time — more tokens, more retries, more model overreach, more expensive fallbacks nobody remembers turning on. Our ops discipline reverses that curve. Cheaper models where they're good enough, cached calls where they're safe, prompt engineering where it earns its keep.
(Yes, we've heard of AI model bills doubling in a month when nobody's watching.)
How we can work together
Three tiers.
Pick the one that fits.
Every tier includes observability, evals in prod, cost monitoring, guardrail maintenance, and incident response. The differences are coverage hours, response SLAs, and the cadence of continuous improvement.
Standard
Keep it running well.
Business-hours coverage with a monthly eval review, quarterly improvement cadence, and always-on cost & drift dashboards. For teams with a small Floki footprint that just needs to keep running well.
Fit
- You have a single agent or a small fleet
- Not mission-critical off-hours
- You want visibility without staffing a full ops team
Priority
For agents in the critical path.
Extended coverage with faster response SLAs, bi-weekly eval review, monthly improvement cadence, and on-call for critical incidents. For teams whose agents are in the customer-facing critical path.
Fit
- Customer-facing agents where quality is visible
- Revenue depends on uptime and behavior
- You need answers fast when something looks off — not in 1-2 weeks
Dedicated
An embedded AI ops team.
24/7 coverage, named operators, weekly improvement cadence, and roadmap co-ownership. Effectively an embedded AI ops team — for organizations operating fleets or platform-level agents at scale.
Fit
- Multiple interoperable agents across business units
- Regulated or high-stakes domains
- You need senior operators in the room, not a ticket queue
What clients say
In their
words.
“Our costs dropped 58% in the first quarter under Nexera's ops, and our eval scores went up. That combination isn't supposed to be possible.”
Placeholder Name · VP Engineering, Placeholder Co.
“We stopped finding out about agent issues from customers. That change alone paid for the engagement three times over.”
Placeholder Name · Head of Support, Placeholder Inc.
AI Ops isn't a subscription bolted onto a black box — it's how we run Floki agents, with the same team that built them, on infrastructure we built.
Keep exploring
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worth solving?
A 30-minute call is usually enough to tell whether we're the right team for it — and if we're not, we'll say so.