Inferviqo
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Inferviqo

Enterprise AI agents that cost less — with control in the path

Viqo in your repo; Inferviqo hosts the model and enforces policy on every request.

We optimize inference for agent workloads and enforce policy where requests actually run. Target pricing: $2–4 per 1M tokens vs typical frontier API blends around $10–20.

Why this mattersTalk to us
Why this matters

Agent stacks stall on cost — or on security review

Multi-step agents burn expensive output tokens. At the same time, sensitive data and tool actions need real controls before anything ships. Most teams bolt one or the other on later. We put both where the request already runs: the inference path.

  1. Bills climb with every step

    Frontier APIs often land around $10–20 per million tokens on agent-heavy mixes. Cost scales with workflows, users, and retries — not just prompts.

  2. Launch waits on control

    Code, customer data, and autonomous tools need access control, policy, audit, and residency. A separate compliance gateway after the model is usually too late.

What we build

An inference engine and an agent layer — one data flywheel

Inferviqo serves models efficiently for your traffic. Viqo is how teams run agents on open-weight models tuned to their codebase. The same customer signals improve both draft-model acceptance and token use over time.

Not a wrapper around a frontier API. Open-weight models, workload-tuned serving, and policy inside the inference path — not a compliance proxy after the fact.

Inferviqo engine

  • Serving built for agentsContinuous batching, tenant-isolated caching, and speculative decoding aimed at real multi-step workloads.
  • Fit to your trafficDraft models can specialize to how your agents actually call the API — so speedups compound with use.
  • Policy hooks in-pathEnforce rules modeled on your company policies — and use the same traces to monitor, debug, and probe for vulnerabilities.

Viqo agents

  • Open weights on InferviqoBases like Kimi K3 and GLM 5.2 are served on Inferviqo infrastructure with tenant isolation — your data stays in your boundary.
  • Tuned to how you shipOptional SFT on your codebase and policies to cut retries and keep quality on your tasks.
  • Use it where you workPoint the Viqo CLI or IDE agent at Inferviqo — no separate GPU ops for your team.
How you buy (targets): usage API around $2–4/MTok, plus a one-time SFT onboarding fee when you specialize a model — usage stays on the same rate card.
How it works

Drop-in inference for agents — specialized to your workload

A compatible API endpoint and an agent layer in your repo. Open-weight models served on infrastructure tuned for multi-step agent traffic — with policy enforcement in the same execution path, not bolted on after.

  1. 01

    Install Viqo

    CLI or IDE agent where your team already ships. Same workflow — no GPU ops on your side.

  2. 02

    Route to Inferviqo

    Point your endpoint at Inferviqo. OpenAI-compatible API, tenant-isolated serving on Kimi K3 and GLM 5.2.

  3. 03

    Specialize over time

    Optional SFT and draft models fit to your traces and policies. Throughput and token use compound as traffic grows.

Platform stackone execution path
ViqoAgent layer

CLI · IDE · multi-step tool loops

Inferviqo APIServing

OpenAI-compatible · tenant isolation · rate cards

Inference engineCore

Speculative decoding · prefix cache · continuous batching

  • workload-tuned draft models
  • policy hooks in-path
  • full agent trace
Model baseOpen weights

Kimi K3 · GLM 5.2 · per-tenant LoRA adapters

Control in the path

Policy shaped by your company — enforced where agents run

Compliance isn't a generic filter pack. We model it on your organizational policies, controls, and data-handling rules, then apply that judgment across prompt, retrieval, tools, and output. The same path lets you monitor live behavior, debug decisions, and experiment to find gaps before they become incidents.

  1. Trained on your policies

    Encode how your company actually handles data, tools, and escalation — not a one-size checklist that ignores your context.

  2. Enforce in the execution path

    Block, redact, route, or log mid-workflow — including before a tool call runs — with the full agent context in view.

  3. Monitor and debug

    Every allow/deny leaves a trail: what context was seen, what rule fired, and why. Use it to audit production agents and chase down surprising behavior.

  4. Experiment for vulnerabilities

    Probe policies safely — try adversarial prompts, risky tool plans, and edge-case data flows — to find holes before customers or attackers do.

Example: an agent is about to send customer PII to an external API. Policy modeled on your egress rules blocks the call, redacts the context, and logs the decision — then your team can replay that trace while tightening the policy.
Who it's for

Built for teams already feeling the bill — or the review

If you're rolling out agents inside a real product org, you usually hit one of two walls first.

  1. You own the AI bill

    VP Engineering or Head of AI at Series A–C SaaS and fintech — especially when frontier API spend is climbing past roughly $30K/month.

  2. Security is the gate

    A launch is blocked until access control, audit, and data handling are credible — not promised in a slide.

Typical path: prove cost and control on your current agent stack, then deepen with workload-specific models when you're ready.
Get in touch

Tell us what you're building.

High token spend, a blocked security review, or early design-partner interest. Email hello@inferviqo.com or use the form.

Submissions open in your email app, addressed to hello@inferviqo.com.