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Auviel
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AI agent development

Agents that do work, not demos that chat.

We design and build AI agents with clear tools, permissions, and evaluation so they can act inside your product or operations stack safely.

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Partners who put agents into production, not pilot purgatory. See what they say

Overview

“Agent” has become a catch-all for anything with a language model. In production, an agent is software that plans, calls tools, respects boundaries, and fails predictably when it should not act.

Auviel builds agents as product features and internal copilots: CRM automation in Flowforce, workflow assistants for ops teams, and customer facing experiences where the agent is one component of a larger system.

We focus on tool design, retrieval quality, evals, and human in the loop patterns, the parts that determine whether an agent survives real users and messy data.

What we see holding teams back

  • Agents without tools or context

    Generic chat wrappers cannot fetch live data, update records, or complete multi-step tasks. Users ask once, get a plausible answer, and go back to manual work.

  • Unsafe or unbounded actions

    Agents that can call APIs without scoping, rate limits, or approval steps create risk. One bad tool call can corrupt data or send the wrong message to a customer.

  • No way to measure quality

    Without eval sets and production metrics, teams cannot tell if a prompt change helped or hurt. Releases become guesswork.

How we work

  1. 01

    Tool-first architecture

    We define explicit tools (read, write, search, notify) with schemas and permissions. The model orchestrates; your systems stay the source of truth.

  2. 02

    Guardrails by default

    Confirmation steps for high-impact actions, structured outputs, timeout and retry policies, and logging that support and engineering can audit.

  3. 03

    Evals before scale

    We build representative test cases from your domain, run them in CI, and track regression as models and prompts change.

Results from agents and AI we ship.

  • Satisfaction

    0%

    Client satisfaction across Auviel engagements.

  • Shipped

    0+

    Projects delivered from concept to production.

  • Ops lift

    0x

    Throughput improvement where agents automate logistics ops.

  • Reach

    0k

    Monthly visitors on Rareplus, a storefront we engineered.

Working with Auviel made a real difference for our clinic. They understood how we operate, and the AI tools, apps, and digital systems they built have made day-to-day work markedly easier so we can focus more on patients.

Ilodibe Onoja

MD, Balija Eye Care

Balija Eye Care

Proof from the field

Case studies and products we have shipped for teams like yours.

Frequently asked questions

What kinds of agents do you build?

Internal ops agents (routing, research, drafting), product embedded assistants (onboarding, support, configuration), and vertical workflows (sales, logistics, healthcare adjacent tools) where tool access is tightly scoped.

Which models and frameworks do you use?

We choose models and orchestration based on latency, cost, and capability needs. Commercial APIs and open weights where appropriate. The architecture is portable; we avoid locking you to a single vendor when it hurts you.

Can agents connect to our CRM or database?

Yes. Agents are most valuable when they act on live systems. We design integrations with least-privilege access and clear audit trails.

How do you handle hallucinations?

Grounding via retrieval, structured tool outputs, confidence thresholds, and human review for high-stakes steps. We treat “I don’t know” as a valid agent response.

Do you offer ongoing agent operations?

We can hand off runbooks and monitoring to your team or stay on retainer for eval updates, model upgrades, and new tool surfaces as the product grows.

Scope an agent that fits your product.

Tell us what work you want automated and what must never happen without a human. We will propose a build path.

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