AI Automation Engineer

Avanai - Sovereign agentic operations Skip to content

95% of enterprise AI never reaches production.

We build the 5%.

Avanai is the forward-deployed engineering firm for agentic AI. We build sovereign agent layers that run enterprise operations - in production, on infrastructure you own. Live in weeks, not quarters.

Book a working sessionSee real examples

Hundreds of agents live in production

• Thousands of users served
• 2,000+ people enabled
• Seven-figure vendor spend avoided

5%

of enterprise GenAI is actually in production. That is the number we move.

40%

of companies missed their AI savings targets, landing under 10%. The tech worked; the value did not arrive.

7%

run fully autonomous agents in production. Most business cases assumed far more.

Industry data

• Bain Automation & AI Pathfinder Survey 2026 (n=951)

What we do

To make AI a real capability, you need control, delivery, and adoption at the same time.

These are the three executive conversations leaders use to avoid vendor lock-in, shadow agents, and pilots that never ship. Start with the constraint that is urgent, then sequence the rest into an operating model.

Orchestration & governance

Sovereign AI

A sovereign orchestration layer above your SaaS estate. Vendor-neutral, governed, and owned by you, so capability compounds instead of locking in by default.

Use case

a global manufacturer declined a seven-figure ITSM AI upsell and kept the capability in-house.

See the use case

Delivery

Agentic Operations

Production agents that do real work. We redesign workflows for agents as primary actors, then ship governed automations that are observable and measured.

Use case

a global life-science group runs a governed automation engine across thousands of users.

See the use case

Adoption

AI-Native Teams

AI as a co-worker, not a chatbot. We help teams change how work gets done, close the internal adoption gap, and ship improvements without waiting on IT.

Use case

from tool access to changed behavior, fast enough to show stakeholders, then transferred to the team to own.

See the use case

Sovereign AI

• Orchestration & governance

Who owns the intelligence running across your estate?

Every major SaaS vendor is building its own AI layer inside its own walls. Left on the default path, you end up with disconnected agent stacks, no shared intelligence, and switching costs that compound. We help enterprises design and build a sovereign orchestration layer above the SaaS estate-vendor-neutral, open-protocol, and run on infrastructure the client owns.

best-fit modelsovereign / EU-hosted optionGDPR & EU AI Act aware

Use case

• Global manufacturer

Replacing a seven-figure ITSM AI upsell

Situation

Multiple SaaS vendors layering AI onto the same systems of record - each with its own bill, each with its own lock-in path. The ITSM platform proposed a seven-figure AI feature set.

What we did

Built the capability on top of the existing system of record. No rip-and-replace. The platform stays; the AI layer becomes something the client owns.

Outcome

The upsell declined. The capability owned. Model choice and roadmap kept in the client's hands.

Why now

The lock-in is happening by default.

Tools are accumulating faster than architecture. Renewals are where AI becomes premium add-ons, priced per seat, per token, per feature. The cheapest moment to own your capability is before the dependency deepens.

Source

Bain Automation & AI Pathfinder Survey 2026

What if you don't

You pay twice and own nothing.

One vendor's roadmap, one vendor's pricing, one vendor's model choice. When the "hot" model changes, you renegotiate. The capability never becomes yours.

Where is your AI bill heading at your next renewal?

Pressure-test it with us

Agentic Operations

• Delivery

Agents that do the work, not slides about agents.

An agent bolted onto a broken process is a faster broken process. We start from the outcome the business actually needs, redesign the workflow to fit agents, then ship it production-grade-governed, observable, and measured from day one.

orchestrationreasoning layeryour systems of record, untouched

Use case

• Global life-science group

A governed, scalable automation engine

Situation

Automation is happening in pockets, but the operating model to run it safely is missing. Ownership is unclear, governance is inconsistent, and value is hard to prove.

What we did

Stood up the platform and an agent factory, embedded engineers who also do the business thinking, redesigned processes, and put governance and value tracking around every workflow.

Outcome

Scaled across thousands of users with hundreds of workflows live, value tracked against a baseline.

40–70%

AP / invoice copilot

less manual effort per invoice, with end-to-end cycle time down from days to hours. Validated against a baseline before scaling.

92%

Market benchmark

time saved on a single bounded workflow when an agent replaces manual assembly. That is the size of the prize per process.

Why now

Every function is already building.

Citizen development is happening with or without you. Without a layer over it you get silos, governance gaps, and a cost line nobody can explain.

Source

Bain Automation & AI Pathfinder Survey 2026

What if you don't

Chaos at scale.

Dozens of shadow agents on processes nobody re-examined, running up variable costs and creating audit gaps. The cleanup costs more than doing it right the first time.

Which process would you stop running if you could?

Map it with us

And we stay

Agent Reliability Engineering.

Agents in production need what production systems need - monitoring, evaluation, governance, and continuous improvement. We run your agent estate under SLA, so capability compounds instead of decaying.

AI-Native Teams

• Adoption

AI as a co-worker, not a chatbot in the corner.

Most enterprise adoption is shallow. People use AI for drafting, but the real gains-workflow redesign, agent delegation, AI-augmented decision-making-do not land without a new operating model. We help teams build the habits, workflows, and governance to work with AI reliably, and to ship improvements without waiting on long IT backlogs.

The journey

01Ideation

02Hackathons

03Product & capability training

04Prototyping

05Production apps

across the whole journey

• governance
• value & cost management
• people & org

automationprototypingbuilding

20–40

A foundation engagement delivers

opportunities prioritized by ROI, 5–15 workflows live, up to 20 people trained - capability that stays after we leave.

5×

The adoption gap

your best people already manage agents like coworkers. Most still paste into a chat window. Enablement closes that distance - at scale.

Why now

The internal gap is widening.

Your best people are already delegating work to agents. The rest are still pasting into a chat window. Left alone, that gap becomes the real productivity problem.

What if you don't

Tools without adoption.

Licenses bought, behavior unchanged. Prototypes never reach production, momentum dies in pilots, and the investment shows up as cost with no matching value.

How far apart are your best and average teams on AI?

Run a session with us

Framework

The Sovereign Agentic Orchestration Layer - our framework, proven in production.

Every engagement runs on our codified architecture

five design groups, a reuse doctrine, a value loop, and a phased path from first agent to full stack independence. Model-agnostic, open-protocol, runtime-agnostic - sovereignty at every layer.

Not a slide framework

the operating system behind hundreds of live workflows.

From what if to what works.

One conversation. We map where you are, where the value is, and what to do in the next few weeks.

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