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.
of enterprise GenAI is actually in production. That is the number we move.
of companies missed their AI savings targets, landing under 10%. The tech worked; the value did not arrive.
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.
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.
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.
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.
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.
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 usAgentic 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.
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.
less manual effort per invoice, with end-to-end cycle time down from days to hours. Validated against a baseline before scaling.
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 usAnd 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
opportunities prioritized by ROI, 5–15 workflows live, up to 20 people trained - capability that stays after we leave.
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 usFramework
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.
Start the conversation