Q3 2026 · Diagnostic engagements open

AI native operations, custom built for your business.

AI Nativ helps businesses rebuild their operational layer for AI agent execution. We diagnose what's blocking AI from running in your business, redesign the parts that need to change, and ship one working pilot at a time.

Who this is forBusinesses ready to buy back time with smart operations
What we operate onAI strategy, operations, and delivery
What we shipHuman-in-the-loop agent execution
How we workDiagnose before we build
Thesis

Businesses are wiring AI tools into operations that were designed to be executed by humans. The failure sits in a seam that was never built for AI agents.

The method · Three phases

Three phases, sequenced so each one earns the next.

i

Diagnose

A defined investment that maps the operational layer, assesses AI readiness, identifies the highest-leverage pilot, and flags the redesign work that has to happen first. You leave with a decision-grade artifact whether or not we continue.

DeliverableOperational map, pilot recommendation, leadership readout
OutcomeDecision-grade artifact, standalone value
ii

Redesign

Rebuild the parts of the operation that block agent execution. This is where most programs skip steps and then fail. The mix of redesign versus AI-fication is set by the diagnosis itself, so the scope follows the evidence.

ScopeSet by Phase I
OutputOperational spec ready for agent build
iii

Pilot, then scale

One agent-executable workflow, shipped and measured. Scale to adjacent workflows after the pilot earns it. Parallel rollouts and platform bets wait until evidence is in.

UnitOne pilot, measured before scale
DecisionScale gate after pilot evidence
The practice · Who we work with

Businesses ready to buy back time with smarter operations.

We work with owner-led and founder-run businesses where operations are still designed around humans making decisions. If your business runs on tribal knowledge, manual handoffs, or senior-person bottlenecks, it isn't ready for AI — yet.

The AI Operator

AI Nativ is led by Dilini Galanga, who has operationalised multiple firms as a fractional COO across the technical, legal, policy, and business layers of this shift.

i

Ex-Google, policy

Years inside Google working on the policies that govern how automated systems make decisions, built in the era before the current AI cycle had a name.

ii

Law-trained

An LLB, non-practicing. The training develops an eye for where operational risk actually sits, which is usually somewhere the client hasn't looked yet.

iii

Founding partner, precious.studio

A parallel practice in UX and product design, where the discipline is shipping systems that people use every day. AI Nativ translates that discipline into the agent era.

Field notes

Make AI work that holds up.

Begin with the diagnostic

Operational rigour,
before the agents.

Phase I is a defined investment that produces decision-grade output, whether or not we continue together.

Book a diagnostic call
Q3 2026 · Limited capacity