Business Challenge

Overstaffed Engineering Teams

You hired for last year’s productivity model. AI tools now do 30-40% of the implementation work. Your team is too big and you’re not sure who to keep.

No AI Adoption Strategy

Engineers experiment with Copilot, Claude, and Cursor alone. There’s no shared playbook, no measurable output gain, just scattered usage and unclear ROI.

Uncontrolled AI Tooling Costs

Subscriptions and API spend creep up monthly with no clear tie to output. You’re paying for tools half the team uses, while token burn quietly compounds.

AI-Era Resource Planning

Audit current team against AI-augmented productivity benchmarks
Define the right team size and skill mix for agentic workflows
Align headcount with realistic output expectations

Agentic Workflow Evaluation

Assess which engineering tasks can be delegated to AI agents
Test and benchmark agentic tools against your actual codebase
Build adoption playbooks your team will actually use

Budget & Token Burn Strategy

Map AI tooling costs to engineering output gains
Prevent runaway API and token spend with usage guardrails
Build a sustainable AI budget tied to business outcomes

What You Get

Team Composition Audit

We evaluate your engineering team against AI-augmented productivity benchmarks. You’ll know which roles are amplified by AI, which need reskilling, and where headcount can be optimized.

Agentic Workflow Playbook

We test agentic tools against your actual codebase and build a practical adoption plan your team will use — not a theoretical framework they’ll ignore.

AI Budget & Token Strategy

We map your API spend, token usage, and tooling costs to engineering output — so every dollar on AI has a measurable return.

Hiring Criteria for the AI Era

We define what “great” looks like when AI handles 40% of implementation — and build interview processes that filter for the right skills.

What Clients Say

“Kunso is my default recommendation for anyone looking for a digital partner.”

Kian Fon Teh

Group CEO at Collectius

“They turned a corporate idea into a live MVP in under a month, delivering a successful internal demo for all stakeholders within tight timelines. An exceptional team.”

Kamil Sarelo

Project Manager at Siemens

“What I thought would take a year to develop, we ended up having a functional product in less than one and a half months.”

Marc Lafleur

Co-Founder of Innertune

Building an engineering team in the AI era?

Stop guessing how many engineers you need. Get a clear plan for team size, AI tooling, and budget — grounded in how AI actually changes output.

Book a Call

Tech Hiring StrategyAI

3x

40%

60%