We build AI systems for companies with real complexity, regulatory pressure, and business risk. Because we understand what matters.
We help enterprise clients design, deploy, and govern AI systems that work across departments, tech stacks, and compliance layers. This is not vendor-led automation. It’s enterprise-grade orchestration, delivered with real accountability.
Role-based access, full traceability, and domain-tuned logic designed for internal use across finance, healthcare, and regulated industries.
Turn video, voice, and image inputs into structured, searchable data. Works across security, CX, and analytics use cases.
Connect fragmented, unstructured environments. Our pipeline automation filters, tags, and prepares your data without brute force.
Architect secure, scalable AI deployments across on-prem, hybrid, and cloud. Designed for uptime, cost control, and compliance from day one.
Frameworks that protect your investment.
We don’t guess. We operate with process. Every Likeable engagement is structured using six proprietary frameworks. Together, they turn scope into milestones, people into accountable teams, and ideas into measurable outcomes.
Assess how prepared your org really is.
Align work to measurable progress.
Define ownership from day one.
Turn strategy into executable architecture.
Deliver fast wins without skipping governance.
Provide proof of post-launch performance in business terms.
Not Built for Everyone. Built for the Right Teams.
Our clients don’t need help running experiments. They need a partner who can help them lead.
They’re under pressure to deliver results—without losing control of compliance, security, or internal trust. They’re past the demo stage. They need something they can deploy, manage, and defend at scale.
Likeable is designed for:
If that’s your world, we speak your language.
We Don’t Guess. We Orchestrate.
Our frameworks and platform have helped enterprise teams:
These are not use case dreams. These are real outcomes for companies who knew what was at stake—and invested accordingly.