What are you solving,
and how a model can solve it with you?

Large Language Models are reshaping how we approach everyday professional problems, unlocking productivity gains and lighter cognitive load. Yet none of it works without the foundational question that anchors the entire process: what problem are we solving, and how do we articulate it in a way an AI assistant can understand?

If your agent works in the lab and stalls in production, let's talk.

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What we build with you

We build platforms that make AI assistants reliable enough to run in your business.

The big gap between an AI agent that impresses in a demo and one you can trust to run your business is trust. Closing that gap means building systems where trust emerges from codified practices. We guide you through the six pieces we designed to hold your agents accountable to your original intent.

Registry Explicitly defining what each agent is built for, to keep accountability clear across handoffs over time.
Evaluation Continuously checking the agent against what the Registry declared, to turn trust into something you can measure.
Observability Clearly seeing every move and every call the agent makes, to keep every decision visible and every cost traceable.
Safety gateway Plainly setting what the agent can decide on its own and what gets routed to a human, to keep autonomy inside the boundaries you trust.
Lifecycle Carefully governing how the agent evolves over time, to track every version, prompt, and tool as one system.
Playbook Steadily holding the practices together to give your teams the operating discipline that ties the rest together: incidents, costs, audits, escalations.

Together we turn your need into a platform, from declared intent to operating capabilities.

How we work with you

We work with you across a three-part cycle.

Advise

Before anything gets built, we sit with leadership to agree on what the agent is actually for: what it should accomplish, what counts as success, what it's allowed to decide on its own, and what return justifies the investment. It's the lightest part of the journey, the one we learn the most from, and the foundation everything else stands on.

From a few-month assessment to a multi-month roadmap.

Deploy

We assemble the six pieces above into the foundation that takes the agreed agent and puts it into operation, with clear limits on its authority, a full record of what it does and why, and the ability to update its parts without breaking the whole. These foundations compound: each new agent makes the next one faster, safer, and better governed.

Cloud-agnostic, deployed wherever the business already operates.

Steer

Live agents drift. Models change, regulations move, data shifts, and real-world use surfaces problems no plan anticipated. We keep the cycle turning: ongoing performance checks, clear escalation when something looks wrong, lessons from incidents written back into the rules, costs tracked agent by agent, and your original intent reopened whenever the conditions behind it stop holding.

An agentic platform isn't a one-time launch, it's an operating and refining rhythm.

Who I am

I'm Tiyab Konlambigue. I drive the deployment of enterprise agentic and semantic systems into customer environments. I help integrate agentic toolkits with internal frameworks to deliver Data & AI platforms that survive real enterprise constraints: security, compliance, legacy data, organizational friction.