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Growthly.

AI Lab

We build our own agents before we build yours.

The Lab is where we carry the cost of finding out what breaks. Runtimes, evaluation harnesses and voice systems get built for our own use first. What holds up in production becomes a component we deploy for clients.

A dark data centre aisle with rows of black racks receding into shadow

What is running now.

Harbor

In production

Agent runtime

A self-hosted runtime for agents that are allowed to touch real systems.

Harbor runs agent workloads inside a sandbox with explicit tool permissions, full request tracing and an immutable audit log. It is what we deploy when a client needs an agent inside their own network and their security team needs to read every action it took.

  • Per-tool permission grants with human approval steps
  • Immutable audit log for every tool call and model response
  • Deploys into a client VPC with no outbound data path by default

Sift

In production

Evaluation harness

Model and agent evaluation that runs in CI, not in a meeting.

Sift turns quality into a versioned number. It holds datasets, adversarial suites and regression thresholds, runs them reproducibly against any model or agent version, and blocks a release that scores below the line.

  • Versioned datasets with reproducible, seeded runs
  • Adversarial and long-context suites built per capability
  • Regression gates wired into existing deploy pipelines

Relay

Private beta

Voice agent

A voice agent for operations teams that hand off cleanly to a human.

Relay handles scheduling, intake and escalation over the phone in Arabic and English. It scores its own confidence on every turn and routes anything below threshold into a human review queue with the full transcript attached.

  • Arabic and English with per-tenant vocabulary
  • Confidence scoring with automatic human handoff
  • Writes back to the systems of record, not to a scratch database

Tessera

Research

Embodied agents

Perception and task planning for robots working in unstructured sites.

Tessera is our internal research track on embodied agents: grounding language instructions in a live scene, planning multi-step physical tasks, and recovering when the world does not match the plan. Not a product yet. We publish what we can.

  • Scene grounding from monocular and depth input
  • Multi-step task planning with recovery behaviours
  • Simulation-to-site transfer testing

Building for AI producers.

Companies whose product is a model or an agent have a specific set of problems. These are the ones we get called for.

Quality that survives a version bump.

A model update should not be a gamble. We build the evaluation harness, write the adversarial suites with your research team, and gate releases on thresholds you set per capability. Regressions get caught in CI instead of in support tickets.

Agents allowed near production systems.

Tool sandboxing, explicit permission grants, human approval steps and an immutable audit log. Security reviews are a lot shorter when every action an agent took is already written down.

The interface is most of the product.

Review queues, confidence surfacing, correction flows and clean human handoff. Operators need to see what the system was unsure about, and fix it in one place, without opening a database.

Cost that does not scale linearly with usage.

Routing between models by task, caching at the right layer, and measuring cost per resolved job rather than per token. We report the number that maps to your unit economics.

Bring us something that has not worked yet.

Agent reliability, evaluation coverage, retrieval quality, inference cost. Send the failing case and what you have already tried.