Question 1

Which model do you recommend — and why?

Accepted Answer

It depends on the task. Claude excels at long-context reasoning and following complex instructions. GPT-4o is strong for multimodal and tool-use tasks. For cost-sensitive, high-volume deployments, Mistral or Llama 3 fine-tuned on your domain often outperform frontier models at a fraction of the cost. We run benchmarks on your actual data before committing.

Question 2

How do you measure agent quality?

Accepted Answer

We build an eval harness before shipping anything. This is a dataset of real inputs with expected outputs that we run against every build. You get numeric scores — accuracy, latency, cost per run — not just 'it seemed to work in testing.'

Question 3

What about hallucination?

Accepted Answer

Hallucination is a design problem, not just a model problem. We use retrieval-augmented generation to ground responses in your actual data, add citation requirements that force the model to reference sources, and build guardrails that catch out-of-scope responses before they reach users.

Question 4

Can the agent actually speak Arabic well?

Accepted Answer

Yes — with the right setup. Claude and GPT-4o have strong Arabic capabilities. For enterprise-grade Arabic, we often fine-tune a Llama or Mistral base model on your domain-specific Arabic data. We also handle bidirectional text, Arabic numerals, and dialect variation.

Question 5

Where does the data live?

Accepted Answer

Wherever you need it to. We can deploy to your AWS/GCP/Azure environment, run on-prem on your GPU cluster, or use API-based models with your own API keys so no data transits through our infrastructure. GDPR and data residency requirements are scoped in discovery.

Question 6

How do you handle versioning and rollback?

Accepted Answer

We version every prompt, every tool definition, and every model configuration in Git. Rollback means a git revert and a redeployment. We also maintain parallel shadow evaluation so you can A/B test agent versions against the same eval dataset before promoting to production.

AI Agents That Actually Ship — Not Demos

Agents your team can trust to operate on real data

Build, compare, and ship production agents in weeks

Eval harnesses and guardrails baked in from day one

We integrate with the tools your team already runs on

Foundation Models

Tool Use & Orchestration

RAG & Memory

What teams ask before deploying agents

Future is here. Schedule a call.

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