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

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Foundation Models

Tool Use & Orchestration

RAG & Memory

What teams ask before deploying agents

Future is here. Schedule a call.

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