Turn large language models into revenue-generating products

We start with prompting and retrieval because they are cheaper and faster to iterate. We fine-tune only when data shows it meaningfully improves accuracy, cost, or latency.

Yes. We support self-hosted open models and private cloud deployments so your data never leaves your boundary, with full audit logging.

We ground models with retrieval, constrain outputs to schemas, and run continuous evals with human-in-the-loop checks on high-risk paths.

We are model-agnostic and benchmark frontier and open-weight models per use case, then choose the best balance of quality, cost, and control.

Cost scopes to model choice, data volume, and how much fine-tuning vs. prompting the use case needs. We quote a fixed price after a short discovery call, not open-ended hourly billing.

Cost scopes to the problem, not a headcount rate card. After a free AI audit we return a fixed, itemized quote tied to clear milestones, so you know the number before any work starts.

Fixed price, tied to outcomes and scope. We don't bill open-ended hours — every engagement has a defined plan and cost agreed upfront.

Most engagements produce a working, data-validated prototype in 2–4 weeks, with full production rollout typically inside one quarter depending on scope and integration complexity.

Yes — we scope a proof of concept against your real data first, so you see measurable value before signing off on the full production build.

Security is built in by default: HIPAA, SOC 2 and GDPR-aware architecture, encryption in transit and at rest, role-based access control, and full audit logging on every deployment.

No, not without your explicit consent. Your data is used to serve your deployment — it is never used to train models for other clients.

Yes. We support private-cloud and on-premise deployments hosted within the UAE and wider GCC, so data-residency requirements are met without sending your data offshore.

Yes — for teams with strict compliance or residency requirements, we deploy self-hosted open models or private cloud infrastructure instead of public model APIs.