From prototype to production behind the firewall.

Objection 01

“OpenAI Enterprise already has our data in a private tenant. Why should we duplicate infrastructure with you?”

Because a tenant is not a capability. The OpenAI Enterprise tenant is a model endpoint - it does not give you a retrieval layer, an eval pipeline, a Khaleeji safety classifier, a model-choice strategy, or a sovereign-deployment posture. Brocode delivers the capability layer that wraps that tenant (and any other model provider) and turns it into something your risk committee will sign. Many of our clients run both: OpenAI for global English workloads, the Brocode stack for sovereign and Arabic workloads.

Proof: anonymised tier-1 UAE bank reference - an internal RAG assistant over 4.2 million policy and product documents, sitting in front of Azure OpenAI UAE North for the English flows and a self-hosted Llama 3.3 70B for the Arabic flows. 87% first-contact resolution lift in the corporate-banking back-office.

Objection 02

“The Big-3 consultancies will give us a CxO-flavoured roadmap. Can a regional engineering firm actually own the build through to production?”

Yes - and we will commit to it on a fixed-fee, fixed-scope contract that the strategy houses will not. The 12-Week Production Path is the same methodology we have run for tier-1 GCC banks, federal entities, and KSA conglomerates. Engineering depth shows up in the eval harness, the red-team test pack, the model-choice abstraction layer, and the named senior engineers who are on the contract and the standup. The strategy deck arrives as a by-product of the build, not as the deliverable.

Proof: anonymised KSA conglomerate reference - a finance-and-procurement copilot saving 18 FTE-equivalent across shared services within seven months, with the original 12-week build paid as a fixed fee and the run-phase SLA on a separate per-quarter pricing band.

Objection 03

“Our risk committee will not approve any deployment without documented red-team results, hallucination rates per use case, and an exit strategy if the underlying model is deprecated.”

All three are in the standard governance pack. Red-team results follow a documented adversarial test plan (prompt injection, jailbreak, Khaleeji and English safety classifiers). Hallucination rates per use case are measured on a domain-specific golden set using Giskard and DeepEval in CI, refreshed monthly. The model-deprecation exit strategy is the model-choice abstraction layer: the application code does not depend on a specific model provider, so any provider can be swapped on a documented playbook with no application-layer rewrite.

Proof: anonymised federal entity reference - a sovereign LLM gateway serving 14,000 employees, fully on Khazna, with a board-approved governance pack mapped to NIST AI RMF and the UAE AI Charter. Two model swaps (one base model deprecated, one upgraded) executed inside the run-phase SLA with zero application rewrite.