SM Stratagem builds custom chatgpt development for Abu Dhabi teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.
We build custom chatgpt development for teams in Abu Dhabi that need working software, not a slide deck for next quarter's steering committee. Abu Dhabi's pull for us is public sector and Mubadala-adjacent enterprise, and briefs coming out of ADGM and the ministries rarely want another pilot that dies before rollout. So our default is private ChatGPT-style systems trained on your data and deployed on your infrastructure, measured and iterated before anything touches production traffic. Every project ships with docs, evals, and a runbook the next engineer can pick up cold, without a knowledge-transfer week. The team is remote-friendly but in-region, so travel to Abu Dhabi for workshops and go-live is standard, not a favour we ask for. What sets our custom chatgpt development delivery apart is that the engineer who scopes the build is the same engineer who ships it and shows up at the go-live call. If the project has already stalled once, the shape of the first release was usually wrong — that's fixable in a week, not a quarter.
Buyers in Abu Dhabi are done with pilots. What they want now is one production system, measured, running, and reducing a real cost line or lifting a real revenue line. That's the frame we work inside.
The private ChatGPT sits inside your cloud account or on-prem. No conversations training external models, no data leaving the perimeter you control.
Answers pull from your documents, wikis, tickets, and product content, with citations. It's a ChatGPT that actually knows your business.
Log in with your identity provider, see the documents your role is allowed to see, and nothing else. Access control matches your existing HR and Active Directory setup.
Every conversation is logged with user, timestamp, sources, and cost, so audit, security, and finance can see exactly what's happening across the tool.
A private ChatGPT trained on your policies, playbooks, and product content, available to every employee through SSO.
Sales copilots, legal review assistants, and support tools grounded on the docs the function actually cares about, not the whole company.
A private ChatGPT that meets your data-residency and audit needs, deployed in the cloud region your regulator expects.
For entities inside ADGM and the Abu Dhabi government, we build custom ChatGPT systems that respects data-residency, vendor-review, and procurement rules from the SoW onward. The cloud region and audit trail get decided before the first line of code.
ChatGPT Enterprise is a great product, but it's OpenAI-hosted, one-size-fits-all, and doesn't integrate deeply with your internal data or systems. A custom build gives you deeper knowledge integration, model choice (including open-source), your cloud region, and role-based access tied to your identity provider. If you don't need those things, ChatGPT Enterprise is fine and we'll say so.
Four to eight weeks for a first working version. That covers document ingestion, the chat UI, SSO integration, basic role-based access, and an initial evaluation set drawn from your team's real questions. Longer builds add multi-tenancy, advanced governance, deeper system integrations, bilingual output formatting, and continuous retraining pipelines that keep the assistant fresh as your knowledge base changes.
It depends on data-residency and workload. If data can go to a vendor API, Claude and GPT-4-class models via Azure OpenAI or AWS Bedrock give the best quality-per-effort. When data must stay on your infra, we use open-source models (Llama, Mistral, Qwen) — often lightly fine-tuned on your content — and benchmark against the closed-model baseline.
The ingestion pipeline runs on a schedule and re-indexes new or updated documents automatically. For high-change sources (a wiki, a ticketing system, a document management system) we set up webhook-driven updates so changes appear in the assistant within minutes rather than the next nightly run.
Yes. Role-based access tied to your SSO provider means the assistant only retrieves documents the user is entitled to see. If HR docs are restricted to HR, the assistant honours that boundary. We test this explicitly in evals — one of the eval categories is 'user of role X asks about restricted document Y' — because access-control bugs in retrieval are the most damaging kind.
Ongoing cost depends on usage and model choice. For a mid-market rollout on hosted models, expect a per-user monthly cost in the same order of magnitude as ChatGPT Enterprise, plus the compute you already run. For open-source deployments the cost profile shifts to hardware and DevOps. We produce a cost model with each proposal so there are no surprises.
Yes. We deliver into ADGM-registered entities and Abu Dhabi government departments on a regular basis, which usually means tighter data-residency and vendor-review controls. We plan for those constraints inside the SoW rather than trying to bolt them on right before go-live, so audits and reviews rarely become the bottleneck.
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We'll scope the first release, define the eval set, and give you a build plan you can hand to any engineering team — ours or yours.