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AI & Automation

Custom GPT Development Services

We build private, production-grade Custom GPTs and OpenAI Assistants for your operations, support and sales workflows — with retrieval augmented generation, real tool use, evals you can defend in a review, and human-in-the-loop wherever the stakes warrant it.

500+
Clients
98%
Satisfaction
15+
Countries
10+
Years Experience

Beyond "ChatGPT With a Logo on It"

Most "Custom GPT" projects in 2026 are a system prompt pasted into the OpenAI Custom GPT builder, branded with a logo, and shipped with no plan for accuracy, no plan for evaluation, and no plan for what happens when the model is wrong. That works for an internal toy. It does not work when a customer-facing assistant gives a wrong policy answer, or when an internal ops copilot hallucinates a SKU that does not exist.

Our Custom GPT engagements treat the assistant as a piece of software, not a prompt. That means: retrieval augmented generation over your real knowledge base so the model answers from your data; tool use and function calling so the assistant can read live systems (your CRM, your order DB, your scheduling system) instead of guessing; evaluation harnesses with a ground-truth question set so accuracy is measured, not assumed; and human-in-the-loop review on every workflow where a wrong answer has a real cost.

We build on OpenAI's Assistants API (or Claude with tool use, depending on accuracy benchmarks against your workload) and host the orchestration on your infrastructure — Laravel, Next.js, or a managed orchestration platform — so your data does not leave a system you do not control. Data-residency for UAE and KSA clients is handled through OpenAI's enterprise data terms (data not used for training, region pinning where required) or via Azure OpenAI with EU-region hosting.

The deliverable is a Custom GPT plus the surrounding software that makes it reliable: the knowledge ingestion pipeline, the eval suite, the review dashboard, the observability that flags when accuracy drifts, and the documentation for your team to extend it. You can swap the underlying model, swap the vector DB, even swap us out, and the system still works.

What We Build

The Five Custom GPT Use Cases That Pay Back Fast

Use cases we have shipped or scoped for UAE / GCC clients in the last 12 months. Each pays back in under 90 days at a typical SME scale.

Internal ops copilot

Answers staff questions from your SOPs, contracts, training PDFs. Cuts new-hire ramp time and stops senior staff from being a help-desk.

TYPICAL COST · AED 12,000-22,000

Customer service draft assistant

Drafts replies for your CS team in your brand voice with reference to order history. Human reviews and sends.

TYPICAL COST · AED 10,000-18,000

Sales SDR assistant

Qualifies inbound leads against your ICP, drafts personalised follow-ups, syncs to CRM.

TYPICAL COST · AED 14,000-25,000

Document intelligence

Extracts structured data from invoices, contracts, RFPs, passports. Posts drafts to your accounting / ERP for approval.

TYPICAL COST · AED 8,000-18,000

Product Q&A on storefronts

Answers buyer questions on ecommerce product pages from your spec sheet and policy docs. Increases conversion, reduces support tickets.

TYPICAL COST · AED 12,000-20,000
Deliverables

What's in Every Custom GPT Build

Same components, every engagement. The size of each grows with the use case.

Discovery + use-case definition

We sit with the operator (not just the manager) for 4-6 hours. Write what success looks like in measurable terms.

Knowledge ingestion pipeline

Documents chunked, embedded (text-embedding-3-large or Voyage), indexed in Pinecone, Weaviate, or pgvector on your Postgres.

Tool / function definitions

Functions the assistant can call against your live systems — read-only by default, write actions gated by human approval.

Evaluation harness

Ground-truth question set (50-200 Q/A pairs from your real ops). Automated regression tests on every model or prompt change.

Review dashboard

Where humans see assistant drafts, edit, approve, send. Built in the same Laravel/Next.js stack as the rest of your site.

Observability

Cost per session, latency P95, accuracy on eval set, user satisfaction signal. Shipped to Grafana or your existing analytics.

Documentation

Architecture diagram, runbook for prompt edits, runbook for adding new tools, hand-over recording.

Optional: human approval queue

When the use case demands it, every assistant reply lands in a queue for human review before sending.

Optional: voice surface

WhatsApp Business / Vapi / Retell front-end if voice is the right channel.

Process

4-6 Week Engagement, From Brief to Production

No 6-month pilot that never ships. Production deployment is the explicit goal of week 6.

Week 1

Discovery: shadow the operator, define ground truth, write SOW.

Week 2

Prototype: knowledge base ingested, baseline assistant working against a 20-question eval set.

Week 3

Tools: function calling against your real systems, read-only first.

Week 4

Dashboard + human-in-the-loop: review queue, edit flow, send action.

Week 5

Eval expansion (50-200 ground-truth Qs), accuracy tuning, observability shipped.

Week 6

Production deploy + parallel A/B with the manual workflow. Measure time-saved and accuracy.

Tech Stack

Models, Frameworks, and Why

LayerDefaultWhen we deviate
ModelGPT-5 via OpenAI Assistants APIClaude 4 Sonnet/Opus when tool-use accuracy or long-context wins matter; Azure OpenAI for EU/UAE region pinning
Embeddingstext-embedding-3-largeVoyage 3 for cost-sensitive ingestion at scale
Vector storepgvector on Postgres (Supabase or self-hosted)Pinecone or Weaviate when collection exceeds 5M chunks or latency matters
OrchestrationLaravel jobs + queues; Next.js API routes for synchronous flowsLangGraph or custom workflow engine on complex multi-step agents
EvalCustom harness with ground-truth Q/A in PostgresBraintrust or LangSmith on larger deployments
ObservabilityHelicone or self-hosted LangfuseOpenTelemetry to your existing Grafana stack
Front-endYour existing site or a dedicated dashboard built in Next.jsSlack/Teams app surfaces when ops staff already live there

Pricing

Custom GPT engagements start at AED 8,000 (~$2,180) for a single use case with limited tool integration. Production-grade multi-tool assistants with eval harness and human-in-the-loop typically land at AED 14,000-25,000. Recurring infrastructure cost after launch is normally AED 200-800/month depending on volume. Fixed-price terms apply.

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Frequently Asked Questions

Custom GPT — Common Questions

For internal-only use cases with a small audience, the OpenAI Custom GPT product is genuinely fine and we can configure one for you. For production systems with measured accuracy, integration into your existing tools, or customer-facing surfaces, the Custom GPT product is a starting point — the production system is a Laravel/Next.js app calling the Assistants API directly, hosted on your infrastructure with your data controls.
Default GPT-5 via OpenAI; Claude 4 Sonnet or Opus where Anthropic outperforms on tool use or long-context tasks. We benchmark both on your specific eval set before locking the choice. Azure OpenAI is used for region-pinned deployments where data residency matters.
We measure accuracy on a ground-truth Q/A set built from your real workflow. Typical production assistants land between 85-95% on a 100-question eval after tuning. Anything below 90% on customer-facing surfaces is paired with mandatory human review. We share the eval methodology in the SOW so accuracy is a measured number, not a promise.
4-6 weeks for a single use case from kick-off to production. Each additional tool or workflow adds 1-2 weeks. Multi-tool agentic systems usually run 8-12 weeks.
OpenAI enterprise terms (data not used for training, encryption at rest, region pinning on request) for most clients. Azure OpenAI with UAE-North or EU region for clients with stricter requirements. We document the data flow in the SOW.
Yes via function calling — read-only by default, write actions gated by human approval. We do not ship fully autonomous write-action AI for customer-facing use cases. The reputation risk on the 1-in-200 hallucination outweighs the time savings.
Yes. Full IP transfer on final payment — repository, prompts, eval set, infrastructure-as-code, and documentation. You can extend it, replace us, or replace the underlying model without our involvement.
Two cost lines: model API calls (varies by volume; typical SME workload is AED 200-800/month) and infrastructure (~AED 50-200/month on Supabase + hosting). We commit to a budget alert at AED 1,500 on launch so cost surprises do not happen.
SaaS AI products are great for cases that fit their feature set. Custom GPT development makes sense when you need integration with your specific systems, your specific knowledge base, your specific tools, and your specific accuracy bar — all of which SaaS products generalise away from.
Yes if that is the right channel — we publish the Custom GPT in the GPT Store under your account. More commonly we surface the assistant inside your own product or via WhatsApp, Slack, or a web widget so you control the conversation context and data flow.