● AI Development
AI should create real business outcomes—faster service, smarter decisions, and scalable automation. 971 Technologies starts with use‑case discovery and data readiness, then chooses the right approach: retrieval‑augmented generation (RAG), fine‑tuning, or off‑the‑shelf models. We prototype quickly, validate with guardrails (prompt injection and PII protections), and move to production with CI/CD, monitoring, and clear KPIs.
We integrate AI into the tools you already use—web, mobile, CRM/ERP, and data platforms—while enforcing security and privacy: encryption, access controls, content filters, and auditable logs. With MLOps, observability, and cost controls, your AI stays reliable, compliant, and efficient as usage grows.
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What kinds of AI solutions do you build?
971 Technologies delivers LLM copilots/chatbots, knowledge search (RAG), document AI, computer vision, recommendations, forecasting, and automation integrated with your apps and workflows.
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Which models and platforms do you support?
We work with OpenAI/Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic Claude, and open‑source models (Llama, Mistral). For tooling we use LangChain/LlamaIndex, vector DBs (Pinecone, Milvus, pgvector), and standard MLOps stacks.
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How do you protect data and IP?
Private deployments, encryption in transit/at rest, strict access controls, optional data‑retention off, PII redaction, content filtering, and audit logs—aligned with UAE PDPL and ISO 27001 practices.
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RAG vs. fine‑tuning—how do you choose?
RAG is best when knowledge changes often or must stay private; fine‑tuning helps when tone, structure, or domain tasks are repetitive. 971 Technologies often combines both and validates via offline/online evals.
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How long to get an MVP in production?
Discovery to MVP is typically 2–6 weeks depending on complexity and integrations. We provide a roadmap, success metrics, and can manage the platform post‑launch.
