# AI services — Metaborong We add production AI capability to existing products and teams. Canonical: https://www.metaborong.com/services/ai Pillar: ai ## Overview AI Development & Agent Engineering ## What is it? AI development at Metaborong adds production language-model, retrieval, and agentic capability to products that already exist. We deliver it across five areas: AI consulting, generative AI, custom AI agents, business-process automation, and the AI engineering that integrates and hardens it. Senior engineers own every build, with evaluations and cost controls scoped from day one. ## AI · AI Consulting Advisory work for teams sequencing AI adoption: use-case mapping, feasibility, and a roadmap anchored in operating cost, not hype. The output is a defensible plan, not a slide deck. - [AI Consulting & Strategy](https://www.metaborong.com/services/ai/ai-consulting): Use-case mapping, feasibility, and a sequenced adoption plan scoped to impact and operating cost. - [AI Adoption Roadmap](https://www.metaborong.com/services/ai/ai-adoption-roadmap): A phased plan from audit findings to deployment, with team enablement and governance built in. ## AI · Generative AI Solutions Copilots, conversational and voice agents, content generation, and video engineered into products that already have users. The job is to add generative AI without breaking what already works. - [Generative AI Development](https://www.metaborong.com/services/ai/generative-ai-development): GenAI built into your product: content generation, enrichment, and backend integration. - [AI Copilots & Internal Tools](https://www.metaborong.com/services/ai/ai-copilots-internal-tools): Custom copilots for support, sales, and ops teams, grounded in your data and wired into your stack. - [Conversational AI & Voice Agents](https://www.metaborong.com/services/ai/conversational-ai-voice-agents): Chat and voice agents that handle real workflows: discovery, support, and scheduling. - [AI Video Generation](https://www.metaborong.com/services/ai/ai-video-generation): Generative video pipelines engineered into products: scripted, templated, and API-driven. ## AI · Custom AI Agents Custom autonomous and multi-agent systems that plan, use tools, write to your systems, and report, with evaluations, guardrails, and human-in-the-loop checkpoints scoped from the start. - [AI Agent Development](https://www.metaborong.com/services/ai/ai-agent-development): Custom autonomous and multi-agent systems that plan, use tools, and report, with evals and guardrails. ## AI · AI for Business Automation Document, email, and reporting workflows automated and wired into CRMs, ERPs, and third-party tools, plus a compounding, LLM-maintained knowledge base your teams and agents query in seconds. - [AI Business Process Automation](https://www.metaborong.com/services/ai/ai-business-process-automation): Automate document, email, and reporting workflows, with CRM, ERP, and third-party integration. - [AI Knowledge Base](https://www.metaborong.com/services/ai/ai-knowledge-base): A compounding, LLM-maintained knowledge base your teams and agents query in seconds. ## AI · AI Engineering The production AI layer other features depend on: GenAI APIs and backend integration, retrieval pipelines, and evaluation and monitoring, with auth, routing, fallback, cost controls, and observability engineered in. - [GenAI APIs & Backend Integration](https://www.metaborong.com/services/ai/genai-apis-backend-integration): Architect, integrate, and harden LLMs in your stack: auth, routing, fallback, cost controls, observability. - [RAG & Retrieval Pipelines](https://www.metaborong.com/services/ai/rag-retrieval-pipelines): Retrieval pipelines that ground LLMs in your data: embeddings, vector stores, reranking, evaluations. - [AI Evaluation & Monitoring](https://www.metaborong.com/services/ai/ai-evaluation-monitoring): Production evals, drift detection, and observability for live LLM and agent systems. ## How we engage 1. **Audit** Opportunity mapping, feasibility, and a sequenced roadmap before anyone writes code. 2. **Build** Architecture, integration, evaluations, and a hardened path to production deployment. 3. **Operate & Govern** Drift monitoring, eval regressions, cost controls, and per-tenant governance. ## FAQ ### Do you train custom AI models from scratch? No. We integrate, fine-tune, and adapt off-the-shelf foundation models: OpenAI, Anthropic, open-weights through Hugging Face: inside your product. Custom pretraining is out of scope and rarely the right answer for the buyers we work with. ### How do you handle evaluation and quality? Every engagement scopes an evaluation harness at the architecture stage. We instrument retrieval quality, generation quality, and end-to-end task success, then wire those evals into CI so regressions are caught before they hit production. ### What does an AI engagement typically cost? AI audits land in the four-to-six week range. Copilot and RAG builds usually run eight to twelve weeks of senior engineering. Agentic systems and multi-tenant LLM platforms run longer. We scope fixed-bid or weekly capacity depending on which the buyer prefers. ### Can you integrate AI into a product we already ship? Yes. Most of our AI engineering work lands inside existing products, not new builds from scratch. We harden auth, routing, fallback, cost controls, and observability around the LLM layer so the existing product keeps shipping while AI features layer in. ### Which model providers do you work with? OpenAI, Anthropic, Google, and open-weights via Hugging Face and self-hosted inference. We route per workload: different models for retrieval, generation, and agent planning: and engineer fallback paths between providers for resilience and cost. ### Do you handle data security and compliance? Yes. We engineer for the compliance posture your product already operates under: SOC 2, GDPR, India DPDP. PII handling, tenant isolation, audit logging, and data-residency choices are architecture decisions, not afterthoughts.