AI Development & Agent Engineering
We add production AI capability to existing products and teams.
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 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.
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.
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 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 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.
Audit → Build → Operate & Govern: production AI, not demoware.
Audit
Opportunity mapping, feasibility, and a sequenced roadmap before anyone writes code.
Build
Architecture, integration, evaluations, and a hardened path to production deployment.
Operate & Govern
Drift monitoring, eval regressions, cost controls, and per-tenant governance.
The other two pillars
Frequently asked questions
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.
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.
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.
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.
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.
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.
Tell us what you are building.
We build what large agencies under-deliver and freelancers can't architect, across Web3 protocols, AI agents, and SaaS products. Tell us what you are building. We will tell you how we would approach it, no pitch deck, no fluff, no commitment required.