AI, LLM, and ML consulting
A separate SureM practice. We engage with each company on their own problems, data, and constraints — and design, build, and operate custom AI agents, LLM applications, and ML systems.
SureM's AI consulting practice is independent from our messaging products. We are hired to solve a specific business problem with AI/ML — starting from the customer's workflow, data, regulatory posture, and operating constraints. Every engagement is custom: there is no off-the-shelf product, no required integration with SureM channels, no vendor lock-in.
Engagement model
What we build for clients
Tool-using agents tailored to a specific workflow — support, ops, research, sales, internal automation. Planning, retries, guardrails, and human-in-the-loop checkpoints designed around your process.
Production LLM systems with prompt and structured-output design, function calling, schema validation, fallback chains, and cost/latency budgeting tied to your SLAs.
Retrieval pipelines over your private corpora — policy, product, support, contracts, code — with intent classification, routing, and evaluation built in.
Forecasting, anomaly detection, classification, and risk scoring — the right tool for problems where an LLM is overkill or unreliable.
Offline eval harnesses, online A/B and shadow traffic, drift monitoring, red-team suites, and the ops scaffolding to keep models reliable post-launch.
For teams earlier in the journey — AI roadmap, build vs. buy, vendor selection, data readiness audits, and pilot prioritization.
How an engagement runs
Map the business problem, data, current workflow, success metrics, risk surface, and regulatory constraints. Output: a decision document, not a sales deck.
Model selection (proprietary vs. open-weight), hosting (managed vs. on-prem vs. air-gapped), latency, cost, evaluation strategy, and compliance posture — documented for stakeholders.
A production-grade prototype with eval harness, observability, and a gate criterion. Real data, real users, measured outcomes.
Either we run it with you, or we transition full ownership to your team with runbooks, eval sets, and dashboards. No lock-in.
Tools we work with
Stack chosen per engagement based on data sensitivity, latency, regulatory posture, and operating budget. We are not tied to a single vendor.
Why work with this practice
We do not sell a product. Every system is designed for your workflow, data, and constraints — built to ship and to be owned by your team.
Pilots ship with eval suites, observability, fallback paths, and operating runbooks. We optimize for what survives in production, not what looks good in a slide.
SureM is a 25-year telecom-grade operator. Telecom SLAs, incident response, and observability discipline carry over directly to AI engagements.
NDA-first engagements. Data handling, prompt logging, and model isolation are designed up front — not retrofitted.
Delivery teams across Korea, China, and the U.S. with bilingual capability — useful for enterprises operating across APAC.
This practice is independent of our messaging products. If your AI work happens to overlap with messaging, fine; if not, that is also fine.
Confidentiality
Examples of engagements
- ›Internal support agents over policy + product docs
- ›Account-takeover and fraud risk scoring
- ›Multilingual voice IVR and call-routing
- ›Sales-research and outbound agents
- ›Document analysis and contract review
- ›Deliverability and demand forecasting
- ›AI roadmap and build-vs-buy advisory