Your AI Consulting Partner
for Enterprise Messaging
Expert guidance on LLM integration and machine learning strategy for messaging platforms.
Why is SureM’s consulting special
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
A clear, phased approach from discovery to production —
with full transparency and measurable outcomes.
scoping, feasibility,
and risk review
production-grade prototype
with eval harness
hardening, monitoring,
and ongoing model + agent ops
documented runbooks, eval suites,
dashboards — your team owns it
What we build for clients
End-to-end AI solutions tailored to your business.
From intelligent agents to enterprise-grade systems and strategy.
AI agents and orchestration
Tool-using agents tailored to a specific workflow — support, ops, research, sales, internal automation. Built with human-in-the-loop checkpoints for reliability.
LLM applications
Production LLM systems with prompt and structured-output design, function calling, schema validation, fallback chains, and cost/latency budgeting tied to your SLAs.
Retrieval and search (RAG)
Retrieval pipelines over your private corpora — policy, product, support, contracts, code — with intent classification, routing, and evaluation built in.
Classical ML systems
Forecasting, anomaly detection, classification, and risk scoring — the right tool for problems where an LLM is overkill or unreliable.
Evals, observability, ML ops
Offline eval harnesses, online A/B and shadow traffic, drift monitoring, red-team suites, and the ops scaffolding to keep models reliable post-launch.
Strategy and advisory
AI roadmap, build vs. buy, vendor selection, data readiness audits, and pilot prioritization — so you invest in what drives real impact.
How an engagement runs
From business discovery to production ownership, every engagement is structured around clear decisions, measurable outcomes, and no lock-in.
Discovery
Map the business problem, data, current workflow, success metrics, risk surface, and regulatory constraints.
OUTPUT a decision document, not a sales deck.
Architecture and tradeoffs
Model selection (proprietary vs. open-weight), hosting (managed vs. on-prem vs. air-gapped), latency, cost, evaluation strategy, and compliance posture — OUTPUT documented for stakeholders.
Pilot build
OUTPUT A production-grade prototype with eval harness, observability, and a gate criterion. Real data, real users, measured outcomes.
Operate or hand off
Either we run it with you, or we transition full ownership to your team with
OUTPUT runbooks, eval sets, and dashboards. No lock-in.
Tools we work with
open-weights
Haystack
Braintrust evals
XGBoost
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
Custom by default
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.
Production engineering, not demos
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.
Operating discipline
SureM is a 25-year telecom-grade operator. Telecom SLAs, incident response, and observability discipline carry over directly to AI engagements.
Confidentiality and isolation
NDA-first engagements. Data handling, prompt logging, and model isolation are designed up front — not retrofitted.
Korean + global delivery
Delivery teams across Korea, China, and the U.S. with bilingual capability — useful for enterprises operating across APAC.
No required SureM integration
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
Discovery and pilot engagements run under mutual NDA. Data handling, prompt logging policy, and model isolation are agreed before any data moves.
Examples of engagements
policy + product docs
fraud risk scoring
and call-routing
outbound agents
contract review
forecasting
build-vs-buy advisory
Frequently asked questions
Common questions about SureM’s AI, LLM, and ML consulting practice — engagement model, confidentiality, and how it differs from a typical AI vendor.
What is SureM's AI, LLM, and ML consulting practice?
A separate practice from SureM's messaging products. We design, build, and operate custom AI agents, LLM applications, and ML systems tailored to each client's workflow, data, and regulatory constraints — with no off-the-shelf product.
Do I need to use SureM's messaging products to work with this practice?No. This practice is fully independent of SureM's messaging channels. If your AI project happens to touch messaging, that's fine — but it's never a requirement.
How long does a typical engagement take?Most engagements start with a 1–2 week discovery sprint for scoping and feasibility, followed by a 4–8 week pilot delivery phase that produces a production-grade prototype with a full evaluation harness.
How much does an engagement cost?There's no fixed, off-the-shelf price — cost scales with scope, data complexity, and infrastructure requirements. The discovery sprint is quoted as a fixed fee upfront, so you know the cost before committing. Pilot delivery and ongoing operate-and-scale work are quoted after discovery, once the build plan, data footprint, and hosting requirements are clear.
What happens after the pilot is complete?You choose: SureM can operate and scale the system with ongoing model and agent ops, or hand off full ownership to your team with documented runbooks, eval suites, and dashboards.
What AI models and infrastructure do you work with?We're vendor-agnostic. Stack choices — proprietary APIs (OpenAI, Anthropic, Google) or open-weight models (Llama, Qwen, Gemma), managed or on-prem/air-gapped hosting — are made per engagement based on data sensitivity, latency, and compliance needs.
How is data confidentiality handled?All discovery and pilot engagements run under mutual NDA. Data handling, prompt logging policy, and model isolation are agreed before any data moves — not retrofitted afterward.
What kinds of problems have you solved for clients?Examples include internal support agents over policy and product docs, fraud and account-takeover risk scoring, multilingual voice IVR, sales-research agents, contract review automation, and demand forecasting.
How is this different from a typical AI consulting firm?SureM operates as a 25-year telecom-grade infrastructure provider. That means telecom-grade SLAs, incident response, and observability discipline carry directly into every AI engagement — we build for what survives in production, not for a demo.
Start With a Discovery Sprint
A 1–2 week scoping engagement that maps your problem, data, and constraints into a clear, decision-ready build plan — no commitment required beyond the sprint.