Build AI agents
that actually
work in production.
LomE provides the complete 10-tool stack for AI MLOps Agents & CRM start-ups — from LLM orchestration and vector databases to Kubernetes deployment and human feedback loops. Revenue from per-agent SaaS, implementation fees, and usage-based API consumption.
Smart agents.
Real outcomes.
LomE builds and deploys autonomous AI agents that interact with customers and internal systems. We orchestrate agentic workflows using large language models, integrate with CRM platforms to read and write customer data, and maintain a robust MLOps pipeline for continuous model improvement, monitoring, and governance.
Revenue model: per-agent/per-seat SaaS subscriptions, implementation fees, usage-based API consumption, and managed AI agent services. A single implementation project bills at $27k–$90k; ongoing management retainers at $2.7k–$9k/month.
Explore all 10 toolsLLM Orchestration & Agent Framework
The "LEGO set and black box recorder" for building AI brains. LangChain provides the building blocks to teach an AI how to break down complex tasks — "Book a meeting with John" — into steps. LangSmith records every decision so you can see why it succeeded or failed. Agentic reliability and debugging are not optional: LangSmith's observability and tracing is required to debug looping agents, reduce token costs, and ensure consistent business logic.
Vector Database & RAG
A "digital library with a photographic memory." Pinecone or Qdrant converts your company's documents and past conversations into a format the AI can instantly search, finding the exact paragraph that answers a customer's question. RAG is the only production-ready pattern for injecting proprietary knowledge into AI agents — reducing factual errors by 90% and enabling the agent to speak authoritatively about your product.
LLM Gateway & Model Management
A "universal remote and tollbooth" for AI brains. Instead of hard-coding your agent to only use OpenAI, you plug it into Portkey. You can instantly swap to a cheaper or better model, see exactly how much each conversation costs, and block the AI from ever seeing a credit card number. Betting on a single LLM provider is risky and expensive. An LLM gateway enables cost optimization and resilience through automatic failover.
AI-Enhanced CRM Platform
A "digital Rolodex and diary" for the AI agent. HubSpot stores everything known about a customer — their company, past purchases, support tickets — so the AI agent can pick up a conversation exactly where it left off. HubSpot provides the structured database and APIs for agents to read/write customer information, and its native workflows can trigger AI agents based on form fills or deal stage changes.
MLOps Experiment Tracking & Model Registry
A "digital lab notebook and library" for AI model training. Weights & Biases automatically records every configuration and result while training a custom LLM, so you can reproduce the magic and safely promote the best model to production. Training a custom LLM without tracking is unreproducible science. W&B provides the audit trail required for compliance and enables confident rollback if performance degrades.
Prompt Engineering & Evaluation
A "unit testing and quality assurance lab" for AI conversations. Write a test: "The AI should never mention a competitor" — and the system automatically runs that test against 100 sample questions every time you tweak the prompt. Changing one word in a prompt can break edge cases. Promptfoo and Humanloop provide automated testing to ensure prompt changes improve overall performance without introducing subtle failures.
AI Agent Observability & Monitoring
A "fleet dashboard and black box" for your AI employees. Arize AI and Langfuse show in real-time how many conversations are happening, if the AI is getting slower or more expensive, and flag individual conversations where the user gave a thumbs-down for human review. Deploying an agent is Day 1. Monitoring is Day 2+. Observability provides the data to identify edge cases and retrain models.
Data Labeling & Human Feedback
A "digital quality circle" for teaching AI. Human experts review the AI's draft responses, correct them, and rate them. This corrected data is fed back into the training loop, making the AI smarter over time. Fine-tuning on a curated, domain-specific dataset is the only way to achieve expert-level performance. Human feedback also provides the ground truth for evaluating prompt changes and RLHF pipelines.
Container Orchestration & Deployment
A "robotic harbor master" for your AI agent software. GKE Autopilot makes sure the right version of the agent is running on the right server, automatically restarts it if it crashes, and can spin up extra copies during busy times. An AI agent handling thousands of concurrent conversations needs to scale — Kubernetes provides the orchestration layer to run containerized agents reliably with rolling updates and auto-scaling.
The Complete 10-Tool Stack
Every tool in the AI MLOps & CRM stack — with pricing, margins, buyer personas, and the BDM sales roadmap baked in. No guesswork, no blank cards.
AI MLOps Workflow
The complete data flow from knowledge ingestion through agentic orchestration, MLOps improvement loop, and production deployment — mapped to the 10 tools.
6 Ideal Buyer Profiles — Per Tool
Every tool in the stack has a defined buyer persona with a go-to-market motion and acquisition strategy. These are not generic — they are derived from the stack's specific value propositions.
Skills at LomE AI Agents
The critical technical skills that separate functional AI agent deployments from reliable, enterprise-grade systems that clients actually pay for.
Explore the AI Learning PathPrompt Engineering & Evaluation
Master chain-of-thought reasoning, few-shot learning, system prompt architecture, and automated regression testing with Promptfoo. Build golden datasets and measure prompt performance quantitatively — so every change improves reliably.
Learn moreBuilding AI Agents at LomE
Our AI engineers work at the intersection of LLM research and production engineering — shipping agents that answer real customers, update real records, and improve from real feedback. Here is what that looks like.
$60k ARR per agent seat.
$500/month per agent seat × 10 customers = $60k ARR. That covers the entire annual software budget in a single month. Implementation projects bill at $27k–$90k. Retainers at $2.7k–$9k/month.
Ideal clients across every sector
The AI Agents stack serves clients from FinTech and Healthcare to E-Commerce and Government — any organization with customer conversations, CRM data, and a need for reliable automation.
AI Agent Deployment Pricing
Three engagement tiers from a single-agent POC to a full enterprise MLOps deployment. All prices are before LLM API pass-through costs, which are billed to the client at cost plus a 15% management fee.
Year 1 Budget Summary
Complete turn-key cost for 3 engineers building and deploying AI agents from day one. Recurring software: ~$10.4k/yr. Hardware one-time: $9k. Total Year 1: ~$21,388.
| Category | Tool / Item | Annual Cost (USD) | Notes |
|---|---|---|---|
| 01 — Agent Orchestration | LangSmith Team (3 users) | $1,404 | $39/user/mo · LangChain OSS free |
| 02 — Vector Database | Pinecone Standard (estimate) | $840 | Based on ~1M vectors, moderate traffic |
| 03 — LLM Gateway | Portkey Team (3 users) | $720 | $20/user/mo · LiteLLM OSS free |
| 04 — CRM Platform | HubSpot Sales Hub Pro (3 users) | $1,200 | Includes API access for agent integrations |
| 05 — Experiment Tracking | W&B Team + HF Hub Pro (3 users) | $2,124 | $50/mo W&B + $9/mo HF per user |
| 06 — Prompt Engineering | Humanloop Team (3 users) | $1,800 | $50/user/mo · Promptfoo OSS free |
| 07 — Agent Observability | Langfuse Cloud Starter | $600 | Or self-host for $0; cloud for managed ops |
| 08 — Data Labeling | Label Studio (self-hosted) | $0 | Free OSS; no license required |
| 09 — Container Orchestration | GKE Autopilot (low volume) | $1,200 | ~$100/mo for dev/staging workloads |
| 10 — AI Dev Workstations | 2× Lambda TensorBook (one-time) | $9,000 | One-time hardware; deferred to cloud = +$2k/yr |
| LLM API Usage | OpenAI / Anthropic API (POC budget) | $2,000 | Pass-through to clients at cost +15% |
| Miscellaneous | Domains, SSL, internal tools | $500 | GitHub, Cloudflare, Notion |
| Total Year 1 | Full Stack — 3 Engineers | ~$21,388 | ~$10,388/yr recurring · $9k one-time hardware |
Why the numbers work.
The AI agent stack is extraordinarily capital-efficient. A 3-person team can build, deploy, and manage agents that would have required a 20-person AI lab just two years ago.
Data Privacy, Compliance & Governance
LomE AI Agents deployments are designed for compliance from day one. PII redaction is enforced at the LLM Gateway layer (Portkey/LiteLLM). Data-at-rest in Pinecone and HubSpot is encrypted. For HIPAA, PCI, and FedRAMP engagements, we deploy on-premise GPU workstations to ensure data never leaves the client's environment. All fine-tuning datasets are handled under a Data Processing Agreement. For governance inquiries, contact ai-compliance@lome.io.