Chadsel LLCBusiness IT · AI Agent Integration
AI Agents for BI and Office Work.
Two things pay for themselves in a small business: knowing what the numbers are doing, and not doing the same office task by hand every week. We build AI agents for both — business intelligence on your own data, and automation of the recurring office processes around it.
Serving Long Beach, Torrance, and the South Bay. Where the data is sensitive, the same agents run on a local LLM on your own hardware — nothing leaves your network, and your team can still verify every output.
Qualified new-client proposals may include an onboarding credit when eligibility and permitted work are stated in the signed proposal.
What It's For
Business intelligence, and the office work around it.
We do not sell a chatbot. Every engagement targets either a number your team cannot get quickly, or a process someone is doing by hand on a schedule — and we start with the narrowest one worth shipping.
Business intelligence
Numbers without a data analyst
Revenue, margin, aging receivables, job profitability and pipeline pulled from the systems you already run — QuickBooks, your CRM, spreadsheets, the line-of-business app — and answered in plain language instead of a report request.
The reporting pack, on a schedule
The monthly or weekly pack assembled and drafted for you, with the commentary written and the outliers flagged, so review replaces assembly.
Ad-hoc questions, same day
Which customers slipped this quarter, which service line carries the overtime, what changed since last month — asked in a sentence, answered against your own data with the query shown.
One version of the numbers
A defined data layer under the agent, so two people asking the same question get the same answer. Most BI projects fail here, not at the dashboard.
Office process automation
Quote to invoice
Intake, quote drafting, job or order records, and invoicing wired together so a request stops being re-typed into three systems.
Accounts payable and expenses
Invoices and receipts read, coded to the right account and job, matched to the PO, and queued for one human approval instead of manual entry.
Email and ticket triage
Inbound sorted, classified, routed and drafted — with the exceptions escalated to a person rather than answered by a guess.
Scheduling and dispatch
Appointment and crew scheduling, confirmations, reschedules and follow-ups handled against your calendar and constraints.
Documents that write themselves
Proposals, service reports, onboarding packets and renewal notices generated from your records and your own templates.
Find anything internal
Contracts, SOPs, manuals and past jobs indexed for retrieval, so the answer comes from the document rather than from whoever remembers it.
Research, idea generation and trading signal work for hedge funds, RIAs and family offices is a different build with different controls — see Custom AI Bots.
Deployment Models
Where your data lives is a design decision.
Not every workflow needs cloud AI. We match the deployment model to your data sensitivity — local for sensitive data, cloud for general tasks, hybrid for most businesses.
Local LLM
- Data location
- Your hardware
- Privacy
- Maximum
- Cost model
- Low at scale
- Best for
- Sensitive ops data
Hybrid
- Data location
- Split by sensitivity
- Privacy
- Strong
- Cost model
- Balanced
- Best for
- Most businesses
Cloud AI
- Data location
- Provider servers
- Privacy
- Standard
- Cost model
- Pay per token
- Best for
- General tasks
Agent Stack
The tools we actually deploy.
We don't build agents from scratch — we integrate the right combination of proven platforms and models for your specific workflows.
AI Bots (OpenClaw / Hermes Agent)
No framework lock-inThe agent frameworks we build on. Both connect to your business systems and surface operational insights — deployable on-premise, cloud, or hybrid, for businesses that need AI-powered reporting without sending data to a third-party cloud. We are not tied to one framework: we run OpenClaw and Hermes Agent, and will build on whatever your stack standardizes on.
Claude
Anthropic's reasoning model for document analysis, complex Q&A, and customer-facing AI workflows. Deployed via API or Azure.
Codex / Computer-Use
Code generation and computer-use agents for automating repetitive desktop, browser, and back-office workflows.
Open Harness
Agent orchestration layer that connects multiple AI models to your data pipelines with routing, fallback, and rate control.
Ollama
Runs Llama, Mistral, Phi, and other open-source LLMs entirely on your hardware. Your data never leaves your network.
LangChain
Framework for building LLM-powered workflows connected to your databases, APIs, and document stores.
LlamaIndex
Indexes your business documents for semantic search and retrieval-augmented generation (RAG) — find anything in your internal knowledge base.
Investment
AI agent deployment pricing.
Projects scope to your actual use case. Most businesses start with a single workflow and expand from there.
| Engagement | Starting From | Typical Range |
|---|---|---|
| AI workflow readiness assessment | $3,500 | $3,500–$8,000 |
| Local LLM setup (Ollama / vLLM) | $5,000 | $10,000–$25,000 |
| Focused AI agent pilot | $15,000 | $15,000–$60,000 |
| Full production AI stack | $50,000 | $50,000+ |
| Specialist consulting | $175/hr | $175–$275/hr by labor category |
Ranges exclude third-party licenses, hardware, cloud consumption, and managed operations unless listed in the signed scope.
Infrastructure Stack
The components under the hood.
Open-source, containerized, auditable. No vendor lock-in — you own the stack.
| Layer | Technology |
|---|---|
| Local LLM Runtime | Ollama, vLLM, llama.cpp |
| API Proxy | LiteLLM (OpenAI-compatible) |
| Agent Orchestration | LangChain, Open Harness |
| Document Intelligence | LlamaIndex, pgvector, Qdrant |
| Cloud AI (opt-in) | Azure AI Foundry, AWS Bedrock |
| Containerization | Docker, Kubernetes |
| Open Models | Llama 3, Mistral, Phi-3, Gemma, DeepSeek |
30-Day Deployment
A working agent in four weeks.
We scope tightly enough to ship one real workflow inside 30 days. No slide decks, no pilots that never end.
Week 1
Workflow audit
We map the manual workflows, classify your data sensitivity, and choose the narrowest automation target worth shipping first.
Week 2
Stack selection & data connection
We select the deployment model (local, hybrid, or cloud), connect your business systems, and define the clean data layer the agent depends on.
Week 3
Prototype build
We deploy a working AI agent with live inputs, outputs, and the controls your team needs to trust and verify the results.
Week 4
Handoff & next steps
We document ownership, human review checkpoints, and the improvement roadmap so the agent survives after we hand off.
Service Area
Greater Los Angeles & Orange County
Common Questions
Frequently asked.
Do I need to send my business data to OpenAI or another cloud provider?
Not necessarily. We deploy local LLMs — including Ollama running Llama and Mistral — entirely on your hardware. Your invoices, customer records, and internal documents never leave your network. For tasks where cloud AI adds value, we use a hybrid model that routes only non-sensitive data externally.
What deployment model is right for my business?
Most businesses start with a hybrid approach: local LLM for sensitive operational data, cloud AI for general-purpose tasks. In week one we assess your workflows and data sensitivity, then recommend a split that balances privacy, cost, and capability.
Which AI agents do you actually deploy?
We deploy agent workflows on OpenClaw or Hermes Agent — we support both and are not tied to either — Ollama or vLLM for local inference, LiteLLM as an OpenAI-compatible proxy, LangChain for workflow orchestration, LlamaIndex for document indexing and RAG pipelines, and Claude or Codex for specific reasoning and automation tasks. All containers run on Docker or Kubernetes. The stack is tailored to your use case in the first week.
How much does AI agent deployment cost?
Readiness assessments run $3,500–$8,000, local LLM setups start from $5,000, and focused agent pilots run $15,000–$60,000. Full production stacks start from $50,000. Specialist consulting is $175–$275 per hour by labor category.
What is the difference between a local LLM and a cloud AI service?
A cloud AI service (ChatGPT, Claude API, Gemini) processes your prompts and documents on the provider's shared infrastructure. A local LLM (Ollama, vLLM, llama.cpp) runs entirely on hardware you control — on-premise or in a dedicated cloud tenant. For businesses handling client data, contracts, or proprietary information, local deployment means zero data egress and no terms-of-service exposure.
How long does it take to deploy a working AI agent?
A basic proof-of-concept with a local LLM and one connected data source typically runs four weeks: one week for workflow audit and stack selection, one week for data connection and model configuration, one week for prototype build, and one week for handoff and documentation. Full production deployments with multiple agents and custom integrations are scoped individually.
New to Chadsel LLC?
Start with a readiness assessment.
A $3,500–$8,000 assessment defines the use case, data boundaries, deployment model, success criteria, and next-step budget before build work begins.