24/7
Answers, books, routes
30–60%
Projected cut in live operator calls (voice-agent pilot)
100%
Human-gated outreach
Yours
Self-hosted option: your server, your data
✓LLM chat & voice agents✓n8n workflow automation✓Self-hosted (Ollama) or OpenAI✓Slack · Teams · Workday · Twilio✓Data pipelines & scoring✓HIPAA-aware design
System blueprint
A single intelligent layer on top of everything you already run
Not a chatbot bolted to a website. A pipeline: your data goes in structured, a model reasons over it, a rule set decides, an action happens — and a human approves anything that leaves the building.
INPUT
Your data
CRM, docs, calendars, orders, open data
→
STRUCTURE
Ingest & normalize
n8n · NocoDB · Postgres
→
MODEL
LLM
Private Mistral on your server, or GPT-4.1
→
DECIDE
Structured output
JSON scores, rules, thresholds
→
ACT
Reply · book · route
Email, SMS, voice, tickets, alerts
→
GATE
Human approval
Nothing sent without a person when it matters
Live example
What “handled” looks like
An inbound request, answered, scheduled and confirmed — with a full trace you can audit.
[09:14:02] INCOMING channel=sms “Hey, can I book a detailing appointment for Saturday morning?”
[09:14:03] intent=book_appointment confidence=0.97 service=detailing window=sat_am
[09:14:04] calendar free_slots=[09:00, 10:30] policy=first_available
[09:14:05] action booked=Sat 09:00 confirmation=sent reminder=scheduled -24h
[09:14:05] crm contact=updated source=sms owner=alec
[09:14:06] status=HANDLED human_needed=no trace_id=7f3a…
Illustrative trace from a booking agent built for a mobile-detailing client.
What SIGNAL deploys
Practical AI, integrated end to end
Every deployment is built around one workflow that eats time or loses money — then extended.
01
Chat & voice agents
Custom LLM agents for your website, SMS and phone lines (Twilio) that answer FAQs, qualify leads, book appointments and route the rest to you — trained on your business.
02
Workflow automation
n8n and custom code connecting the tools you already use — Gmail, Sheets, WooCommerce, Microsoft 365, Slack, Teams, Workday — so data moves without copy-paste.
03
AI lead-generation engines
Public and open data ingested, filtered and scored by a model into structured JSON, pushed into your CRM, and released to outreach only after a human approves.
04
Knowledge & document AI
Retrieval over your own documents (FAISS / Chroma) for internal Q&A, grant research, product education and vendor onboarding — with role-based access.
05
Data pipelines & reporting
Automated syncing, recommendation engines and reports that arrive on a schedule instead of being built by hand.
06
Content & media automation
AI-drafted campaigns, product descriptions and even a podcast generated from a client’s recorded meeting — reviewed by you before it ships.
Impact
AG1 Growers: an AI platform for an agriculture network
AG1 Growers connects growers, vendors and research partners. Crossover architected and built its AI layer: a FastAPI service with FAISS / Chroma retrieval over Postgres, GPT-4.1 with an optional private model, and four modules — Grants, Products, Education and Internal — surfaced through a chat widget with role-based access for vendors and growers. The same stack drives automated outreach, vendor pricing and an AI-generated podcast from a recorded field meeting.
- Self-hosted production stack on the client’s own VPS: Docker, n8n, NocoDB, Ollama (Mistral 7B)
- Lead engine: public data → filtering → LLM scoring → CRM → human-gated outreach
- Role-based vendor and grower access; internal knowledge kept internal
- Grant and product research assistant used in university and agency conversations
StackFastAPI · Postgres · FAISS/Chroma
ModelsGPT-4.1 / 4.1-mini · Ollama Mistral 7B
Automationn8n · NocoDB · Twilio
HostingClient-owned VPS on CloudPanel
AccessRole-based: vendor · grower · internal
GovernanceHuman approval before outreach
Why it’s different
Built for your workflow, not rented by the seat
Off-the-shelf AI tools are fine until they need your data, your rules and your approval flow.
| Crossover SIGNAL | An AI subscription | |
|---|---|---|
| Data ownership | Runs on your server if you want; your data never leaves | Their cloud, their terms |
| Fit | Designed around one real workflow, then extended | Generic features you adapt to |
| Guardrails | Structured outputs, thresholds, human gates, full traces | A checkbox, if that |
| Integration | Wired into CRM, calendar, phones, M365, WooCommerce | Whatever the connector list allows |
| Cost model | Build once; pay for tokens or run a private model for free | Per-seat forever |
How it works
From one painful workflow to a working agent
You see a working prototype before you commit to a full build.
01
Map
We pick the one workflow that costs the most time or leaks the most leads, and define what “handled” means for it.
02
Prototype
A working agent or automation on real data within days — so you can judge it on results, not slides.
03
Deploy
Hardened, integrated and monitored — on your VPS or in the cloud — with approval gates and audit trails.
04
Tune
Prompts, rules and thresholds tuned on real traffic; new workflows added as the first one pays for itself.
Free · no strings
Free automation audit
Walk me through a normal week. I’ll send you one specific workflow you can automate immediately — with the tools, the steps and what it would save — whether or not we build it together.
Questions
Frequently asked
Q.Will the AI make things up or say something wrong to a customer?
It can, which is why deployments use structured outputs, confidence thresholds and human approval gates for anything consequential. Agents answer from your approved knowledge, escalate when unsure, and every action is logged with a trace.
Q.Do we have to send our data to OpenAI?
No. A private model (Ollama / Mistral) can run on your own server for sensitive workloads, with cloud models used only where their quality is worth it. You choose per workflow.
Q.What does it integrate with?
Websites, SMS and phone lines (Twilio), Gmail and Google Sheets, Microsoft 365, Slack, Teams, Workday, WooCommerce, CRMs and anything with an API or webhook — via n8n or custom code.
Q.How long does it take?
A prototype on real data typically lands within days; a hardened deployment with integrations and monitoring inside a few weeks, depending on the systems involved.
Q.Can this be used in healthcare or other regulated settings?
Yes, with the right design: self-hosted models, minimum-necessary data, role-based access, retention rules and audit logs. Every deployment starts with a data-flow map you can hand to compliance.
Ready to put AI to work in your business?
Let’s talk about where automation and AI can save you time and money — and build something around your actual workflow.
