S.E.E.R

Inspiration

Most camera analytics systems are either:

expensive
over-engineered
slow to customize
or require ML engineers to operate

Small businesses can’t afford that. Even enterprises routinely deploy the wrong models — wasting compute, increasing latency, and locking themselves into bloated pipelines.

We asked a simple question:

Why can’t you just describe what you want monitored… and have the system build itself?

That became S.E.E.R — a prompt-to-deployment camera intelligence platform with built-in emergency response.

What it does

S.E.E.R turns natural language into a live, production-style camera pipeline.

Example prompt:

“Detect shoplifting in high-value aisles and call for help if a fight breaks out.”

S.E.E.R automatically:

selects appropriate computer vision models
wires them into an end-to-end pipeline
deploys real-time inference workers
configures alerts and analytics
enables emergency escalation via outbound voice calls

Supported capabilities:

camera ingestion & stream management
model discovery + selection
detection, tracking, OCR, activity recognition
dwell time & behavior analytics
event routing (webhooks, notifications, clips)
emergency escalation (Twilio outbound calling)

Use cases include:

Retail – shoplifting + violence detection
Factories – worker injury detection
Parking lots – collision / hit-and-run detection
Healthcare rooms – fall & immobility detection

How we built it

We designed S.E.E.R as an orchestration system:

Prompt → structured monitoring plan
Task decomposition (detection, tracking, analytics, alerts)
Model discovery for each task
Cost/latency/accuracy-aware model selection
Pipeline compilation
Deployment across inference workers
Real-time event routing + escalation actions

We used modular MCP servers for:

camera tools
model tools
inference tools
alerting tools
analytics tools
storage tools
voice escalation tools

The result: intent → deployed system in minutes.

Challenges we ran into

Live emergency calling

Connecting LiveKit to Twilio required:

SIP trunking setup
call routing & termination logic
real-time audio bridging
handling silent failures from misconfiguration

This was one of the hardest parts to get stable.

Model deployment at runtime

Selecting models is easy. Running them reliably is not.

We had to solve:

dependency packaging
GPU scheduling
inference latency under multiple streams
scaling workers dynamically

Model documentation ingestion

We used Firecrawl to scrape model documentation, but ran into:

limited credits
inconsistent formatting
oversized pages

We added caching, targeted crawling, and fallback strategies.

Accomplishments that we're proud of

Prompt → deployed camera pipeline working end-to-end
Automatic model selection based on cost & latency
Real-time multi-service orchestration
Working emergency voice escalation (actual phone calls)
Fully built during the hackathon timeframe

What we learned

Orchestration is harder than model accuracy
Real-time systems fail in non-obvious ways
Emergency workflows require extreme reliability
Most businesses don’t need “better models” — they need better deployment

What's next for S.E.E.R

Behavior-based shoplifting detection
(concealment gestures, distraction teams, rapid exits)
Human-in-the-loop verification
(confirm / dismiss alerts before escalation)
Multi-camera re-identification
(track behavior across entrances, aisles, and exits)
Cost-aware auto-optimization
(continuously swap models as usage patterns change)