# Allerin -- Full Description

> Enterprise AI and computer vision company delivering production-ready solutions across manufacturing, logistics, retail, insurance, transportation, and energy. 19 industry-specific solutions, 4 flagship products, and 18 engineering services. Founded 2005, headquartered in Austin, Texas. KPI-gated rollouts with measurable outcomes before scaling.

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## Products (4 Flagship Platforms)

### VISTA -- Enterprise Computer Vision Platform
ALPR alerts and compliant video and audio redaction with audit trails for cities, campuses and police records teams; also run by fleets, corporate security, logistics operations and retail loss prevention.

Capabilities:
- Hotlist/watchlist alerts with reviewer queues and dispositions
- Fleet management and logistics security: loading dock monitoring, distribution center access control
- Aviation/Airport: airside perimeter security, rental car facility monitoring, parking structure surveillance
- Bulk redaction (faces/plates/objects/audio) for compliance, FOIA, and incident response
- Chain-of-custody, access logs, and configurable retention controls
- Evidence packaging and export for investigations and insurance claims
- API-first: CAD/RMS, VMS, SSO/IAM, storage, notification, ERP integration
- Investigator search: plate/vehicle metadata, geo-temporal pivots

Outcomes:
- Watchlist alert review time: down 40-60%
- Redaction turnaround: down 50-70%
- Critical CVEs to 0 before go-live (with OWASP sweep)

Deployment: On-premise, cloud, edge, or hybrid. VMS integration via REST/webhooks. SSO/IAM (OIDC/SAML). S3-compatible storage.
Security: GDPR, HIPAA, CCPA, SOC 2 compliance. TLS 1.3 in transit, AES-256 at rest. RBAC with audit trails. SBOM + SAST/DAST evidence.
Industries: Enterprise, Retail, Logistics, Transportation, Manufacturing


### SENTRA -- Traffic Analytics
Edge traffic analytics for DOTs, MPOs and cities: counts, classification, speed and incident detection; also deployed at warehouses, distribution centers, corporate campuses and manufacturing facilities.

Capabilities:
- Real-time counts, speed, and FHWA class at the edge (Jetson/x86)
- Aviation/Airport: curbside flow optimization, ground transportation analytics, terminal access monitoring
- Warehouse and Logistics: loading dock optimization, yard management, trailer tracking
- Corporate Campus: traffic flow analysis, parking guidance, employee shuttle optimization
- Incident detection (stopped vehicle, wrong-way, queue build-up, congestion alerts)
- ArcGIS/LRS conflation; shapefiles/feature classes export
- Live dashboards and scheduled study packs
- Hybrid edge-to-cloud sync and statewide batch inference
- Model monitoring and drift alerts; retrain hooks

Outcomes:
- Vehicle classification accuracy: 98%+ on tuned sites
- Incident detection latency: under 1-2 seconds (tuned per site)
- Cloud GPU/egress spend: down 20-40% with edge batching

Deployment: Edge devices (Jetson/x86). Kubernetes microservices. ArcGIS/feature classes export. Statewide batch inference support.
Security: Encrypted telemetry and data transmission. Role-scoped access controls. Optional anonymization for public data portals.
Industries: Logistics, Manufacturing, Enterprise, Transportation, Retail


### iPAM -- Municipal Parking Management
Parking management software for towns and cities. Camera-supported lots, free parking limits and extensions, permits and violations, with the rules and automation the city configures.

Capabilities:
- Virtual permits and waitlists; pro-rating and rules engine
- Airport Parking: terminal/economy lot management, reservation systems, loyalty programs
- Corporate Campus: employee parking management, visitor registration, EV charging integration
- Retail: customer parking validation, loyalty rewards, holiday/peak pricing
- Enforcement from a phone; e-chalking and no-return blockface rules
- Citations, disputes, violation management and appeals workflows
- Event and valet modules
- Analytics and scheduled reporting; open APIs/webhooks
- End-user portal; payments and receipts; mobile app notifications

Outcomes:
- Permit adoption: up 10-25% with self-service and virtual permits
- Violator capture: up 15-35% with e-chalking policies
- Dispute handling time: down 30-50% with evidence packaging

Pricing (published; flat and public, so every town of the same size pays the same price. Purchasing follows the town's own policy: written quotes provided the same day, and Allerin takes part in a formal bid where the policy calls for one):
- Core: $7,500/yr. Towns under about 10,000 residents with one enforcement zone. Virtual permits, phone-based enforcement with no hardware to buy, digital citations with online payment, resident portal, standard reports, onboarding and support included.
- Standard: $14,500/yr. Towns of 10,000 to 30,000 running permits plus active enforcement. Everything in Core plus waitlists, pro-rating, the rules engine, disputes and appeals with evidence packaging, merchant validation, up to 5 zones, scheduled reporting.
- Full: $19,500/yr. Towns and small cities of 30,000 to 75,000 with multiple zones or seasonal demand. Everything in Standard plus event and valet modules, open APIs and webhooks, unlimited zones, priority support.
- Enterprise: custom quote. Cities over 75,000, counties, parking authorities, airports, universities, multi-jurisdiction deployments. Custom modules, on-premise or dedicated cloud, ERP integrations (SAP, Oracle, Munis), parking hardware rollout support, SLA commitments, dedicated success manager.
Terms: 12-month term, renews annually, cancel with 60 days notice. Purchase orders and ACH accepted, Net 30. Onboarding included in year one. No hardware required to start.
Outcome ranges above are indicative, not a guarantee; actual results depend on a town's parking volume, current process and policy.

iPAM Small Communities Program (https://www.allerin.com/products/ipam/small-communities): a free first year of iPAM for eligible small US municipalities, part of Allerin's community engagement work. Eligibility, the scope of the award and the terms that follow the first year are agreed with each town directly and sent in writing before anything is activated; nothing about eligibility thresholds, allowances or renewal pricing is public. The award is a 12-month software subscription with the onboarding and standard support of the Core tier, sized for a small-town operation. Included functions: free-parking limits and extensions; seasonal and time-based parking rules; administrator-controlled automation (the town chooses which supported actions run on their own and which staff review); permits, applications and renewals with active-permit tracking; enforcement from an officer's phone with digital citations and online payment; lot availability and temporary closures; permit-renewal reminders and operational notices; parking reports on permit activity, violations, citations and payments; basic scheduled reports (recurring standard reports to parking administration and finance; the full scheduled-reporting set is on the Standard tier); basic event parking scheduling (requests to use municipal lots by location, date, hours and spaces requested; the full event module is on the Full tier); a resident portal with payments and receipts; and configured parking rates by time or location where parking is paid. Nothing beyond that list is implied; the scope of a basic item is set out in the written terms sent to applicants. Camera-supported lots (occupancy, arrival and departure tracking, parking duration, matching against permits or payments) are scoped separately and are not part of the award; hardware, installation, connectivity and payment processor fees are outside the award. It is an agreement with a written 12-month term, not a donation. Paid parking, meters, a minimum permit count and citation revenue are not criteria. Whoever runs parking day to day and can apply on the town's behalf (a clerk, administrator, chief or public works lead) applies online; estimates are acceptable.

Deployment: Cloud-hosted or on-premises. Mobile enforcement app (iOS/Android). Parking hardware integration. PCI-compliant payment gateway. Finance/ERP data feeds.
Security: RBAC with audit trails. SSO (SAML/OIDC). PCI DSS alignment. Configurable data retention. WCAG 2.1 AA.
Industries: Enterprise, Retail, Healthcare, Logistics, Manufacturing


### NeuroSight -- AI Weapon Detection
Sub-200 ms firearm detection and behavior cues at the edge for campuses, venues and public buildings; also protecting retail stores, corporate headquarters, manufacturing facilities, financial institutions and healthcare facilities.

Capabilities:
- Firearm detection and behavior analytics (loitering, raised-arm cues, crowd anomalies)
- Aviation/Airport: terminal security, checkpoint monitoring, baggage claim, parking structure surveillance
- Retail Security: storefront monitoring, loss prevention integration, customer safety alerts
- Corporate and Manufacturing: facility entrance monitoring, sensitive area protection, workplace violence prevention
- Human-in-the-loop review queue and alert confirmation with prioritization
- Integrations: VMS, access control, mass notification, incident management systems
- Device health, stream quality, and alert reliability metrics
- Model monitoring, drift and threshold tuning per environment
- Privacy modes and masking tools with audit compliance

Outcomes:
- Detection latency: under 200ms (edge pipeline)
- Reviewer load: down 30-50% with triage and event bundling
- NDAA-compliant camera posture for public procurements

Deployment: On-prem edge devices (Jetson/x86) or hybrid cloud. VMS integration via API/SDK. Access control and mass notification webhooks. Air-gap deployment options.
Security: TLS 1.3, AES-256. RBAC with audit trails. NDAA-compliant hardware options. Privacy modes with configurable masking.
Industries: Enterprise, Retail, Manufacturing, Healthcare, Financial Services

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## Solutions (19 Industry-Specific)

### Manufacturing

#### QualiZer -- In-Line Defect Detection
Reduce scrap and rework by detecting defects at the line with edge CV and explainable reviews.

Capabilities:
- Real-time defect cues on conveyor/fixture views
- Reviewer console with side-by-side frames and reason codes
- Trend by SKU/shift/tool; alerts to MES/Andon
- Evidence export for supplier claims and training
- Optional AOI/3D fusion where available
- In-line detection at 200+ FPS
- Edge-first architecture on NVIDIA Jetson or industrial PCs
- Native MES/SCADA integration (Siemens, Rockwell, Ignition)

Outcomes:
- Scrap/rework: down 10-25%
- Inspection time: down 30-60%
- False rejects: down 10-30%

Deployment: Edge (Jetson/x86), industrial cameras (GigE, USB3). MES/ERP via REST APIs.
Timeline: PoC to Pilot 8-12 weeks with KPI gates.


#### VibraQore -- Predictive Maintenance
Detect bearing/imbalance faults early and plan interventions to avoid unplanned downtime.

Capabilities:
- Multi-axis vibration intelligence (X, Y, Z axes with FFT analysis, 100Hz to 20kHz)
- 14-day failure forecasting with ML models trained on historical data
- Native CMMS and EAM integration (SAP PM, IBM Maximo, Fiix, UpKeep)
- Sensor fusion: vibration + current + thermal
- Asset health scoring and remaining-useful-life estimation
- Parts and labor forecasting
- Drift and seasonality monitoring

Outcomes:
- Fewer unplanned breakdowns: down 35%
- Average failure warning: 14 days
- Maintenance ROI: up 3x

Industries/Equipment: Rotating equipment, gearboxes and drives, CNC machinery, HVAC and utilities.
Deployment: Vibration sensors + edge compute. CMMS hooks for work orders.
Timeline: Sensor+vision baseline, alert rules, CMMS hooks.


### Warehousing & Logistics

#### StockVexel -- Autonomous Inventory Scanning
Nightly cycle counts and OSA/shrink visibility using drone/AMR scanning -- no aisle closures.

Capabilities:
- Corridor scans to shelf map; OCR/barcode reconciliation
- Exception list to handhelds; photos for proof
- WMS reconcile and tasking API
- Night shift friendly; safety interlocks
- AMR/drone path planning and collision avoidance

Outcomes:
- Faster cycle counts: up 40%
- Inventory accuracy: up to 99.2%
- Stock reconciliation: under 5 minutes

Deployment: AMR/drone integration. WMS adapters via REST/webhooks.
Timeline: PoC to Pilot 8-12 weeks with KPI gates.


#### YardLytix -- Dock & Yard Analytics
Reduce door dwell and improve trailer turns with camera+OCR and a live yard map.

Capabilities:
- Gate OCR (98%+ accuracy) with event timeline
- Live yard map with slot status and heatmaps
- SLA breach alerts to ops (SMS, email, TMS dashboard)
- TMS/YMS integration (SAP TM, Blue Yonder, Oracle TMS, Manhattan)
- Real-time trailer location tracking
- Bi-directional sync for appointments and carrier updates

Outcomes:
- Dwell time reduction: down 15-30%
- Faster trailer turns: up 2x
- Annual detention savings: $50K+

Deployment: Gate cameras with OCR. Edge devices (Jetson/x86). TMS/YMS adapters.
Timeline: Gate OCR pack, yard map + KPIs, TMS/YMS hooks.


### Retail

#### ShelfSentra -- Shelf Compliance & OSA
Detect out-of-stocks and planogram drift in-aisle; push tasks to devices and measure recovery time.

Capabilities:
- OOS and planogram variance detection
- Task push to handhelds with SLA timers
- Recovery measurement and leaderboards
- Privacy-preserving analytics (no PII)
- Real-time shelf compliance scoring

Outcomes:
- OSA: up 2-6 percentage points
- Task recovery time: down 20-40%
- Conversion lift: up 3-5%

Deployment: In-aisle cameras. Handheld device integration. ERP/WMS adapters.
Timeline: In-aisle models, tasking API, KPI dashboards.


#### Queuence -- Queue & Staffing Analytics
Predict queues, alert lane opens, and cut walk-offs with staffing hooks.

Capabilities:
- Real-time queue length and wait prediction (85-90% accuracy)
- Smart alerts to managers and signage
- Workforce management integration (Kronos, ADP, Legion)
- Anonymized video analytics (no facial recognition, no PII)
- Abandonment tracking and trend analysis

Outcomes:
- Wait time: down 20-40%
- Walk-offs: down 15-30%
- Prediction accuracy: 85-90%

Deployment: Existing security cameras for queues. Edge inference. Cloud sync for reporting.
Privacy: GDPR and CCPA compliant by design.


### Insurance & Financial

#### ClaimSentra -- Roof/Property Claims Triage
Accelerate claim triage using aerial damage scoring and assessor routing with defensible evidence.

Capabilities:
- Aerial image ingest; roof/property scoring
- Route and priority suggestions
- Evidence packs for adjusters
- Public records redaction (paired with VISTA)
- Change detection for disaster response

Outcomes:
- Claim cycle: down 30-50%
- Desk settlements: up 35%
- Re-inspection rate: under 5%

Integration: Xactimate, Guidewire, Duck Creek.


#### ClaimVista -- Claims Automation & Adjudication
Reduce claims cycle time and improve adjuster productivity with AI-powered triage and data extraction.

Capabilities:
- Auto-triage claims by complexity, fraud signals, and urgency
- OCR extraction of policy/loss data from photos, PDFs, and forms (95%+ accuracy)
- Pre-filled adjuster worksheets with validated data
- Fraud signal detection with 50+ red flag rules and SIU queue routing
- Duplicate detection, document authenticity analysis, network analysis

Outcomes:
- Cycle time: down 30-50%
- Data entry: 8 minutes saved per claim
- Extraction accuracy: 95%+

Integration: Guidewire, Duck Creek, Applied Epic.


#### CompliChek -- Policy Form Compliance Validation
Validate insurance policy forms against state-specific regulations before SERFF filing.

Capabilities:
- Validate policy documents against state/federal regulations (all 50 US states)
- Flag missing disclosures, outdated clauses, and non-compliant language
- Audit trail generation for SOC 2, HIPAA, FedRAMP compliance
- Version control and change tracking
- Regulatory database auto-updates with jurisdiction rules
- Issue resolution workflow with SLA tracking and approval chain

Outcomes:
- Faster audit prep: down 40-60%
- Fewer SERFF objections: down 85%
- Validation accuracy: 98%+


#### DocQore -- Government Document Processing
Digitize paper and microfilm archives. Make 15 years of records searchable in 90 days.

Capabilities:
- Legacy format conversion: 16mm, 35mm roll film, COM microfiche, aperture cards, bound volumes, handwritten records
- Advanced preprocessing: deskew, despeckling, contrast enhancement
- NLP-powered metadata extraction and tagging
- Exemption-aware PII redaction with FOIA exemption codes
- Searchable archive with full-text indexing
- Batch processing with quality gates and human-in-loop validation

Outcomes:
- Throughput: 1,000+ pages/hour
- OCR accuracy: 95-98% (98%+ typewritten, 95%+ handwritten)
- Searchable archive: 60-90 days
- Capacity: 1 million+ pages in 90 days


#### FraudLens -- Insurance Fraud Detection
Score every claim at FNOL in milliseconds. Flag suspicious claims before payout.

Capabilities:
- Anomaly detection on claim patterns, networks, and submission behavior
- Image forensics: pixel-level analysis, AI-generation detection (GANs, DALL-E, Midjourney), metadata forensics
- Fraud ring detection: entity relationship mapping, network graphs for SIU investigation
- SIU workflow integration with investigation queue
- External database lookups (ISO, NICB, fraud bureaus)
- Explainable scoring with evidence packaging

Outcomes:
- Detection: 3x more fraud vs rules-based systems
- Resolution: 50% faster SIU case closure
- Precision: 75% fewer false positives


### Transportation & Rail

#### RailAurex -- Track & Catenary Inspection
Detect defects from video/LiDAR and prioritize work orders for maintenance of way.

Capabilities:
- Multi-sensor defect detection: video, thermal, LiDAR from inspection cars
- Rail cracks, gauge anomalies, fastener defects, tie degradation, catenary wear
- Geo-tagged findings with GIS overlay (ArcGIS, QGIS)
- Automated work order export to EAM (Maximo, SAP PM, Infor EAM)
- Model accuracy and QA dashboards with confusion matrices

Outcomes:
- Inspection time: down 30-50%
- Defect detection: 95%+
- Hazardous misses: target zero

Deployment: Edge devices during route traversal. No connectivity required for real-time processing.


#### RailGuard -- Right-of-Way Monitoring & Clearance
Automated vegetation encroachment detection and clearance violation alerts with GIS mapping.

Capabilities:
- Vegetation encroachment detection with GPS mapping
- Structure gauge and clearance monitoring (AAR Plate B-K standards)
- Seasonal intelligence: growth rate tracking, herbicide effectiveness, storm damage detection
- Catenary wire proximity monitoring for electrified territories
- Work order generation with severity scoring and priority routing
- GIS layer export for corridor planning

Outcomes:
- Survey time: 35-45% faster (weeks to days per subdivision)
- Detection: 60-80% more encroachments vs manual patrol
- Costs: 40-55% lower with targeted crew dispatch

Compliance: FRA 49 CFR 213.321 vegetation compliance mandate.
Deployment: Mobile (hi-rail vehicles), fixed, or revenue service.


#### TrackSentinel -- Surface Defect Detection
AI-powered inspection for rail surface conditions, tie health, fastener integrity, and ballast indicators.

Capabilities:
- Rail surface defects: head checking, squats, visible cracks
- Tie condition grading (wood and concrete)
- Fastener anomaly detection
- Ballast surface assessment
- FRA-compliant documentation

Important scope: Camera-based AI for SURFACE defects only. Internal rail defects require ultrasonic testing and are outside detection scope.

Deployment: Edge processing on inspection vehicles.
Compliance: FRA Part 213 track safety standards.


#### FleetTherm -- Rolling Stock Thermal Monitoring
Predictive thermal anomaly detection for wheels, bearings, and brakes with depot integration.

Capabilities:
- Complete thermal profiling at rest (depot-based)
- Complements wayside Hot Box Detectors (point-in-time at line speed)
- Component-level tracking and trending
- Pre-failure alerts with maintenance scheduling

Target markets: Transit and Metro, Commuter Rail, Light Rail, Passenger Rail, Industrial/Port fleets.
Deployment: Depot-mounted thermal arrays. CMMS integration.


#### SignalEye -- Signal & Switch Monitoring
Automated signal aspect verification and switch position confirmation with PTC integration.

Capabilities:
- Signal aspect verification from wayside cameras (red/yellow/green)
- Switch position confirmation with anomaly alerts (points alignment, lock status)
- PTC integration for automated fault reporting (Wabtec I-ETMS, Alstom ACSES)
- Trend analysis for signal degradation patterns
- Alert escalation by signal criticality and location

Outcomes:
- Signal lamp degradation: 50-70% earlier detection vs monthly walking inspection
- Switch maintenance alerts: 40-60% proactive identification
- Fault documentation time: down 60-75%

Deployment: Wayside cameras, 24/7 monitoring. Edge-deployed models.


### Energy & Utilities

#### GridAurex -- Transmission Line Inspection
Anomaly detection from drone/helo feeds for line, tower, and substation inspections.

Capabilities:
- Anomaly detection: corrosion, vegetation encroachment, hot spots
- Route-based review queues with priority scoring by severity and location
- Work order export to EAM/CMMS with GPS coordinates and evidence packs
- GIS-based asset mapping for targeted inspections
- Integration with drone platforms (DJI, senseFly)

Outcomes:
- Inspection cycle time: down 45% (6 months to 3.3 months for full system patrol)
- Defect detection accuracy: 90-95% F1 score across 8 categories
- Flight costs: down 30-40%
- Review time: down 70%

Validated on: 2,400 miles of 345kV, 50,000+ images.


#### HelioLytix -- Solar PV Thermal Monitoring
Hot-spot and underperforming panel detection from thermal imagery, prioritized by revenue loss.

Capabilities:
- Hot-spot detection and panel string mapping with production impact estimates
- IEC TS 62446-3:2017 compliance for outdoor infrared thermography
- CoA-based severity classification (Minor/Serious/Critical)
- Revenue-prioritized maintenance work orders
- Integration with monitoring systems (SolarEdge, Huawei, etc.)
- CMMS integration (SAP PM, Maximo)

Outcomes:
- Production recovery: 2-8% (up to 12% on severely degraded sites)
- Anomaly detection: 94% F1 score (validated on 25,000+ module images across 40+ sites)
- Review time reduction: down 70%

Standards: IEC 62446-3 outputs valid for warranty claims, insurance documentation, investor due diligence, EPC commissioning.


#### ThermaSentinel -- Leak/Flare Monitoring
Methane and flare anomaly detection from thermal/OGI sensors with automated regulatory reporting.

Capabilities:
- Methane leak detection from thermal/OGI sensors
- Flare monitoring with combustion efficiency and visible emissions tracking
- Multi-frame validation to filter false positives
- EPA/LDAR-compliant report generation with audit trails
- Alert prioritization by severity and regulatory risk
- Integration with field maintenance systems

Outcomes:
- Leak detection time: down 60%
- Regulatory compliance: 100%
- False positives: down 40-50%

Compliance: EPA OOOOa/OOOOb/OOOOc methane regulations, LDAR requirements.

---

## Services (18 Engineering Services)

### Agentic AI Systems
Multi-agent automations with guardrails and HITL review.

What you get:
- Orchestration graph in code (agents, tools, policies) with retries/timeouts
- Evals suite: jailbreak, toxicity, groundedness, accuracy gates
- Guardrails and safety gates enforced in CI and runtime
- Reviewer console (approve/annotate/retry) with audit trail
- Run log and trace viewer (inputs, prompts/versions, tool calls, outputs)
- Budget caps and alerts; cost per transaction export

Outcomes:
- Eliminate manual back-office steps
- Reviewer minutes per case: down 30-50%
- Cost per transaction capped and reported
- Agent accuracy: 74% to 94% (+20%)
- Human review time: 8hr/day to 45min/day (89% efficiency gain)

Timeline: a working agent in 4-6 weeks, production in 8-12 weeks.
Team: Architect, MLE, FE, BE, PM; security reviewer on demand.
Tech: LangGraph/Temporal/Argo; OpenAI/Claude/local models; SSO (OIDC/SAML); on-prem/GovCloud or VPC.


### GenAI Product Accelerator
RAG features shipped to production safely.

What you get:
- Vector pipeline + knowledge ingestion (automated re-indexing)
- RAG orchestration layer with prompt versioning and fallbacks
- Evals suite: accuracy (exact-match + semantic), hallucination gates, toxicity filters
- CI/CD integration with regression gates (accuracy thresholds)
- Observability dashboard: usage, cost per query, latency p95
- Safety monitoring: PII detection, content filters, rate limits

Outcomes:
- Working RAG feature in prod with accuracy at or above target
- Production RAG hallucination rate under 3%
- Time to market: 6 months to 6 weeks (4x faster)
- Prompt accuracy: 68% to 91% (+23%)

Timeline: 4 weeks (MVP), 6 weeks (production-ready with full evals and monitoring).
Tech: Pinecone/Weaviate/pgvector; OpenAI/Claude/Gemini/Llama; LangSmith/Phoenix.


### Computer Vision FastTrack
PoC-to-production pipeline on edge/cloud.

What you get:
- Custom model trained on your footage (detection/tracking/classification)
- Edge deployment package: ONNX/TensorRT optimized for Jetson/x86/ARM
- Inference pipeline with target latency (typically 60-200ms p95)
- Precision/recall benchmarks on test set with confusion matrices
- MLOps workflow: drift monitoring, review UI, re-labeling, retraining hooks
- Production runbook: deployment, rollback, troubleshooting, scaling

Outcomes:
- Model accuracy: 82% to 96% (+14%)
- Inference latency: 850ms to 120ms (7x faster)
- Reviewer minutes/event: 3.8 to 1.9 min
- Measurable reduction in manual review time (typically 87%)

Timeline: 4 weeks (PoC), 8 weeks (production-ready with MLOps).
Tech: YOLOv8/v11, ONNX/TensorRT, INT8 quantization, NVIDIA Jetson, Docker/K3s.


### Data & Analytics Platform
GIS + KPI dashboards with a data-quality spine.

What you get:
- Data connectors with retry logic and monitoring (source to warehouse)
- KPI catalog with definitions, owners, refresh schedules, and SLAs
- Data quality pipeline: profiling, validation rules, alerts on critical failures
- GIS-enabled dashboards with zoom, filter, layer controls, and export
- Narrative report generator with automated summaries and anomaly detection
- Runbook: troubleshooting, scaling, adding KPIs, data refresh procedures

Outcomes:
- Query performance: 45s to 1.2s (97% faster)
- Data pipeline SLA: 82% to 99.7%
- Report prep time: 8 hours to 2 hours (75% savings)

Timeline: 3-6 weeks.
Tech: Snowflake/BigQuery/Redshift; ArcGIS/Mapbox/PostGIS; Tableau/Power BI/Looker; Great Expectations/dbt.


### MLOps & Model Operations
Model registration, quality evaluation and rollback with compatible model, feature and serving-configuration versions.

What you get:
- Release records connected to artifact identities, provenance and evaluation evidence
- Separate observations of input drift, labeled prediction quality and operational failure
- Canary criteria with explicit label coverage and hold conditions
- Rollback validation, recovery checks and an incident runbook scoped to the actual deployment

Inspect the runnable synthetic demonstration at https://www.allerin.com/resources/model-rollback-demo and its source/tests/logs at https://www.allerin.com/downloads/model-rollback-demo.zip. It tests local failure decisions and compatible recovery, not distributed availability, a production SLA or a customer's outcomes.

Scope follows a review of artifacts, feature pipelines, label availability and serving topology. Review current assurance evidence with its scope and reporting period; the example is not a certification.


### Platform Modernization
Modular monolith first; APIs; observability and cost.

What you get:
- Modernization blueprint with target architecture and migration phases
- Dual-run infrastructure with blue-green/canary deployment capability
- Observability stack: distributed tracing, structured logging, error budgets
- API gateway and module boundaries with contracts and versioning
- CI/CD pipeline with automated testing and rollback automation
- Security baseline: SBOM generation, SAST/DAST scanning, CSP/HSTS headers
- Cost plan: unit economics per request/service, autoscale policies, budget alerts

Outcomes:
- Deployment frequency: 1x/month to 12x/day (360x faster)
- Infrastructure cost: $85k/mo to $31k/mo (64% reduction)
- Error budget adherence: 72% to 99.2%

Timeline: 4-8 weeks.
Tech: Ruby/Rails, Node.js, Python, Java/Spring Boot, .NET; Docker/Kubernetes; Datadog/New Relic/Grafana.


### AI Security & Compliance
AI-specific security hardening with compliance frameworks.

What you get:
- AI threat assessment: prompt injection, model theft, data poisoning vulnerability analysis
- LLM security hardening: input validation, output filtering, jailbreak protection
- Model access controls: RBAC, API key management, usage monitoring
- AI-specific SBOM: model lineage, training data provenance, dependency tracking
- Compliance mapping: SOC 2, ISO 27001, HIPAA, NIST AI RMF alignment
- Continuous monitoring: anomaly detection, drift monitoring, incident response
- Audit evidence pack: security controls documentation, penetration test results

Outcomes:
- Production CVEs: 37 critical to 0 (100% elimination)
- Audit prep time: 6 weeks to 2 days (95% faster)
- SBOM coverage: 0% to 100%

Timeline: 4-8 weeks.
Tech: Lakera, Protect AI; SAST/DAST for ML pipelines; Arize, WhyLabs; Vanta, Drata.


### Product Pods
Dedicated pods + fractional leadership. Predictable throughput, zero-freeze onboarding.

What you get:
- Pod charter with ownership, scope, and escalation paths
- Weekly sprint demos, bi-weekly retrospectives
- Monthly roadmap health review
- Real-time backlog visibility with definition of ready/done
- Incident response SLOs: P0/P1 triage, post-mortem reports
- Transparent metrics: sprint completion, carry-over %, bug density, PR cycle time

Outcomes:
- Feature velocity: 2.1/sprint to 8.4/sprint (4x faster)
- Bug density: 12/kloc to 1.8/kloc (85% quality boost)
- Carry-over: 38% to 6%
- Time to production: 3.2 weeks to 1.1 weeks (66% faster)

Team: Pod Lead (1 FTE), Frontend (2), Backend (2), QA (1), PM (0.5).
Timeline: Ongoing; monthly or quarterly renewal.


### AI Platform & Orchestration
One control plane to design, run, observe, and govern multi-agent/LLM apps.

What you get:
- Orchestration graph with retries, timeouts, circuit breakers, and backoff logic
- Multi-model routing engine with cost/latency policies and automatic fallbacks
- Central run log with PII redaction, prompt versioning, and audit trails
- Guardrails blocking unsafe actions (jailbreak, toxicity, groundedness)
- Observability with traces, metrics, alerting
- SSO/SAML, RBAC, secrets management

Outcomes:
- Fraud screening cost: $0.18 to $0.04 per transaction (global bank, 8-agent pipeline)
- False positives: down 41% through model routing (same case)
- Full audit trace for FINRA and FinCEN

Timeline: 3-5 weeks.
Tech: LangGraph/Temporal/Argo; OpenAI/Claude/Gemini/Llama; Datadog/Grafana/OpenTelemetry.


### Rapid Prototyping Lab
Executive-ready prototypes + tech spikes in 2 weeks.

What you get:
- Clickable prototype covering 5-7 critical flows
- Tech spike code for riskiest section (RAG, CV, SSO)
- Risk register with cost/effort T-shirt sizing
- Clear decision: proceed, park, or pivot

Outcomes:
- Decision cycle: 6 weeks to 2 weeks (3x faster)
- Cost of wrong decision: $400k wasted to $18k prototype (96% cost avoidance)
- Feature validation rate: 40% unused to 85% adopted
- Risk identification: 20% to 90% risks mapped

Timeline: 2 weeks.
Team: Architect, UX, FE, MLE/BE (spike).


### Integration FastTrack
Production-ready connectors with test suites and docs.

What you get:
- Spec pack: sequence diagrams, auth scopes, OpenAPI/Protobuf/Avro/JSON schema
- Test harness: unit/contract/integration; replayable fixtures; mock servers
- Error taxonomy + retry/backoff and idempotency keys
- Reconciliation jobs + variance dashboards; backfill plan
- Runbooks: deploy/rollback, replay, DLQ handling
- Security: SAST/DAST/SCA clean; secrets in KMS; PII masking

Outcomes:
- Sync success rate: 91.8% to 99.7% (+7.9%)
- Integration time: 18 weeks to 10 weeks (44% faster)
- Data variance: 2.3% to 0.3% (87% improvement)

Timeline: 3-6 weeks.
Protocols: REST/GraphQL/gRPC, SOAP, SFTP/S3, Kafka/Kinesis/PubSub, Webhooks.
Target systems: SAP, Oracle, Workday, Salesforce, ServiceNow, Snowflake, ArcGIS, and more.


### Rails Upgrades without Feature Freeze
In-place Rails/Ruby upgrades while you keep shipping.

What you get:
- Upgrade plan: Ruby X to Y, Rails A to B; gem audit and shim strategy
- Dual-boot enabled; green path proven in staging with traffic replay
- Zero-downtime migrations via strong_migrations / gh-ost / pt-osc
- CI/CD hardening: matrix builds (old/new), contract tests, flaky-test quarantine
- Observability pack: pre/post p95, p99, error budgets
- Performance fixes: N+1 elimination, index strategy, cache keys
- Security hardening: Brakeman, bundler-audit, CSP/HSTS
- Cutover runbook: canary %, health gates, abort switch, rollback under 10 min

Outcomes:
- Same-day cutovers with dual-boot (no freeze)
- Zero critical CVEs at release
- p95 down 30%+ on named hot paths
- Deploy frequency: daily or better
- Infra/DB cost: down 20-40% with YJIT, caching, pool tuning

Timeline: 4-8 weeks.
Tech: Ruby 3.2/3.3 with YJIT; Rails 7.x; Puma 6; PostgreSQL 13+; pgbouncer; OpenTelemetry.

---

### DevOps & Platform Engineering
Infrastructure as code, CI/CD with tested rollback, observability, and spend guardrails for Cloudflare and AWS estates. Readiness assessment in 2-3 weeks. (https://www.allerin.com/services/devops-platform-engineering)

### AI Architecture Audit
A fixed-scope audit that measures where your LLM stack leaks money and accuracy from your real usage data. Start with the free 3-minute diagnostic, end with a prioritized fix plan. (https://www.allerin.com/services/ai-architecture-audit)

### PoC to Production
Your pilot works in the demo and stalls before production; this engagement takes it the rest of the way.

What you get:
- Production architecture before more model work
- Shadow mode beside the current system, then 5%, 25% and all traffic, each step gated on one evaluation set
- Monitoring at each step and a rollback exercised before the first 5%
- Runbooks, dashboards and alert rules handed to a named owner

Outcomes:
- The pilot serves real traffic in production, having passed the same evaluation set at every step
- An ROI case built from numbers measured at 5% and 25% of traffic, not from the demo
- Cost per request and behaviour measured at volume before full traffic

Timeline: 8-16 weeks, fixed scope and price agreed before work starts.
Team: architect, ML engineer, platform engineer, QA; security reviewer on demand.
Agentic pilots go through the same gates with an evaluation set that covers multi-step, tool-calling runs. (https://www.allerin.com/services/poc-to-production)


### AI Infrastructure & Governance Planning
Governance that is enforced in the platform, not written in a policy document: a fixed-scope plan for the AI control layer.

What you get:
- An owner and an enforcement point for every AI control
- One gateway in front of every provider: routing by cost and latency policy, hard per-team budgets, provider fallback
- Guardrails and a permission model for what each agent may read, write and trigger in production
- Per-action audit records that answer an auditor or a customer asking what an agent did
- Data boundaries by data class and model

Outcomes:
- A reference architecture with enforced controls, budgets and audit records, not a policy document
- The build-out runs on the AI Platform & Orchestration engagement; model registration, evaluation and rollback stay with MLOps

Timeline: 2-3 weeks, fixed scope and price agreed before work starts.
Team: architect, platform engineer, security architect.
Boundary: this plan covers the controls on the AI layer; the infrastructure underneath it is the platform readiness assessment on the DevOps page. (https://www.allerin.com/services/ai-infrastructure-governance)


### AI Implementation Services
Senior forward-deployed engineers in a US mid-market company's own codebase (Rails, Java or .NET), getting one AI workflow into production with evals, monitoring, KPI gates and rollback. Fixed-price diagnostic ($15k-25k, 2-4 weeks) and production pilot (typically $80k-150k, 8-16 weeks), then embedded engineering ($30k-40k per senior engineer-month) or a run-and-improve retainer ($10k-25k/month). Client owns all code. Works with existing Copilot, Agentforce, ChatGPT Enterprise and Claude contracts. (https://www.allerin.com/services/ai-implementation)


### Ruby on Rails practice
Hub at https://www.allerin.com/services/ruby-on-rails with sub-pages for new builds, embedded engineers, rescue, twelve buyer questions, client words, the support calendar and the library.

## Industries (6 Verticals)

### Manufacturing
90-day quick wins for quality, maintenance, and safety -- without ripping out your stack.
Solutions: QualiZer (defect detection), VibraQore (predictive maintenance), SiteSentra (worker safety).
Personas: Plant Ops, Quality.
ROI proof: 27% scrap reduction (tier-1 supplier), 35% fewer breakdowns (food processing).

### Warehousing & Logistics
Cycle counts done overnight, doors turning faster, picks more accurate -- no aisle closures, no WMS re-platform.
Solutions: StockVexel (inventory scanning), YardLytix (dock/yard analytics), PickPathor (pick-path optimization).
Personas: DC Managers, 3PL Ops.
ROI proof: 40% faster cycle counts, 25% dock dwell reduction.

### Retail
Shelves stocked, queues moving, customers converting -- no manual audits, no new POS.
Solutions: ShelfSentra (shelf compliance), Queuence (queue analytics), Backroom Ranger (back-room scanning).
Personas: Store Ops, Merchandising.
ROI proof: 18% OSA improvement, 30% queue walk-off reduction.

### Insurance & Government
43% faster claims adjudication, 68% document processing lift, fraud losses down -- no core system replacement.
Solutions: ClaimVista (claims automation), CompliChek (compliance validation), DocQore (document processing), FraudLens (fraud detection), ClaimSentra (property claims triage).
Personas: Claims Ops, Compliance Teams, Agency IT Directors.
ROI proof: 43% faster claims cycle time, 68% document processing improvement.

### Transportation & Rail
31% fewer track incidents, 40% faster ROW clearance, thermal failures caught before service disruption.
Solutions: RailGuard (ROW monitoring), TrackSentinel (track defects), FleetTherm (thermal monitoring), SignalEye (signal/switch monitoring), RailAurex (track/catenary inspection).
Personas: Infrastructure Directors, Safety Managers, Rolling Stock Operations Teams.
ROI proof: 31% fewer track incidents, 40% faster ROW clearance.

### Energy & Utilities
Line inspections and PV analytics with fewer flights and faster findings.
Solutions: GridAurex (line inspection), HelioLytix (solar PV monitoring), ThermaSentinel (leak/flare monitoring).
Personas: Transmission and Distribution, Renewable Asset Management, Compliance and Safety.
ROI proof: 45% inspection time reduction, 2-8% solar production recovery.

---

## Compare (decision pages)

- [AI engineering partner vs in-house team](https://www.allerin.com/compare/ai-partner-vs-in-house-team): Hire five ML engineers or partner with a firm that has shipped production AI; timelines, costs and survival rates compared.
- [AI proof of concept vs production](https://www.allerin.com/compare/ai-poc-vs-production): Why most AI dies as a demo; the gap between a PoC and production is architectural, and what is different.
- [Boutique AI firm vs Big 4 consultancy](https://www.allerin.com/compare/boutique-ai-vs-big-consultancy): Big 4 AI practices charge 3-5x more and staff juniors after the pitch; boutique firms ship the seniors who sold the work.

## Dedicated AI Teams

- [Dedicated AI Teams](https://www.allerin.com/dedicated-ai-teams): A dedicated senior AI team working only on your problems, with MLOps pipelines and model governance built in, first model in production in 90 days, and a build-operate-transfer option.
- [Dedicated AI Team, 90-day blueprint](https://www.allerin.com/dedicated-ai-teams/blueprint): Six phases: discovery, architecture, team assembly, platform setup, first deployment, governance handoff.
- [Engagement models](https://www.allerin.com/dedicated-ai-teams/engagement-models): Build-Operate-Transfer, Hybrid and the Team Readiness Sprint, compared on investment, timeline and deliverables.
- [MLOps pipeline setup](https://www.allerin.com/dedicated-ai-teams/mlops-pipeline-setup): Data pipelines, model development, deployment automation, monitoring and governance, operational in weeks.

## Customers

- [Customer stories](https://www.allerin.com/customers): 15 case studies with measured outcomes across SaaS, healthcare, logistics, finance and the public sector; each has its own page under /customers/.

## Assessments and conversion pages

- [AI strategy for engineering leaders](https://www.allerin.com/ai-strategy): Routes the five questions engineering leaders bring (a vague mandate, LLM cost and accuracy, platform controls, security proof, a stalled pilot) to a fixed-scope answer run by the team that builds it. No strategy sprint; a large firm is named as the better fit for company-wide transformation programs.
- [AI Production Readiness Assessment](https://www.allerin.com/ai-readiness-assessment): A five-minute assessment with a score and recommendations.
- [AI Architecture Diagnostic](https://www.allerin.com/ai-architecture-diagnostic): Map a RAG or LLM architecture and quantify where it leaks money and breaks under load.
- [Platform Readiness Check](https://www.allerin.com/platform-readiness-check): Twelve questions on infrastructure as code, deploy safety, observability, spend, access and resilience; results immediately, no email required.
- [SOC 2 for startups](https://www.allerin.com/soc-2-for-startups): Audit-ready in 8-12 weeks without building an internal security team.

## Resources

- [ALPR & Redaction Policy Guide](https://www.allerin.com/resources/alpr-redaction-policy-guide): Field guide to shipping ALPR and video/audio redaction that stands up to PRA/FOIA and CJIS-style audits. By Sarah Chen, Solutions Architect. 12 min read.
- [Edge Computer Vision Production Blueprint](https://www.allerin.com/resources/edge-computer-vision-production-blueprint): Practical blueprint from PoC to stable edge CV -- covering datasets, accuracy gates, latency budgets, and operational review loops. By Marcus Rodriguez, Principal Engineer. 14 min read.
- [Model rollback with compatible features and configuration](https://www.allerin.com/resources/model-rollback-demo): A runnable synthetic local example of registration, lineage, canary checks, rollback and recovery. Organizational author: Allerin. No production SLA or multi-region benchmark is claimed.
- [Agent guardrails with expiring approvals and safe retries](https://www.allerin.com/resources/agentic-ai-guardrails-evals-hitl): A runnable synthetic workflow tests authorization, bound human approvals, duplicate calls, retries and audit records. Organizational author: Allerin. No model execution or production case is claimed.

---

## Company

- **Name**: Allerin US Inc.
- **Founded**: 2005
- **Headquarters**: 2921 Prado Street, Unit A, Austin, TX 78702, USA
- **Phone**: +1-512-200-2416
- **Email**: contact@allerin.com
- **Website**: https://www.allerin.com
- **LinkedIn**: https://www.linkedin.com/company/allerintech
- **Twitter/X**: https://x.com/allerint
- **Specialties**: Computer Vision, Edge AI, Agentic AI, GenAI/RAG, MLOps, Data Analytics, Platform Modernization, AI Security
- **Deployment**: On-premise, cloud, edge, hybrid -- KPI-gated rollouts with measurable outcomes before scaling