ANCHI Industrial AI & Ops
MLOps, LLMOps, and Vision AI for real-world operations. We help companies turn AI from 'nice slides' into reliable systems: clean data pipelines, deployed models, AI copilots, and vision solutions that actually run in production.
Who we focus on
SMEs & Mid-size Companies
Manufacturing & Industrial
Factories, automotive, EV, logistics
EdTech / Training Centers
Online learning platforms, corporate training, educational technology
Services & Operations
Logistics, customer service, back-office
Startups
Have a web/app product that "should use AI",
Some data (logs, user events, images, sensor data),
But no in-house MLOps / LLMOps team.
Typical pains
Models exist in Jupyter notebooks, not in production.
Data is scattered: Excel, CRM, ERP, Google Sheets, home-grown tools.
They tried "AI features" but: no monitoring, no retraining plan, performance degrades silently.
Everyone talks about LLMs & GPT, but: no clear architecture, security worries, they're afraid to expose private data to third parties.
For factories/logistics: Video cameras everywhere, but no automated QC, counting, or safety checks.
Service pillars
Pillar 1 – MLOps Foundation for Tabular & Time-Series
For companies with relational databases, logs, sensor data
What we do:
- Data pipelines: Ingest from existing systems, clean & standardize, schedule regular refresh
- Model lifecycle: Training pipelines, automated evaluation, CI/CD for models
- Use cases: Forecasting, scoring/ranking, anomaly detection
- Ops layer: Monitoring dashboards, logging and alerts
Outcome:
From "we have data and some prototype model" → to a maintained ML system that your ops team can trust.
Pillar 2 – AIOps for IT & Business Operations
For teams with complex systems, logs, and manual triage
What we do:
- Integrate telemetry: Logs from applications, servers, cloud services, business events
- Apply AI to: Detect anomalies, correlate events, recommend remediation
- Automate routine ops: Auto-open/label tickets, route incidents, summarize history
Our advantage:
ANCHI's signal/time-series DNA: treat logs and metrics as "another kind of signal".
Pillar 3 – LLMOps & AI Copilots for Business Workflows
For companies wanting internal GPT-like tools, but safer and integrated
What we do:
- LLM strategy & architecture: Choose APIs vs. on-premise, design RAG over internal documents
- Use cases: Knowledge assistants, customer support, content & reporting copilots
- LLMOps layer: Prompt/version management, evaluation, logging & analytics
Outcome:
Not just "we added a chatbox", but a maintainable LLM feature with observability and clear boundaries.
Pillar 4 – Vision AI for Quality & Operations
For factories, warehouses, labs, training centres
What we do:
- Design camera-based AI workflows: Product defect detection, counting objects, safety & compliance, activity monitoring
- Pipeline: Camera setup consultation, data collection, model training, deployment (edge or server-side)
- Integration: Connect alerts to existing systems, simple dashboards for trends
Boundary:
No face recognition for surveillance/policing. Focus on process, quality, safety – not identity.
Engagement models
AI & Ops Diagnostic
4–6 weeks
Review current systems (DB, apps, logs, cameras). Identify 2–3 high-impact AI opportunities.
Deliver:
- Architecture proposal
- Rough data requirements
- Impact vs. effort matrix
- Small proof-of-concept
Good entry point for:
Factories, logistics firms, EdTechs that "want AI" but are lost.
Pilot Project
8–16 weeks
Choose one clear use case: Defect detection, lead scoring, knowledge assistant, etc.
Deliver:
- Working pipeline
- Trained model + data flow
- Simple UI/API
- Documentation & training
Goal:
Prove value and build trust.
Managed AI & Ops
Monthly Retainer
For clients who like the pilot and want ongoing support.
Monthly retainer covers:
- Monitoring & maintenance
- Improvements
- Small add-on use cases
- Model updates
- Handling data growth
Boundaries & legal framing
No clinical diagnosis
In healthcare/med sectors, ANCHI only builds tools for workflow & operations, not diagnostic engines. Any clinical interpretation is the client's responsibility.
Data governance
For sensitive data: prefer running code on client's infrastructure (VPN/SSH/cloud account), or anonymised / synthetic subsets for development.
Ownership
For project work: client owns models & code specific to their business, ANCHI may reuse generic components (frameworks, templates) unless exclusivity is agreed.
Ready to turn your AI prototypes into production-ready systems?
Have models stuck in notebooks? Need reliable AI operations? Let's turn your AI ideas into systems that actually run in production. Leave your information below, and the ANCHI team will contact you for a free consultation.