01
AI application deployment
LLMs, RAG and agents shipped as systems your team actually uses, not showcase POCs.
RAGAgentsPrivate deployment
AI deployment studio
Deploy AI intoreal operationsnot just the demo
We are a team that takes AI from proof of concept to production. Process diagnosis, system engineering, and the network and cloud foundation that keeps it running.
- First shippable milestone
- 12 wksFirst shippable milestone
- App · Data · Infrastructure
- 3 layersApp · Data · Infrastructure
- Monitoring and operations
- 24/7Monitoring and operations
prod / agent-pipelinerunning
Ingest100%
Vector index86%
Agent runtime72%
Workflow94%
- 09:42:07deployapi.gateway v2.4.1
- 09:42:09index12,480 docs embedded
- 09:42:11agenttool registry loaded
- 09:42:14guard0 policy violations
- 09:42:16livetraffic 100% on new build
latency142ms
Architecture illustration
Technology we build with
- OpenAI
- Anthropic
- Google Cloud
- AWS
- Microsoft Azure
- Vercel
- Cloudflare
- PostgreSQL
- Docker
- Kubernetes
CAPABILITIES
Five capabilities, one delivery standard
Buying a tool does not make AI work. It needs the application, data, and infrastructure layers to line up, so we build all three together.
02
AI-assisted engineering
Faster delivery without skipping the parts that matter: specs, tests, and code review.
AI codingCI/CDCode review
03
Network & cloud foundation
AI is only as reliable as the network under it. We design connectivity, access, and cost together.
CloudZero trustObservability
04
Data & workflow automation
Connect scattered data and systems so quoting, reconciliation, and triage run themselves.
ETLAPIWorkflow
05
Support agents & knowledge base
Customer support and internal assistants built on your own documents, with citations you can audit.
ChatbotKnowledge baseMulti-channel
Not sure where to start?
A 30-minute diagnostic call. We will tell you which process is worth automating first.
HOW WE WORK
From diagnosis to operations, every step is verifiable
We do not deliver "roughly done". Each phase has a concrete output you can inspect and stop at.
- 01
Diagnose
1–2 weeksMap current processes and systems, find the bottlenecks worth measuring, estimate cost and return.
- 02
Design
2–3 weeksArchitecture, data flow, access control, risks and cost, written down before any code.
- 03
Deploy
4–12 weeksTwo-week increments. Every milestone is something you can click through and sign off.
- 04
Operate
OngoingMonitoring, tuning, documentation, handover. Your team should be able to take the wheel.
INSIGHTS
Engineering notes and company updates
We write down the judgement calls: what worked, what only sounded clever.
Company
How we rebuilt this site: moving content out of the codebase
A rewrite is not a new skin. We moved articles into Postgres and images into object storage so an AI agent can publish on its own.
6 min read
Industry
What MCP actually is, and whether you should adopt it now
Model Context Protocol solves "how does AI safely reach our systems". It is an interface standard, not a product.
8 min read
Industry
The five most common AI adoption mistakes
We have seen too many "bought the tool, nobody uses it" projects. The failure is almost always the process, not the model.
7 min read
INDUSTRIES
Operating contexts we know
- Financial services
- E-commerce
- Trading & sourcing
- Manufacturing
- Logistics
- Professional services
iBosontech · AI Deployment
Ready to put AI into production?
Tell us the one process you most want to fix. We will reply with a concrete approach, not a product brochure.
CONTACT
Start a conversation
Use the form below. We reply within one business day.
- Emailinfo@ibosontech.com
- HoursMon–Fri 09:00–18:00