
InnoEx
An innovation alliance connecting businesses, government agencies, universities, investors, and technology experts.
Production-grade AI built into the systems you already run — document automation, operational copilots and decision support, committed against operational KPIs.
Hitek turns enterprise data, workflows and institutional knowledge into production-grade AI systems — integrated with the ERP, CRM and MES you already run, and measured against operational KPIs rather than demo impressions.

An innovation alliance connecting businesses, government agencies, universities, investors, and technology experts.

An end-to-end interior design and construction company founded in 2017.

An international logistics company providing transportation, warehousing, and supply chain solutions.

A transportation and import-export company providing freight transportation and logistics solutions.

A logistics and import-export company founded in 2013, specializing in freight forwarding and customs procedures.

A provider of global freight forwarding services and logistics solutions.
The obstacles that keep pilots from reaching the operating floor
A demo that impresses the board still has to survive real volumes, real edge cases and real approval chains.
ERP, CRM, MES and a decade of spreadsheets, each holding a different version of the same fact.
The rules that actually govern the work live in contracts, manuals and the heads of a few senior staff.
Without a baseline agreed before the build, every result becomes a matter of opinion.
Sensitive records cannot simply be posted to an external service, and the review that says so arrives late.
The people who understand the process are the same people running it every day.

Six kinds of system, each integrated into the tools your staff already open
Extraction, classification and retrieval over contracts, invoices and internal documentation, with human approval kept in the loop.
Assistants grounded in your own data that answer staff questions and draft routine work inside the systems people already use.
Demand, capacity and risk models that feed the dashboards management already reads, not a separate tool nobody opens.
Rule-based and model-based steps stitched into existing approval chains, with full activity logging.
The unglamorous part that decides whether any of the above works: pipelines, quality checks and access control.
API, database or RPA paths into the ERP, CRM and MES already in service, chosen to suit what those systems allow.

Four disciplined stages, with commitments made only once the data is understood
Every engagement moves through four disciplined stages: survey and use-case selection, a Proof of Value built on real data, rollout into the operating environment, then continuous optimisation. Commitments on accuracy and savings are set after the data survey — committing before understanding your data is not a commitment worth having.
Spreading across a dozen processes is the fastest route to failure. One well-chosen problem with a measured ROI earns the internal trust and budget for the next.
On-premise and private cloud architectures are available for sensitive data. Security risk assessment is a formal part of the survey stage.

Manufacturing, distribution, finance, logistics, retail and large service organisations — typically 300 to 3,000 staff, already running ERP or CRM, with data spread across systems that do not yet talk to each other.
What the programme is measured on once it reaches the operating floor
Routine document and request handling moves from manual reading to review of a prepared draft.
The same team absorbs more volume without the queue growing behind them.
Every automated step is logged, so bottlenecks stop being anecdotes.
A Proof of Value on real data, not a slideware pilot, inside the first stage.

Built to sit inside your environment rather than beside it
API, database or RPA paths chosen to suit what each system actually permits.
Sensitive workloads stay inside your boundary when the risk assessment calls for it.
Ingestion, cleaning and validation, with the checks that stop bad input becoming bad output.
Who asked what, what the system answered, and which human approved it.
Accuracy tracked against the agreed baseline after go-live, not only before it.
Hosted or self-hosted language models selected per workload, with the option to change without rebuilding around them.

An engineering team that has to operate what it builds
Over 100 systems delivered for clients including Hyundai AutoEver, Naver, Lotte and Seegene.
Targets on accuracy and savings are set once the data is understood, and written down.
The result runs inside the tools your staff already use, with no second platform to maintain.
Optimisation continues once the system is live, because that is when the real inputs arrive.
Hitek takes responsibility for AI in the operating environment — integrated with the ERP, CRM and MES you already run, committed against KPIs agreed after the data survey, and optimised once it is live. Start with a survey and one well-chosen problem.
A demo that impresses the board still has to survive real volumes, real edge cases and real approval chains. The data sits in ERP, CRM, MES and a decade of spreadsheets; the rules that actually govern the work live in contracts, manuals and the heads of a few senior staff; and without a baseline agreed before the build, every result becomes a matter of opinion. Hitek answers with four disciplined stages — survey and use-case selection, a Proof of Value on real data, rollout, then continuous optimisation — and starts with one or two problems done properly.
No. On-premise and private cloud architectures are available for sensitive data, and a security risk assessment is a formal part of the survey stage — sensitive workloads stay inside your boundary when the assessment calls for it. Hosted or self-hosted language models are both supported, with access control and an audit trail of who asked what, what the system answered, and which human approved it.
Through API, database or RPA paths chosen to suit what each system actually permits, so the result runs inside the tools your staff already open rather than a second platform nobody maintains. Pipelines handle ingestion, cleaning and validation, and rule-based or model-based steps are stitched into existing approval chains with full activity logging.
The pilot programme runs 3 months and covers 3–5 priority problems, with budget from 5,000 USD. It starts with a survey: commitments on accuracy and savings are set after the data survey, not before. A Proof of Value runs on real data inside the first stage, and accuracy is tracked against the agreed baseline after go-live as well as before it.
Partnership is like finding a travel companion — it has to fit to go far. A free 30-minute talk to see if we match: you walk away with a concrete solution proposal and a transparent quote, whatever you decide.
Confidential, no-obligation consultation · NDA available from day one · Reply within 24 business hours