Enterprise AI

Full programme

Priority problems

Programme budget

Committed after survey

Pilots that never reach production

A demo that impresses the board still has to survive real volumes, real edge cases and real approval chains.

Data spread across systems that do not talk

ERP, CRM, MES and a decade of spreadsheets, each holding a different version of the same fact.

Knowledge locked in documents and in people

The rules that actually govern the work live in contracts, manuals and the heads of a few senior staff.

No agreed measure of whether it worked

Without a baseline agreed before the build, every result becomes a matter of opinion.

Security and data residency

Sensitive records cannot simply be posted to an external service, and the review that says so arrives late.

Teams already at capacity

The people who understand the process are the same people running it every day.

Document & knowledge automation

Extraction, classification and retrieval over contracts, invoices and internal documentation, with human approval kept in the loop.

Operational copilots

Assistants grounded in your own data that answer staff questions and draft routine work inside the systems people already use.

Forecasting & decision support

Demand, capacity and risk models that feed the dashboards management already reads, not a separate tool nobody opens.

Workflow automation

Rule-based and model-based steps stitched into existing approval chains, with full activity logging.

Data foundation work

The unglamorous part that decides whether any of the above works: pipelines, quality checks and access control.

Integration into running systems

API, database or RPA paths into the ERP, CRM and MES already in service, chosen to suit what those systems allow.

One or two problems, done properly

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.

Your data stays where you need it

On-premise and private cloud architectures are available for sensitive data. Security risk assessment is a formal part of the survey stage.

Handling time down

Routine document and request handling moves from manual reading to review of a prepared draft.

Throughput up

The same team absorbs more volume without the queue growing behind them.

Visibility across the process

Every automated step is logged, so bottlenecks stop being anecdotes.

Time to first result

A Proof of Value on real data, not a slideware pilot, inside the first stage.

ERP, CRM and MES integration

API, database or RPA paths chosen to suit what each system actually permits.

On-premise and private cloud

Sensitive workloads stay inside your boundary when the risk assessment calls for it.

Pipelines and data quality

Ingestion, cleaning and validation, with the checks that stop bad input becoming bad output.

Access control and audit trail

Who asked what, what the system answered, and which human approved it.

Monitoring and evaluation

Accuracy tracked against the agreed baseline after go-live, not only before it.

Model layer

Hosted or self-hosted language models selected per workload, with the option to change without rebuilding around them.

Delivery record

Over 100 systems delivered for clients including Hyundai AutoEver, Naver, Lotte and Seegene.

Commitments after the survey

Targets on accuracy and savings are set once the data is understood, and written down.

Your systems, not ours

The result runs inside the tools your staff already use, with no second platform to maintain.

Operated after go-live

Optimisation continues once the system is live, because that is when the real inputs arrive.

Systems Delivered

Global Clients

Clutch Rating

Offices Worldwide