AI Solutions
The enterprise value of AI is not a chat window; it is a task that repeats every day disappearing. We start by measuring which task actually qualifies.
- ACandidate process list
- BFeasibility decision
- CPilot
- DAccuracy measurement
- ERollout
Mipo builds AI applications that run on an organisation's own data: document and invoice processing, an internal knowledge assistant, request classification and forecasting. We do not train foundation models from scratch; we connect existing models to your data and processes and integrate them with your ERP, accounting and support systems. Because the same company builds the infrastructure the application runs on, the question of where your data is processed is answered from the outset.
AI projects in five steps, from feasibility to production.
At the end of each step you hold something concrete. There is no in-between state called “in progress”.
- A
Candidate process list
Processes where AI could help are listed and the monthly manual workload of each is measured. An unmeasured process does not make the list.
↳ Candidate process list and workload table
- B
Feasibility decision
We separate what needs AI from what needs a simple rule or a process change. In most organisations half the list falls into the second group.
↳ Feasibility note with rationale
- C
Pilot
A limited-scope version running on real data in a single process. Not a demo — a version people use in their daily work.
↳ Working pilot and usage log
- D
Accuracy measurement
Output accuracy and time saved are measured numerically. If it falls below the threshold, the project stops here.
↳ Accuracy and benefit report
- E
Rollout
If measurement is positive, scope widens, integrations are completed and monitoring is established.
↳ Go-live report and monitoring dashboard
Where the model and integration work stands at each stage.
Two-week cycles keep the project visible. Months of “in development” followed by a surprise delivery don’t happen here.
Candidate processes and workload measurement
Feasibility and hosting decision
Pilot development and field trial
Accuracy measurement and decision
Which models and tools we build AI solutions with.
Pilot 4–8 weeks · rollout depends on scope
What we do not promise in AI solutions.
Items not in the quote are surprises that appear mid-project. With us, exclusions are also in writing.
- Training a foundation model from scratch — unnecessary cost in enterprise scenarios
- Rolling out a pilot that shows no measurable gain
- Rebuilding your data collection processes — a separate item
- Designs that decide automatically without human approval
Questions about ai solutions.
That is a decision, and it is made up front. Where confidentiality requirements are high the model runs on-premise and data never leaves your servers. If cloud is chosen, exactly what is sent and where it is processed is defined in writing.
In a properly built enterprise application the model does not decide alone. A confidence threshold is defined for every output; results below it are not applied automatically but go to human approval. The source document behind each answer is shown so accuracy can be checked.
In most enterprise scenarios no, and doing so creates unnecessary cost. Connecting existing models to your own documents and records covers the majority of needs. Fine-tuning only becomes relevant when a domain-specific language or output format is mandatory.
For document-based applications even a modest set is enough to start; what matters is not volume but whether the data is organised and accessible. For forecasting, the number of historical records directly affects accuracy and is measured during feasibility.
We stop and report why in writing. Rolling out an AI project that produces no measurable gain leaves the organisation with a permanent maintenance cost and nothing else.
No. The application sits alongside your existing systems and connects through integration. We do not propose designs that require replacing your ERP or accounting software.