Miga Digital
Discuss your situation
Capabilities and services

What we can do with your data, documents and processes

From cleaning spreadsheets and building dashboards to prediction, Document AI, copilots, automation or training. We don't apply every capability at once: we choose the one that fits your data, your process and the risk involved.

Discuss your situationSee how to choose where to start

Demos use fictional data. AI proposes; people review and decide.

Choose where to look

You don't need to know whether you need ML, generative AI or automation. Start from the type of problem.

My data is a mess

Data, cleaning, BI

  • Spreadsheets and exports that don't match
  • A base for dashboards and reports
  • Not everything needs AI — sometimes tidying up is enough

I want to understand my business better

Analysis, dashboards, reporting

  • Clear KPIs without rebuilding the spreadsheet each week
  • Actionable trends and alerts
  • A good place to start with existing data

I want to predict or prioritise

Prediction, ML, scoring

  • Only where there's enough history
  • Scoring for leads, demand or risk
  • Explainable models, not a black box

I have documents, emails or internal knowledge

Document AI, copilots, generative AI

  • Extract data from PDFs and invoices
  • Copilots that cite internal manuals
  • With human review at every sensitive step

I want to automate or train the team

Automation, applied training

  • Connect repetitive steps with approvals
  • Pilot first, integrate later
  • Training on your real processes
012 capabilities

Data foundation and decisions

Tidy up what you already have and turn it into clear reports — the most common starting point.

If you have scattered data or manual reports, start here.

01Essential baseA good place to start

Data organised and ready to use

Data, cleaning and data engineering

We unify spreadsheets, exports and scattered sources so they can be analysed, reported on or automated.

What it solves

Data scattered across spreadsheets, exports and systems that don't add up.

Business value

Less time hunting for figures; fewer copy errors; a solid base for dashboards and prediction.

Examples

  • Consolidate monthly sales
  • Match customers with orders
  • Prepare data for a management report

Data required

  • CSV/Excel exports
  • Read access to agreed sources
  • Definition of key fields

What we deliver

  • Source inventory
  • Cleaning pipeline
  • Unified dataset
  • Documentation
When it does NOT fit

No data owner; inaccessible sources; expecting an enterprise data warehouse.

We say so before we start — judgement, not a catalogue.

02DecisionA good place to start

Analysis and executive reports

Data analysis and BI

KPIs, trends, alerts and a narrative to decide on — not just charts.

What it solves

Manual reports, unclear KPIs and little visibility to decide.

Business value

Fewer hours building the report; more time interpreting and acting.

Examples

  • Sales dashboard
  • Margin tracking
  • Deviation alerts

Data required

  • Exportable history
  • Agreed KPIs (≤8)
  • A clear reporting cadence

What we deliver

  • Dashboard
  • Recurring report
  • Interpretation guide
When it does NOT fit

Very messy base data with no willingness to clean it; endless KPIs with no priorities.

We say so before we start — judgement, not a catalogue.

022 capabilities

Prediction and models

Prioritise and anticipate when there's enough history — we explore the fit, we don't promise up front.

Only if the history is reliable and the case justifies it.

03Requires historyRequires data

Prediction and prioritisation (with data)

Prediction, ML and advanced analytics

Lightweight, explainable models to prioritise leads, demand or risk — when history exists.

What it solves

Commercial or operational decisions with no history-based prioritisation.

Business value

Sharper commercial or operational focus; less guesswork without data.

Examples

  • Lead scoring
  • Demand forecasting
  • Anomaly detection

Data required

  • Enough history (months/years depending on the case)
  • Documented variables
  • An agreed prediction target

What we deliver

  • Explainable model
  • Report of limitations
  • Integration into the decision flow
When it does NOT fit

No history; poor-quality data; expecting infallible prediction.

We say so before we start — judgement, not a catalogue.

04AdvancedAdvanced capability

Advanced models for specific cases

Deep learning and advanced models

Neural networks when rules or classic ML aren't enough — vision, complex text, long time series.

What it solves

Very complex tasks (vision, dense text, long series) where rules or classic ML fall short.

Business value

Automate highly complex tasks already repeated thousands of times.

Examples

  • Visual quality classification
  • Specialised NLP
  • Complex time series

Data required

  • A high volume of examples
  • Labelling or documented criteria
  • A scoped business case

What we deliver

  • Prototype
  • Metrics on a sample
  • Production plan with human review
When it does NOT fit

An SME without volume; a problem solvable with rules; an exploratory budget.

We say so before we start — judgement, not a catalogue.

033 capabilities

AI applied to text, documents and knowledge

Extract, classify and query documents with human review at every sensitive step.

Documents, emails and internal manuals — always with human oversight.

05With human reviewWith human review

Generative AI with method

Applied generative AI

Classification, drafts, summaries and assisted extraction — always reviewable.

What it solves

Repetitive text tasks: classifying, summarising, drafting.

Business value

Speed up preparation without losing control or consistency.

Examples

  • Email drafts
  • Meeting summaries
  • Query classification

Data required

  • Examples of inputs/outputs
  • A confidentiality policy
  • Agreed review checkpoints

What we deliver

  • Flows with operational prompts
  • Review checkpoints
  • Safety checklist
When it does NOT fit

Full automation with no oversight; sensitive data with no controlled environment.

We say so before we start — judgement, not a catalogue.

06DocumentsWith human review

Document extraction

Document AI / OCR

Pull fields from PDFs and invoices; a person reviews anything uncertain.

What it solves

Manually retyping PDFs, invoices and forms into a spreadsheet or admin system.

Business value

Less manual retyping; fewer errors in records and entries.

Examples

  • Invoices to a spreadsheet
  • Contracts to structured fields
  • Scanned forms

Data required

  • A sample of documents of the same type
  • A target field schema
  • Validation criteria

What we deliver

  • OCR pipeline
  • Review queue
  • Export to the agreed destination
When it does NOT fit

Highly variable documents; illegible scans with no improvement process.

We say so before we start — judgement, not a catalogue.

07With sourcesWith human review

A copilot for your documentation

Private document copilots

Questions about manuals and procedures, with source citations.

What it solves

Searching for answers across manuals, procedures and internal documentation.

Business value

Less searching; consistent answers on internal docs.

Examples

  • Internal FAQ
  • HR procedures
  • Operational rules

Data required

  • An accessible document corpus
  • Defined permissions
  • Someone responsible for updates

What we deliver

  • Private chat with citations
  • Panel of documentation gaps
  • Usage and update guide
When it does NOT fit

Outdated docs; legal/tax expectations without a human expert.

We say so before we start — judgement, not a catalogue.

043 capabilities

Operations, territory and adoption

Automate with review checkpoints, analyse by area and train the team before implementing.

Day-to-day operations, territory and team adoption.

08Stable processWith human review

Automation with review

Process automation

Connect repetitive steps between tools with human approvals.

What it solves

Copying data between tools, missing steps and repetitive manual processes.

Business value

Less copy-paste; fewer things slipping between systems.

Examples

  • Email → task → CRM
  • Document → validation → export
  • Notifications with approval

Data required

  • A stable process (≥3 months)
  • Access to the tools involved
  • Clear approval points

What we deliver

  • Automated flow
  • Basic logs
  • Operational documentation
When it does NOT fit

A process that changes every week; no internal owner of the flow.

We say so before we start — judgement, not a catalogue.

09TerritoryRequires data

Location-based analysis

Geospatial and geomarketing

Maps and territorial analysis of customers, sales, points of interest or delivery.

What it solves

Decisions on area, campaign or expansion without seeing the geographic data.

Business value

Better decisions on area, campaign or expansion.

Examples

  • Customer map
  • Sales coverage
  • Sales heatmap

Data required

  • Addresses or coordinates
  • Sales by area
  • An agreed geographic definition

What we deliver

  • Interactive maps
  • Territorial reports
  • Recommendations by area
When it does NOT fit

No geographic data; a 100% online market with no local component.

We say so before we start — judgement, not a catalogue.

10AdoptionA good place to start

Applied training by role

Applied training

Workshops on your processes, with safety and human review.

What it solves

A team using AI or data with no shared judgement or common method.

Business value

Shared judgement; pilot adoption; safe use of AI.

Examples

  • AI for management
  • Admin and documents
  • Sales and marketing

Data required

  • A description of the processes
  • Fictional or anonymised examples
  • Participating roles

What we deliver

  • Operational playbook
  • Safe-use checklist
  • A light opportunity map
When it does NOT fit

Looking for a cheap, generic ChatGPT certificate with no link to processes.

We say so before we start — judgement, not a catalogue.

Sectors

Use cases we can design

Not all have a public demo. Where there's data, a process and a clear decision, we can design a demo or scoped pilot for your sector.

Advisory / accounting firms

Demo

Typical data

  • Shared email inbox
  • Tax deadlines and tax calendar
  • Client documents (PDFs, official forms)

We can explore

  • Inbox triage by urgency and type
  • Reviewable draft replies
  • Field extraction from invoices and official forms

Capabilities

Generative AIDocument AIAutomation

No auto-send; professional responsibility always stays human

Request a demo for this sector

Pharmacies

Example scenario

Typical data

  • Sales by product and category
  • Stock, expiry dates and stock-outs
  • Supplier delivery notes and invoices

We can explore

  • Medicine expiry control
  • Stock and stock-out alerts
  • Seasonal demand forecasting

Capabilities

DataBIPredictionDocument AI+2

Sensitive health data; no pharmaceutical advice; prediction only with reliable history

Request a demo for this sector

Restaurants / hospitality

Example scenario

Typical data

  • POS and tickets by shift
  • Bookings and attendance
  • Recipe costings and food costs

We can explore

  • Demand forecasting by day and hour
  • Bookings, attendance and no-shows
  • Margin by dish or category

Capabilities

BIPredictionGenerative AIGeospatial+2

Heterogeneous POS data; recipe costings sometimes incomplete; we don't promise to fill tables

Request a demo for this sector

Influencers / creators

Demo

Typical data

  • Metrics by platform and format
  • Editorial calendar and published content
  • Brand contracts and briefs

We can explore

  • Performance by format and platform
  • Editorial calendar and content reuse
  • Commercial proposals for brands

Capabilities

BIGenerative AIAutomation

We don't promise virality; platform APIs change; generative AI without losing your own voice

Request a demo for this sector

E-commerce

Demo

Typical data

  • Orders, returns and stock by SKU
  • Campaigns and ads
  • CRM / customers and cohorts

We can explore

  • Demand and stock-out forecasting
  • Segmentation and purchase propensity
  • Abandoned carts (where there's tracking)

Capabilities

DataBIPredictionAutomation

ML requires history; tracking and attribution are often incomplete

Request a demo for this sector

Local shops

Example scenario

Typical data

  • Sales by time slot
  • Average ticket and products
  • Customers and loyalty

We can explore

  • Sales by time slot and average ticket
  • Low-rotation products
  • Local campaigns and promotions

Capabilities

GeospatialBIData

Limited geographic data; small samples; maps don't replace a full strategy

Request a demo for this sector

Technical services

Demo

Typical data

  • Work orders and incidents
  • Field photos and routes
  • Resolution times

We can explore

  • Extraction from paper or PDF work orders
  • Classification of recurring incidents
  • Operational reports for dispatch

Capabilities

AutomationDocument AIGenerative AIDeep Learning

Deep learning only with image volume; data scattered across mobile, email and ERP

Request a demo for this sector

Clinics

Demo

Typical data

  • Schedule, appointments and no-shows
  • Administrative documentation
  • Consent forms and billing

We can explore

  • Reminders and fewer no-shows
  • Extraction from administrative docs
  • Occupancy and appointment reporting

Capabilities

Document AIAutomationBI

No medical diagnosis; sensitive data and GDPR; human review required

Request a demo for this sector

A scenario we can design, not a public demo — we can explore a scoped pilot where there's data, a process and a clear decision. Human review at sensitive steps.

Decision

How to choose where to start

Six common situations — no wizard, just clear judgement.

  • If you don't know what you need

    Start with an AI Sprint: training, assessment and a prioritised opportunity map.

    See the AI Sprint

  • If you have data but not confidence

    Cleaning + BI: tidy the sources and a first dashboard before thinking about prediction.

    See data and BI

  • If you want prediction

    Validate the history first. Without enough data, we don't promise models.

    See prediction and ML

  • If you have documents

    Document AI to extract fields; a copilot if the value is in querying internal manuals.

    See Document AI and copilots

  • If the team doesn't know how to use AI

    Applied training on your processes — shared judgement before implementing.

    See applied training

  • If there's already a repetitive process

    A scoped automation pilot with human review checkpoints.

    See automation
Start with an AI SprintRequest a demo for my case
Trust

What we don't sell up front

We'd rather say it plainly than sell something that neither scales nor can be measured.

  • A full multi-tenant SaaS

    Without validated repeatability or sustainable support.

  • Promises of guaranteed savings

    Impossible to sustain; it breeds distrust.

  • Replacing professional/legal/tax judgement

    Responsibility beyond scope; AI prepares, it doesn't decide.

  • Predictive models without data

    Fragile results and broken expectations.

  • Critical automations without oversight

    Operational and reputational risk.

  • Real integrations with no assessment

    A scoped pilot first; then connect systems with judgement.

  • Regulated cases with no conditions

    Health, tax or legal require human review and an agreed framework.

Discuss your situationRequest a demo for my caseStart with an AI Sprint