How we would approach four common problems

Example solutions

• Example solution

  AI Knowledge Assistant

  • AI
  • Business Systems
  • Data

  Business problem

  A company’s procedures, policies, and product details are spread across a wiki, a shared drive, and years of email attachments. Staff ask colleagues rather than search, and answers depend on who is available. Nobody is sure which version of a document is current.

  Proposed solution

  A retrieval-augmented assistant over approved internal sources. Documents are indexed with their owner, version, and access group. Questions are answered from retrieved passages with citations, and anything outside the indexed scope is answered with a clear "not covered" response and a route to a person.

  Main capabilities

  • Natural-language questions over approved internal documents
  • Answers with citations to the source document and section
  • Retrieval filtered by the signed-in user’s permissions
  • Scheduled re-indexing when source documents change
  • Feedback control on every answer, feeding a review queue
  • Usage and cost reporting per department

  Expected business value

  • Reduces repeated internal questions to experienced staff
  • Makes existing documentation genuinely usable instead of merely stored
  • Shortens onboarding for new team members
  • Surfaces gaps and contradictions in documentation through unanswered questions

  Example architecture

  1. 01 Connectors pull documents from the wiki, file storage, and shared mailboxes
  2. 02 Ingestion service extracts text, splits it into passages, and records version and access group
  3. 03 Hybrid keyword and vector index, refreshed on a schedule
  4. 04 Retrieval layer applies the user’s permissions before any passage is selected
  5. 05 Model call composes an answer from the retrieved passages only, returning citations
  6. 06 Web interface with conversation history, source links, and feedback capture

  Technology categories

  • Python
  • FastAPI
  • Vector database
  • PostgreSQL
  • React
  • TypeScript
• Example solution

  Intelligent Document Processing Platform

  • AI
  • Automation
  • Data

  Business problem

  Supplier invoices and delivery notes arrive by email in dozens of layouts. A small team keys them into the finance system by hand. Mistakes surface weeks later during reconciliation, and the backlog grows whenever someone is on leave.

  Proposed solution

  A processing pipeline that ingests documents from a monitored mailbox, extracts the required fields with a confidence score, and validates them against master data and arithmetic rules. Documents that pass every check post automatically; anything uncertain goes to a review queue where a person confirms or corrects it, and those corrections are kept as training and evaluation data.

  Main capabilities

  • Ingestion from monitored mailbox, upload, and API
  • Text and layout extraction, including OCR for scanned pages
  • Field-level extraction with per-field confidence scores
  • Validation against supplier master data, totals, and tax rules
  • Review queue with the original document shown beside the extracted values
  • Straight-through posting for documents that pass all checks
  • Accuracy reporting per field and per supplier

  Expected business value

  • Removes repetitive keying from a team that has better uses for its time
  • Catches errors at capture rather than at reconciliation
  • Keeps processing time independent of who is in the office
  • Produces a measurable accuracy figure per field instead of an assumption

  Example architecture

  1. 01 Mailbox and upload watchers place documents into object storage
  2. 02 Queue-based workers handle extraction so ingestion never blocks
  3. 03 OCR and layout parsing, followed by model-based field extraction
  4. 04 Deterministic validation: arithmetic, formats, and master-data matching
  5. 05 Confidence thresholds route each document to auto-post or human review
  6. 06 Integration service writes approved records to the finance system, with idempotency keys
  7. 07 Corrections from the review queue are stored as labelled evaluation data

  Technology categories

  • Python
  • OCR
  • Queue workers
  • PostgreSQL
  • Object storage
  • React
• Example solution

  Predictive Operations Dashboard

  • AI
  • Data
  • Business Systems

  Business problem

  Operations planning is done in a spreadsheet rebuilt each week from several exports. Demand is estimated from last month repeated forward, so stock and staffing are consistently wrong in both directions, and nobody can see a problem forming until it arrives.

  Proposed solution

  A consolidated operations view fed by a scheduled data pipeline, with forecasting where the history supports it. Every prediction is shown alongside its confidence range and the actual outcome once known, so the team can see where the model is reliable and where it is not. Planners can override any figure, and overrides are recorded.

  Main capabilities

  • Scheduled pipeline consolidating sales, inventory, and operational data
  • Demand and capacity forecasts with confidence ranges
  • Anomaly detection on daily operational metrics
  • Forecast versus actual tracking, visible to the people who plan
  • Planner overrides with a recorded reason
  • Threshold-based alerts routed to the responsible team

  Expected business value

  • Replaces a weekly manual rebuild with a maintained, shared view
  • Makes forecast reliability visible instead of assumed
  • Gives earlier warning of stock and capacity problems
  • Keeps a record of planning decisions and the reasons behind them

  Example architecture

  1. 01 Scheduled extraction from source systems into a reporting database
  2. 02 Transformation and quality checks, with failures raised rather than hidden
  3. 03 Model training pipeline with versioned datasets and stored metrics
  4. 04 Forecast service writing predictions and confidence ranges back to the database
  5. 05 Drift monitoring comparing recent accuracy against the training baseline
  6. 06 Dashboard interface with filters, override controls, and export

  Technology categories

  • Python
  • scikit-learn
  • PostgreSQL
  • Scheduled jobs
  • React
  • TypeScript
• Example solution

  AI-Enabled Customer Support Portal

  • AI
  • Automation
  • SaaS

  Business problem

  Support requests arrive by email and through a web form, are triaged by hand, and are often reassigned two or three times before reaching the right person. Customers cannot see progress, so they follow up by email, which adds further load to the same team.

  Proposed solution

  A multi-tenant support portal with automated triage and drafted first replies. An incoming request is classified by topic, urgency, and product area, then routed accordingly. A draft reply is prepared from documentation and previous resolved cases, and an agent edits and sends it — nothing reaches a customer without review. Customers see the status of their own requests without emailing anyone.

  Main capabilities

  • Customer portal with per-account access and request history
  • Automatic classification by topic, urgency, and product area
  • Routing rules with escalation when a request stalls
  • Drafted replies grounded in documentation and resolved cases, always agent-reviewed
  • Duplicate and related-case detection
  • Agent workspace with the source passages used for each draft
  • Reporting on volume, response time, and category

  Expected business value

  • Cuts the manual triage step before a request reaches the right person
  • Reduces follow-up emails by making status visible to the customer
  • Keeps a human accountable for every message that leaves the company
  • Turns resolved cases into a reusable source for future answers

  Example architecture

  1. 01 Multi-tenant application with strict data isolation per customer account
  2. 02 Intake service normalising email, form, and API submissions into one request model
  3. 03 Classification service assigning topic, urgency, and area, with a confidence score
  4. 04 Retrieval over documentation and resolved cases to ground the drafted reply
  5. 05 Draft queue in the agent workspace; sending always requires a person
  6. 06 Rules engine for routing, escalation, and service-level tracking
  7. 07 Reporting layer over the request database

  Technology categories

  • Node.js
  • TypeScript
  • React
  • PostgreSQL
  • Vector search
  • Cloud hosting

No example solutions match that category yet.