Solutions
Solution examples for problems standard software can't solve.
These examples show the kinds of systems we are equipped to design and build: the problem, how the system works and where AI fits — or doesn't.
Client project · anonymized
Advanced Multi-User Web Application
Alongside the illustrative examples below, this anonymized client project shows how we engineer a multi-user web platform end to end.
Read the case study- Type
- Multi-user web application and staff console
- Starting point
- An existing native mobile product
- Our scope
- Specification, architecture, front end, API, admin console, deployment
- Status
- Release candidate, in pilot (October 2026)
About these examples. The solution examples below are illustrative architectures. Unlike the client project above, they are not case studies, and they do not describe any specific customer's system, data or business rules.
01Manufacturing & engineering
AI-assisted manufacturing engineering
The challenge
Manufacturers of custom parts receive technical drawings and must turn each one into a production plan and a price. The work depends on experienced engineers reading drawings, selecting processes and calculating costs — a slow step that limits how many quotes and orders a company can handle.
Role of AI
AI handles interpretation and extraction. Deterministic manufacturing logic and engineer approval govern every result that reaches production.
What the system does
- Interprets engineering drawings: geometry, dimensions, tolerances, materials and notes
- Extracts structured part data and validates it against engineering rules
- Applies manufacturing logic to propose operations, machines and sequences
- Presents results to engineers for review, correction and approval
- Generates routings and work orders from approved data
- Calculates production cost estimates for quoting and planning
- Integrates with existing ERP and production systems
Extracted part data
- Material
- AL 6061-T6
- 0.99
- Thickness
- 8 mm
- 0.98
- Holes
- 4 × Ø12 H7
- 0.97
- General tolerance
- ±0.05
- 0.71Needs review
- Operations
- CUT · DRILL · DEBURR
- 0.93
Verified
How it fits together
- 01Drawing
- 02Extraction
- 03Manufacturing rules
- 04Engineer review
- 05Routing & work order
- 06Costing
- 07ERP
Related expertise
02Intelligent document processing
Intelligent document processing
The challenge
Organizations that receive large volumes of invoices, certificates, specifications or forms spend significant time re-keying and checking data by hand — and every error travels into downstream systems.
Role of AI
AI performs classification and extraction. Validation rules and reviewers ensure that only verified data reaches systems of record.
What the system does
- Classifies incoming documents and routes them to the right process
- Extracts fields and tables, including from scanned documents
- Validates extracted data against business rules and existing records
- Sends exceptions and low-confidence fields to a review queue
- Writes approved data to ERP, accounting or document systems
- Keeps a link from every value back to its source
How it fits together
- 01Intake
- 02Classification
- 03Extraction
- 04Validation
- 05Review
- 06Export
Related expertise
03Operational platforms
Custom operational platforms
The challenge
Companies coordinating production, logistics, field service or projects across teams often run on a patchwork of spreadsheets, messaging apps and disconnected tools. Nobody has a reliable, current picture of the work.
Role of AI
Often none at first. Once reliable data exists, AI can support forecasting, prioritization or anomaly detection.
What the system does
- A single source of truth for jobs, resources and status
- Planning and scheduling that respect real constraints
- Mobile access for teams in the field or on the shop floor
- Role-based views for managers and operators
- Notifications and escalation when work is at risk
- Integration with existing ERP, accounting and communication tools
How it fits together
- 01Plan
- 02Assign
- 03Execute
- 04Track
- 05Report
Related expertise
04Complex business applications
Complex business applications
The challenge
Quoting with hundreds of pricing rules, product configuration with technical constraints, multi-step approvals across departments — processes whose logic is too specific for configurable standard software.
Role of AI
Optional. AI can help interpret input or suggest options, while the core business logic stays deterministic and testable.
What the system does
- Rules engines that encode pricing, configuration or eligibility logic
- Guided interfaces that prevent invalid combinations
- Multi-step approval workflows with a complete history
- Generation of quotes, contracts and specifications
- Reporting on volumes, margins and bottlenecks
- APIs for integration with CRM, ERP and customer portals
How it fits together
- 01Input
- 02Rules engine
- 03Approval
- 04Documents
- 05Integration
Related expertise
05AI-enhanced systems
AI capabilities in existing software
The challenge
Many companies already run software that works but could do more: search that understands technical content, automatic classification or assistance with complex decisions. The challenge is adding AI without destabilizing what already runs.
Role of AI
AI is the core addition — built as a separate, well-bounded service with its own evaluation, so the existing system stays stable.
What the system does
- Semantic search across internal documents and technical knowledge
- Automatic classification and tagging of records
- Recommendations embedded in existing screens and workflows
- Summaries of long technical or operational records
- Evaluation and monitoring of AI quality over time
- Controls for cost, access and data boundaries
How it fits together
- 01Existing system
- 02AI service
- 03Validation
- 04User
Related expertise
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