Sales & lead generation.
An AI agent qualifies leads, follows up, and hands off warm conversations to your team.
03. ApplicationsBy problem
What AI can actually do for your business.
Concrete applications, organised by problem, not by buzzword.
ST/03.1 · By function
By function
Nine places where an LLM integration pays for itself inside a normal week of work.
Content & copywriting.
On-brand copy, drafts, and campaign content generated at the pace a marketing calendar actually needs.
Scrum & project automation.
Standups, backlog grooming, and sprint reporting automated, so teams spend less time updating tickets and more time shipping.
Accounting & operations.
Invoices organised, records reconciled, and the repetitive parts of back-office work automated.
Code generation.
AI-assisted code generation integrated directly into an existing development workflow.
Design by prompting.
A written brief turned into a first-draft design in minutes, not days.
Document & image analysis.
Fast, automated analysis of documents and images at volume, without manual review bottlenecks.
Video transcription.
Recorded meetings, calls, and training videos turned into searchable, structured text.
Digital sales persona.
A branded AI persona representing a business consistently across every channel.
ST/03.2 · By industry
By industry
Multilingual document intelligence, applied across sectors.
- ManufacturingRecurring equipment issues diagnosed by connecting technician logs, service reports, and shift notes across languages.
- Travel & hospitalityGuides, booking information, hotel policies, and itineraries translated instantly.
- FinanceFinancial reports, investment documents, and banking materials translated with accurate financial terminology.
- EducationEducational materials, research papers, and academic content translated while preserving academic integrity.
- TechnologyTechnical documentation and software interfaces translated while maintaining technical accuracy.
- LegalContracts and compliance materials translated precisely, with legal terminology intact.
ST/03.3 · Engineering
AI in engineering and manufacturing
Not every problem needs AI, and pretending otherwise is how projects fail.
The right approach: where a deterministic, programmable solution exists and produces exact results, that solution wins. AI earns its place only where an exact programmatic solution does not exist or is cost-prohibitive to build. In engineering and manufacturing especially, a probabilistic error is expensive: a single wrong value in a production record can block an entire manufacturing run.
Applied well, this looks like three tiers of capability rather than AI layered on everything at once.
Tier 01
Systems integration & data automation.
Design systems, planning systems, and document platforms connected into a single, deterministic pipeline: structured data moved automatically and accurately between systems, with a full audit trail and clean rollback if any step fails. AI is introduced only at the edges where it adds genuine value, such as document intelligence on incoming change requests, kept fully isolated from the deterministic core so a model's uncertainty never touches production data.
Tier 02
Intelligent design & search.
Visual and geometric search across historical design records, so a rough sketch or partial spec instantly surfaces the closest existing designs instead of starting from scratch. Automated classification and metadata tagging, combining language and vision models to clean up and organise design data at scale. Automated identification of lower-cost, off-the-shelf substitutes for custom-designed components.
Tier 03
Predictive manufacturing intelligence.
Real-time cost estimation as a product is being designed, based on geometry, material, and current production rates. Predictive flags for manufacturability issues before a design ever reaches the production floor. Fast surrogate models that give instant early-stage estimates ahead of expensive full simulations. The same class of model extends into computer-vision-driven cost and quality assessment: analysing design or product images directly to calculate pricing factors or flag defects.
ST/03.4 · Research
Emerging capabilities
What the lab is building toward next.
Synthetic personality & memory systems.
Long-term, persistent memory for AI agents, so an assistant retains context across sessions instead of starting from zero every time.
MCP indexing & search.
Discovery and search infrastructure for the growing ecosystem of MCP servers, making it easier to find and connect the right tools to an agent.
Don't see your exact use case?
That usually means it has not been built yet. Tell us what you need.