The team at Daphnis Labs redefined what’s possible for Urbanface. They delivered a bespoke, animation-heavy website that remains incredibly quick and functional. The…
AI developmentfrom business problemto working product.
Start with a use case, test its feasibility and build the software around it.
Business task
Company data
Existing software
Product feature
Team workspace
API endpoint
AI System
What Can We Build?
Discuss what you want to buildDocument Extraction
Support Triage
Product Discovery
Image Review
Content Workspaces
Operations Summaries
What You Actually Get
- Feasibility Report
- Application Source
- Interface Designs
- Integration Contracts
- Evaluation Dataset
- Deployment Package
- Usage Dashboard
- Handover Guide
- Feasibility Report
- Application Source
- Interface Designs
- Integration Contracts
- Evaluation Dataset
- Deployment Package
- Usage Dashboard
- Handover Guide
Selected Work
PourBud
A complete consumer platform connecting city-wise drink data with AI, regional stories and community.
View project
PA: Product & Collection Sort
Sales signals, stock levels and merchant rules help decide which products shoppers encounter first.
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Z08 Heatmap & Recording
A Shopify app combining heatmaps, session recording and AI-assisted behavioural insight.
View projectExample: Compare Supplier Documents.
Step 1 / 6
PDF Pack
Two specification sheets supplied
- PDF Pack
- Fields
- Units
- Comparison
- Review
- Brief
Worked example · Step 0 of 6
What Does It Take to Build?
Get a custom estimatePilot
- One use case
- Representative sample data
- Feasibility prototype
- Recommended
Production
- One application
- Live data connections
- User rollout
Enterprise
- Multiple products or teams
- Shared platform requirements
- Ongoing development
- Founded in
- 2013
- Projects delivered
- 550+
- Client countries
- 43+
- Global offices
- 3
FAQs
What if we only have an idea?
We start with the task, intended users and available data. A focused feasibility exercise identifies what can be tested before committing to a full application.
Do we need to train a model?
Not always. An existing model, retrieval or conventional software may cover the task. Custom training is considered when the available data and evaluation results justify it.
Can you extend our existing product?
Yes. We can add a feature or API to an existing application. The integration approach depends on its codebase, interfaces and release process.
What determines the first milestone?
A narrow task with representative inputs and clear acceptance criteria. Data access, integration dependencies and reviewer availability determine what can be demonstrated first.
Who owns the delivered code?
Code ownership, licensing and third-party dependencies are agreed in the project contract. We identify these terms before implementation.
How do you handle uncertain results?
The product should distinguish a usable result from an incomplete or low-confidence one. Depending on the task, it can ask for more information, show sources or route the item to a person.
Which problem should we test first?
Bring a task, a few sample inputs and the result you want to achieve.
