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…
LLM applicationsbuilt around realbusiness workflows.
Give customers and staff an AI interface inside the software they already use.
Your LLM Application: a user request arrives with your knowledge and business data as context, the application calls the right model and your tools, the result is validated against sources, format and rules, and the validated answer is delivered back to the user.
User / Interface
Knowledge
Business Data
LLM
Tools / APIs
Validation
Your LLM Application
What Can We Build?
Explore AI servicesEmployee Q&A
Writing Copilots
Customer AI Features
Document Extraction
Knowledge Search
Recommendations
What You Actually Get
- Application Interface
- Prompt Library
- Knowledge Index
- API Adapters
- Access Rules
- Evaluation Suite
- Deployment Package
- Admin Console
- Application Interface
- Prompt Library
- Knowledge Index
- API Adapters
- Access Rules
- Evaluation Suite
- Deployment Package
- Admin Console
LLM Deployment Work
We deployed LLMs for Easy Chair and Sinzo. The Sinzo case study below shows the commerce product.
Example: Turn an Invoice into Records.
Step 1 / 6
Invoice
Uploaded PDF
- Invoice
- Fields
- Draft Record
- Checks
- Correction
- Export
Worked example · Step 0 of 6
What Does It Take to Build?
Get a custom estimatePilot
- One use case
- One model
- Limited data sources
- Most popular
Production
- Customer or staff application
- Multiple integrations
- Production rollout
Multi-Team
- Multiple workflows
- Multiple models
- Shared administration
- Ongoing engineering
- Founded in
- 2013
- Projects delivered
- 550+
- Client countries
- 43+
- Global offices
- 3
FAQs
How does LLM app development differ from model development?
Model development focuses on training or adapting the model itself. LLM app development builds a product around a model. This service covers applications and deployment using existing models.
What is included in the build?
The product interface, prompt and model orchestration, a knowledge or retrieval layer where the use case needs one, integrations with your systems, permissions, evaluation and deployment. Scope is agreed per use case before build starts.
Can you work with our existing systems?
Yes. Existing APIs, databases and internal applications are connected through explicit schemas, identity scope and validated read and write boundaries rather than ad-hoc calls.
Which AI models can you use?
Model choice follows the problem. OpenAI, Claude, Gemini and other providers can be used, and the application is built so a model can be changed without rewriting the product around it.
Can you use our private company data?
Yes. Private content can be connected through a retrieval layer with tenant scope, permission-aware access and agreed data-handling boundaries, so users only receive context they are allowed to access.
How do you measure answer quality?
Through repeatable evaluation scenarios, structured-output checks, grounding and citation checks, failure-mode review and production monitoring - tracked over time rather than judged from a single demo.
What affects project cost and timeline?
Use-case clarity, data readiness and access, integration count, permission and compliance requirements, evaluation depth and the scope of the interface shape delivery.
What should your product help people do?
Bring a user journey or an early prototype. We’ll define the first release.
